![\"Stockholm](\"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg\")
The PHP library that renders article citations in Open Journal Systems (OJS) will change maintenance hands from the library\u2019s creator, Sebastian B\u00f6ttger, to PKP.
\nFor those of you unfamiliar, citeproc-php is the server-side library that takes raw article metadata (author names, titles, publication dates, DOIs) and transforms it into formatted citations using Citation Style Language (CSL) styles including APA, MLA, Chicago, Vancouver, and more.
\nIn OJS, this library works behind the scenes of the Citation Style Language plugin. When a reader clicks \u201cHow to Cite\u201d on an article page, or when a journal manager selects a citation style under Settings, citeproc-php generates the formatted output.
\nFor OJS users: Nothing immediate. Your citation displays will continue working exactly as they do now. The plugin settings in your dashboard remain unchanged.
\nFor developers: The citeproc-php library has moved to the PKP organization under Github and PKP will take an active role in its steering and release management. Little else will change \u2013 PKP would like to continue working with the active contributor community already supporting the library.
\nFor the broader audience: PKP has long benefited from, and frequently contributed to, 3rd-party Free and Open Source (FOSS) libraries like citeproc-php. As good FOSS citizens, PKP recognizes the value of a strong ecosystem of support and contribution; this move to formally maintaining citeproc-php is done in recognition of the need for material support for the tools we all rely upon.
\nThe handover to PKP makes strategic sense since citeproc-php\u2019s primary consumer is OJS and therefore PKP has a strong interest in keeping the library secure and updated.
\nThe PKP team would like to thank Sebiastian B\u00f6ttger for his excellent work supporting the scholarly publishing community for many years!
\nThe handover discussion can be found in GitHub #200. The new pkp/citeproc-php GitHub page can be found here.\u00a0
The post citeproc-php is under new management! appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/r58bv-8dm69","funding_references":null,"guid":"https://pkp.sfu.ca/?p=18764","id":"d8fec180-e406-436f-9a9f-00613a903c03","image":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg","images":[{"alt":"Stockholm Archipelago photo by PKP's Jason Nugent represents the distinct yet deeply interconnected nature of landscapes, ecosystems, and infrastructures, a theme of PKP's Community Newsletter, Archipelago.","height":"576","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg","srcset":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-300x169.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-768x432.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1536x864.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue.jpg","width":"1024"},{"src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg"}],"indexed":true,"indexed_at":1776374604,"language":"en","parent_doi":null,"published_at":1776373941,"reference":[],"registered_at":0,"relationships":[],"rid":"gg4fn-qbc59","status":"active","summary":"\n \n The PHP library that renders article citations in Open Journal Systems (OJS) will change maintenance hands from the library\u2019s creator, Sebastian B\u00f6ttger, to PKP.\n \n\nWhat does citeproc-php do? For those of you unfamiliar, citeproc-php is the server-side library that takes raw article metadata (author names, titles, publication dates, DOIs) and transforms it into formatted citations using Citation Style Language (CSL)","tags":["Community Newsletter","Citations","Citeproc-php","FOSS","Metadata"],"title":"citeproc-php is under new management!","updated_at":1776373941,"url":"https://pkp.sfu.ca/2026/04/16/citeproc-php-is-now-managed-by-pkp/","version":"v1"}},{"document":{"abstract":"Matt and I were discussing a paper from last year, Korneisel and Maddin (2025) on the evolution of the atlas\u2013axis complex. (It\u2019s excellent, by the way. Really comprehensive.) Mike: Atlases are so weird.","archive_url":null,"authors":[{"affiliation":[{"id":"https://ror.org/0524sp257","name":"University of Bristol"}],"contributor_roles":[],"family":"Taylor","given":"Mike","url":"https://orcid.org/0000-0002-1003-5675"}],"blog":{"archive_collection":22153,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22153/20231105213934/","archive_timestamps":null,"authors":[{"name":"Mike Taylor"}],"canonical_url":null,"category":"earthAndRelatedEnvironmentalSciences","community_id":"0e13541f-417e-46c0-a859-65927249df72","created_at":1675209600,"current_feed_url":null,"description":"SV-POW! ... All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/atom+xml","feed_url":"https://svpow.com/feed/atom/","filter":null,"funding":null,"generator":"WordPress.com","generator_raw":"WordPress.com","home_page_url":"https://svpow.com","id":"c6cbbd2e-4675-4680-8a3f-784388009821","indexed":false,"issn":"3033-3695","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729882329,"relative_url":null,"ror":null,"secure":true,"slug":"svpow","status":"active","subfield":"1911","subfield_validated":true,"title":"Sauropod Vertebra Picture of the Week","updated_at":1776328855.589855,"use_api":true,"use_mastodon":false,"user_id":"04d03585-c8bb-40f2-9619-5076a5e0aed2"},"blog_name":"Sauropod Vertebra Picture of the Week","blog_slug":"svpow","content_html":"Matt and I were discussing a paper from last year, Korneisel and Maddin (2025) on the evolution of the atlas\u2013axis complex. (It’s excellent, by the way. Really comprehensive.)
\nMike: Atlases are so weird. It occurs to me that had the nomenclatural dice fallen differently, we might not even consider them vertebrae at all, just as we don’t consider metacarpals to be manual phalanges. We might have considered the axial skeleton to consist of skull, atlas, and a sequence of vertebrae starting with the axis. (And, yes, ribs, chevrons and sternal plates.)
\nMatt: Good point on […] the weirdness of the atlas. The atlas in particular really does feel like an embryonic segmentation error codified and exapted into a useful structure.
\nMike: Your mom is an embryonic segmentation error codified and exapted into a useful structure.
\n\n
Dise\u00f1ar la bibliograf\u00eda para los cursos de Biolog\u00eda
Generar la lista de la bibliograf\u00eda, tanto b\u00e1sica como complementar\u00eda, constituye un proceso fundamental en el dise\u00f1o curricular porque conforma el n\u00facleo te\u00f3rico, metodol\u00f3gico y conceptual de la clase, establece el enfoque, las dimensiones, el contexto, el alcance y define claramente el marco conceptual que guiar\u00e1 el curso.
Dada su relevancia el proceso de seleccionar y crear la bibliograf\u00eda que se usar\u00e1 debe ser meticuloso, detallado y formal porque ser\u00e1 el sustento que utilizar\u00e1n los alumnos para obtener definiciones, establecer marcos de referencia, localizar bibliograf\u00eda relacionada y\u00a0
Este elemento es com\u00fanmente relegado en el dise\u00f1o curricular, esto es evidente porque existe muy poca literatura\u00a0 que aborde este tema,\u00a0 no se le da la importancia suficiente, al parecer, en muchos casos se ve como un requisito, no c\u00f3mo un componente fundamental del programa de una clase.
En este texto escribe algunas ideas que se deben considerar para generar la lista de la bibliograf\u00eda de un curso de ciencia, o de cualquier otro tema aunque yo me centrar\u00e9 en uno de biolog\u00eda.
El dise\u00f1o
La primera etapa es el dise\u00f1o de la bibliograf\u00eda del plan.
![](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBLXLPBBXRdEIaaz8KoCouXlmhZFLa8E_wB8YXlqoJTQQo3QY6rpCeUTwOqrynCxwSh_dcLqK4K4FvcIW-OvkZRbK3eM2p7oRB9MFjzE7USmJoMFraA2FFCiKiDEqQwYqjT1ZUXtSqIeUKf8MMK2hKxugUfgHIzUJZpR6_CyLRY08oslLNIByIUGmcWiw/s2248/biologia%202024.png\")
I wanted to know:
\n\n\n\nAfter a little bit of searching, I couldn’t find any answers. So I decided to use R to retrieve the necessary info from Uniprot and calculate it myself. I thought I’d post it here in case it’s useful for others.
\n\n\n\n![\"\"](\"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1024x576.png\")
We’ll start with the info I wanted. According to Uniprot there are 20,659 proteins in the human proteome. One quarter of these have one or more TMD. The majority have one TMD and there are almost 1,000 7TM proteins (all those GPCRs, I guess). There’s 413 4TM and 327 2TM proteins. We can find examples of 1 through 17 TMDs, there’s no proteins with 18, 4 proteins with 19TM, 21 with 24TM and 2 with 38TM.
\n\n\n\nThe analysis is done simply by looking at how many TRANSMEM Uniprot has for each IDs in the reference proteome. I have not distinguished between helical and partial entries, and of course it’s possible that the annotations are not quite correct.
| TMDs | Count | Frequency (proteome as %) | Frequency (TMDPs as %) |
| 1 | 2402 | 11.6 | 45.8 |
| 2 | 327 | 1.6 | 6.2 |
| 3 | 159 | 0.8 | 3.0 |
| 4 | 413 | 2.0 | 7.9 |
| 5 | 77 | 0.4 | 1.5 |
| 6 | 276 | 1.3 | 5.3 |
| 7 | 947 | 4.6 | 18.0 |
| 8 | 83 | 0.4 | 1.6 |
| 9 | 63 | 0.3 | 1.2 |
| 10 | 123 | 0.6 | 2.3 |
| 11 | 75 | 0.4 | 1.4 |
| 12 | 202 | 1.0 | 3.8 |
| 13 | 24 | 0.1 | 0.5 |
| 14 | 26 | 0.1 | 0.5 |
| 15 | 13 | 0.1 | 0.2 |
| 16 | 1 | 0.0 | 0.0 |
| 17 | 9 | 0.0 | 0.2 |
| 19 | 4 | 0.0 | 0.1 |
| 24 | 21 | 0.1 | 0.4 |
| 38 | 2 | 0.0 | 0.0 |
Having written this code, I decided to run some other proteomes to see how they compare.
\n\n\n\n![\"\"](\"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png\")
![\"\"](\"https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1024x576.png\")
![\"\"](\"https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1024x576.png\")
![\"\"](\"https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1024x576.png\")
These four organisms have between 18% and 29% of the proteome made of proteins with TMDs. The pattern of numbers of TMDs are kind of similar although there’s no peak in yeast for 7TM and the peaks for 2, 4, 6 or 12 TMs differ from human.
\n\n\n\nMaybe this information is out there in some database or other. As I said, I couldn’t find something easily. Even if there are more precise ways of determining the TMDs, I think this data is good enough to know roughly what the proportions are.
\n\n\n\nI manually downloaded the fasta.gz files for the reference proteomes. They are currently linked here.
\n\n\n\nTo extract all the Uniprot IDs, I used a shell one-liner:
\n\n\n\nawk '/^>sp\\|.*\\|/{gsub(/^>sp\\|/,""); gsub(/\\|.*/,""); print ">" $0; next} {print}' file.fasta | grep "^>" | sed 's/>//g' > species_uniprot.txt\nThen I used this R script. The main function can probably be simplified. I had to add several checks to make sure I got all the data back from the API. Before posting, I tried to cut it back to make it easier to read, but Ionly succeeded in breaking the script! This is the working version.
\n\n\n\n# if (!require("BiocManager", quietly = TRUE)) {\n# install.packages("BiocManager") \n# }\n# BiocManager::install("biomaRt")\nlibrary(httr)\nlibrary(stringr)\nlibrary(ggplot2)\nlibrary(biomaRt)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(cowplot)\n\n## FUNCTIONS ----\n\nisJobReady <- function(jobId, pollingInterval = 5, maxWaitSeconds = 3600) {\n if (is.null(jobId) || length(jobId) == 0 || is.na(jobId) || !nzchar(jobId)) {\n return(FALSE)\n }\n nTries <- ceiling(maxWaitSeconds / pollingInterval)\n for (i in 1:nTries) {\n url <- paste("https://rest.uniprot.org/idmapping/status/", jobId, sep = "")\n r <- GET(url = url, accept_json())\n status <- content(r, as = "parsed")\n if (!is.null(status[["results"]]) || !is.null(status[["failedIds"]])) {\n return(TRUE)\n }\n if (!is.null(status[["messages"]])) {\n print(status[["messages"]])\n return(FALSE)\n }\n Sys.sleep(pollingInterval)\n }\n return(FALSE)\n}\n\nretrieveUniprotInfo <- function(x,\n chunk_size = 5000,\n maxWaitSeconds = 3600,\n taxId = "9606",\n progress = TRUE) {\n normalize_uniprot_ids <- function(values) {\n values <- trimws(values)\n # Accept FASTA-style headers like: sp|P12345|... or tr|A0A...|...\n m <- str_match(values, regex("^>?\\\\s*(?:sp|tr)\\\\|([^|]+)\\\\|", ignore_case = TRUE))\n values <- ifelse(!is.na(m[, 2]), m[, 2], values)\n values\n }\n \n ids <- unique(normalize_uniprot_ids(x))\n ids <- ids[!is.na(ids) & nzchar(ids)]\n if (length(ids) == 0) {\n stop("No valid identifiers were provided to retrieveUniprotInfo().")\n }\n \n fields <- "accession,id,protein_name,gene_names,ft_transmem,length,cc_function,cc_subcellular_location,go_p,go_c"\n acc_pattern <- "^[OPQ][0-9][A-Z0-9]{3}[0-9](-[0-9]+)?$|^[A-NR-Z][0-9](?:[A-Z][A-Z0-9]{2}[0-9]){1,2}(-[0-9]+)?$"\n is_accession <- str_detect(ids, acc_pattern)\n \n split_into_chunks <- function(values, chunk_size = chunk_size) {\n split(values, ceiling(seq_along(values) / chunk_size))\n }\n \n get_next_link <- function(link_header) {\n if (is.null(link_header)) {\n return(NULL)\n }\n links <- unlist(strsplit(link_header, ",\\\\s*"))\n next_link <- links[str_detect(links, "rel=\\\\\\"next\\\\\\"")]\n if (length(next_link) == 0) {\n return(NULL)\n }\n next_url <- str_extract(next_link[1], "(?<=<).+?(?=>)")\n if (is.na(next_url) || !nzchar(next_url)) {\n return(NULL)\n }\n next_url\n }\n \n read_tsv_response <- function(resp) {\n read.table(\n text = content(resp, as = "text", encoding = "UTF-8"),\n sep = "\\t",\n header = TRUE,\n fill = TRUE,\n quote = "",\n comment.char = "",\n check.names = FALSE\n )\n }\n \n fetch_from_redirect <- function(redirect_url) {\n if (is.null(redirect_url) || length(redirect_url) == 0 ||\n is.na(redirect_url) || !nzchar(redirect_url)) {\n return(NULL)\n }\n \n # The paged idmapping results endpoint is capped at size <= 500.\n # Use stream endpoint to retrieve the full chunk in one response.\n stream_url <- gsub("/results/", "/results/stream/", redirect_url)\n sep <- ifelse(str_detect(stream_url, "\\\\?"), "&", "?")\n stream_url <- paste0(\n stream_url,\n sep,\n "fields=", URLencode(fields, reserved = TRUE),\n "&format=tsv"\n )\n \n r <- GET(url = stream_url)\n if (status_code(r) < 400) {\n return(read_tsv_response(r))\n }\n \n # Fallback to paged endpoint if stream is unavailable.\n sep <- ifelse(str_detect(redirect_url, "\\\\?"), "&", "?")\n url <- paste0(\n redirect_url,\n sep,\n "fields=", URLencode(fields, reserved = TRUE),\n "&format=tsv&size=500"\n )\n \n r <- GET(url = url)\n stop_for_status(r)\n resultsTable <- read_tsv_response(r)\n \n next_url <- get_next_link(headers(r)[["link"]])\n while (!is.null(next_url) && !is.na(next_url) && nzchar(next_url)) {\n r <- GET(url = next_url)\n stop_for_status(r)\n resultsTable <- rbind(resultsTable, read_tsv_response(r))\n next_url <- get_next_link(headers(r)[["link"]])\n }\n \n resultsTable\n }\n \n map_ids <- function(values, from_db, to_db, chunk_size, taxId = NULL,\n label = "ids") {\n if (length(values) == 0) {\n return(NULL)\n }\n \n results_list <- list()\n chunks <- split_into_chunks(values, chunk_size = chunk_size)\n n_chunks <- length(chunks)\n for (i in seq_along(chunks)) {\n chunk <- chunks[[i]]\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d (%d ids)\\n",\n label, i, n_chunks, length(chunk)))\n }\n \n files <- list(\n ids = paste0(chunk, collapse = ","),\n from = from_db,\n to = to_db\n )\n if (!is.null(taxId)) {\n files$taxId <- taxId\n }\n \n r <- POST(url = "https://rest.uniprot.org/idmapping/run", body = files,\n encode = "multipart", accept_json())\n stop_for_status(r)\n submission <- content(r, as = "parsed", encoding = "UTF-8")\n \n job_id <- submission[["jobId"]]\n if (is.null(job_id) || length(job_id) == 0 || is.na(job_id) || !nzchar(job_id)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: no jobId returned\\n",\n label, i, n_chunks))\n }\n next\n }\n if (!isJobReady(job_id, maxWaitSeconds = maxWaitSeconds)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: timeout/not ready\\n",\n label, i, n_chunks))\n }\n next\n }\n \n details_url <- paste("https://rest.uniprot.org/idmapping/details/",\n job_id, sep = "")\n r <- GET(url = details_url, accept_json())\n stop_for_status(r)\n details <- content(r, as = "parsed", encoding = "UTF-8")\n \n redirect_url <- details[["redirectURL"]]\n if (is.null(redirect_url) || length(redirect_url) == 0 ||\n is.na(redirect_url) || !nzchar(redirect_url)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: missing redirectURL\\n",\n label, i, n_chunks))\n }\n next\n }\n chunk_result <- fetch_from_redirect(redirect_url)\n if (is.null(chunk_result)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: invalid redirectURL\\n",\n label, i, n_chunks))\n }\n next\n }\n \n results_list[[length(results_list) + 1]] <- chunk_result\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: completed\\n",\n label, i, n_chunks))\n }\n }\n \n if (length(results_list) == 0) {\n return(NULL)\n }\n do.call(rbind, results_list)\n }\n \n accession_ids <- ids[is_accession]\n gene_like_ids <- ids[!is_accession]\n \n acc_results <- map_ids(\n values = accession_ids,\n from_db = "UniProtKB_AC-ID",\n to_db = "UniProtKB",\n chunk_size = chunk_size,\n label = "accessions"\n )\n \n gene_results <- map_ids(\n values = gene_like_ids,\n from_db = "Gene_Name",\n to_db = "UniProtKB-Swiss-Prot",\n chunk_size = chunk_size,\n taxId = taxId,\n label = "gene_names"\n )\n \n results_list <- list()\n if (!is.null(acc_results)) {\n results_list[[length(results_list) + 1]] <- acc_results\n }\n if (!is.null(gene_results)) {\n results_list[[length(results_list) + 1]] <- gene_results\n }\n \n if (length(results_list) == 0) {\n stop("No UniProt results were returned. Check identifiers and taxId.")\n }\n \n resultsTable <- do.call(rbind, results_list)\n if ("Entry" %in% colnames(resultsTable)) {\n resultsTable <- resultsTable[!duplicated(resultsTable$Entry), ]\n }\n return(resultsTable)\n}\n\n\n\n## SCRIPT ----\n\nspecies <- c("human", "zebrafish", "drosophila", "worm", "yeast")\nsci_names <- c("human" = "Homo sapiens", "zebrafish" = "Danio rerio", "drosophila" = "Drosophila melanogaster",\n "worm" = "Caenorhabditis elegans", "yeast" = "Saccharomyces cerevisiae")\n\nfor (org in species) {\n # look up scientific name of org\n sci_name <- sci_names[org]\n output_path <- paste0("Output/Data/", org, "_uniprot.csv")\n if(file.exists(output_path)) {\n message(paste("File", output_path, "already exists. Loading", org))\n df <- read.csv(output_path)\n } else {\n message(paste("Retrieving UniProt info for", org))\n species_ids <- read.delim(paste0("Data/", org, "_uniprot.txt"), header = FALSE)\n names(species_ids) <- c("uniprot_id")\n df <- retrieveUniprotInfo(species_ids$uniprot_id)\n # save this result\n write.csv(df, output_path, row.names = FALSE)\n }\n \n df$tms <- str_count(df$Transmembrane, "TRANSMEM")\n tm_counts <- df %>%\n group_by(tms) %>%\n summarise(count = n()) %>% \n filter(tms > 0)\n \n p1 <- ggplot(tm_counts, aes(x = tms, y = count)) +\n geom_col(fill = "#009988") +\n labs(x = "Transmembrane domains", y = "Count",\n title = sci_name) +\n lims(x = c(0.5,NA), y = c(0,NA)) +\n theme_bw(10)\n \n p2 <- SuperPlotR::pieplot(x1 = c(sum(df$tms == 0), sum(df$tms > 0)),\n cols = c("#bbbbbb", "#009988")) +\n # blank background and no legend\n theme_void() +\n theme(legend.position = "none")\n \n # inset p2 in p1 and add information about the percentages\n p <- ggdraw() +\n draw_plot(p1) +\n # top right\n draw_plot(p2, x = 0.9, y = 0.9, hjust = 1, vjust = 1, width = 0.4, height = 0.4) +\n draw_label(paste0("Total Proteins: ", nrow(df),\n "\\nNo TM: ", round(sum(df$tms == 0) / nrow(df) * 100, 1),\n "%\\nWith TM(s): ",\n round(sum(df$tms > 0) / nrow(df) * 100, 1), "%"),\n x = 0.97, y = 0.85, hjust = 1, vjust = 1, size = 8)\n plot_path <- paste0("Output/Plots/", org, "_uniprot_tm_counts.png")\n ggsave(plot_path, p, width = 1600, height = 900, units = "px", dpi = 300)\n}\nNote that I am using {cowplot} at the end to inset the pie chart and to add the text onto the main plot.
—
\n\n\n\nThe post title comes from “My Domain” by Bernard Butler from his People Move On album.
\n","doi":"https://doi.org/10.59350/0ahfj-mxf51","funding_references":null,"guid":"https://quantixed.org/?p=3753","id":"70276eb9-e532-42ea-b09c-8d05e8df0b84","image":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts.png","images":[{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts.png","width":"1024"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1024x576.png"}],"indexed":true,"indexed_at":1776341368,"language":"en","parent_doi":null,"published_at":1776341040,"reference":[],"registered_at":0,"relationships":[],"rid":"k78xz-cmp18","status":"active","summary":"I wanted to know: how many proteins in the human proteome have transmembrane domains? of those that do, how many have 1 or 2 or n transmembrane domains? After a little bit of searching, I couldn\u2019t find any answers. So I decided to use R to retrieve the necessary info from Uniprot and calculate it myself. I thought I\u2019d post it here in case it\u2019s useful for others. Human We\u2019ll start with the info I wanted.","tags":["Science","Bioinformatics","Cell Biology","Proteins","Rstats"],"title":"My Domain: proteome-wide scanning of TMDs","updated_at":1776112087,"url":"https://quantixed.org/2026/04/16/my-domain-proteome-wide-scanning-of-tmds/","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"affiliation":[{"name":"Front Matter"}],"contributor_roles":[],"family":"Fenner","given":"Martin","url":"https://orcid.org/0000-0003-1419-2405"}],"blog":{"archive_collection":22096,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22096/20231101172748/","archive_timestamps":[20231101172748,20240501180447,20241101172601],"authors":[{"name":"Martin Fenner","url":"https://orcid.org/0000-0003-1419-2405"}],"canonical_url":null,"category":"computerAndInformationSciences","community_id":"91dd2c24-5248-4510-9c2b-30b772bf8b60","created_at":1672561153,"current_feed_url":"","description":"The Front Matter Blog covers the intersection of science and technology since 2007.","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/15a362ea-8138-42b8-917f-1840a92addf8/logo","feed_format":"application/atom+xml","feed_url":"https://blog.front-matter.de/atom","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.52","home_page_url":"https://blog.front-matter.de","id":"74659bc5-e36e-4a27-901f-f0c8d5769cb8","indexed":null,"issn":"2749-9952","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://hachyderm.io/@mfenner","prefix":"10.53731","registered_at":1729685319,"relative_url":null,"ror":null,"secure":true,"slug":"front_matter","status":"active","subfield":"1710","subfield_validated":null,"title":"Front Matter","updated_at":1776327653.24951,"use_api":true,"use_mastodon":true,"user_id":"8498eaf6-8c58-4b58-bc15-27eda292b1aa"},"blog_name":"Front Matter","blog_slug":"front_matter","content_html":"All science blogs participating in the Rogue Scholar science blog archive provide a feed (in RSS, Atom or JSON Feed format) that includes not only metadata and full-text content for all archived blog posts, but also metadata about the blog itself. This includes required metadata such as the blog homepage and feed URL, but also an optional description, logo, license, language and science subject area, using the OpenAlex subfield, which maps to the Scopus All Science Journal Classification (ASJC) widely used to classify journals. Rogue Scholar also supports an optional International Serial Number (ISSN) as globally unique identifier for the blog \u2013 this blog has ISSN 2749-9952. While I encourage all Rogue Scholar blogs to obtain an ISSN, there are two important limitations of the ISSN:
An alternative approach is using DOIs as globally unique persistent identifier for the blog, and this aligns with using DOIs to register blog posts. The DOI registration agencies Crossref and DataCite support the resource type Journal and some other periodical publication formats (e.g. Book Series, Proceedings Series), but unfortunately not Blog. And they support the optional inclusion of one or more ISSNs.
Until Crossref and DataCite support the resource type Blog, Rogue Scholar will register blogs as resource type Journal, and include the optional ISSN. Front Matter is a Crossref member and can do this for all blogs participating in Rogue Scholar where it registers the DOIs (almost all of them). The Crossref resource type Journal unfortunately doesn't support many metadata (e.g. no subject classification), and Journal is mainly used as a container to describe a collection of Journal Articles. The DOI uses the same DOI prefix as the corresponding blog posts, and the blog slug (unique identifier used everywhere in Rogue Scholar) as the suffix.
The new blog DOI is displayed on the blog community page (e.g. here for this blog), and clicking on the DOI redirects users to the blog homepage (also shown, as is the ISSN).
![\"\"](\"https://storage.ghost.io/c/c5/33/c533c955-b5f3-4ff1-ae2d-6b52a212e602/content/images/2026/04/Bildschirmfoto-2026-04-16-um-10.57.03.png\")
It will take a few days until the DOIs for all blogs where Rogue Scholar manages DOI registration are registered.
Periodical publications typically have page numbers, issues and volumes. While page numbers and issues are typically not used for online publication formats such as blogs, volumes still make sense in a different context. One particular challenge with science blogs that the Rogue Scholar science blog archive tries to address is long-term archiving. While different approaches can address is problem, the basic idea is Lots of Copies Keep Stuff Safe (LOCKSS). And blog volumes \u2013 all blog posts from a particular blog from a given year \u2013 can simplify long-term blog archiving in multiple places.
This week Rogue Scholar implemented blog volumes for all participating blogs:
![\"\"](\"https://storage.ghost.io/c/c5/33/c533c955-b5f3-4ff1-ae2d-6b52a212e602/content/images/2026/04/Bildschirmfoto-2026-04-16-um-11.16.28.png\")
Clicking on a year shows all blog posts from that blog published in that year:
![\"\"](\"https://storage.ghost.io/c/c5/33/c533c955-b5f3-4ff1-ae2d-6b52a212e602/content/images/2026/04/Bildschirmfoto-2026-04-16-um-11.21.24.png\")
Currently this is an automatically generated search in Rogue Scholar. The next step is generating a downloadable file with all content and metadata of the blog posts published in that year that can be archived in Rogue Scholar and elsewhere. I am working on generating these blog volumes in ePub format. These blog volumes will be registered with DOIs using the Crossref Journal Volume resource type (again, Blog Volume as resource type doesn't exist yet, and DataCite also has no Journal Volume ResourceTypeGeneral, Collection is the closest match).
These blog volumes can then be archived also in other repositories, and the ePub format makes reuse easier compare to PDF, as it is much easier to extract content from ePub. It has not escaped our notice that the specific archiving we are postulating immediately suggests a possible copying mechanism for a blog when its authors want to migrate to a different blogging platform.
Please use Slack, email, Mastodon, or Bluesky if you have any questions or comments, in particular if you are interested in archiving blogs via blog volumes generated by Rogue Scholar.
At rOpenSci, we\u2019re continually grateful for the support and engagement of our community, who help make research open-source stronger, more inclusive, and more collaborative. The software peer review program continues to grow, and today we announce that our editorial team keeps expanding:
We\u2019re excited to welcome Alec Robitaille and Lucy D\u2019Agostino McGowan as new editors. Alec joins our general review team, and Lucy our statistical software review team. Their expertise and dedication will help sustain and strengthen software peer review, ensuring that software reviews continue to meet the highest standards of quality, transparency, and impact.
Meet our new editors!
![\"headshot](\"https://ropensci.org/img/team/alec-robitaille.png\")
Alec is a graduate student at Memorial University of Newfoundland and Labrador (Canada) studying foraging ecology, habitat selection and social networks in caribou and other ungulates. He has been involved in many projects along the way, from measuring muskrat habitat and lake ice dynamics at McGill University to estimating drought sensitivity in Canada\u2019s forests with the Canadian Forestry Service. Passionate about teaching open science and programming, he regularly mentors peers, runs workshops, develops example repositories, and organizes a Bayesian stats colearning group. He maintains the package spatsoc (reviewed by rOpenSci in 2018) and has developed smaller packages with diverse applications including remote sensing (irg), social networks (hwig), camera trap monitoring (camtrapmonitoring), and animal movement (distanceto). He reviewed the ohun, chopin, and emodnet.wfs packages for rOpenSci, guest edited for the rredlist package and is currently handling the reviews for the ActiGlobe, and saperlipopette packages.
I first connected with the rOpenSci community through the review process for the package spatsoc in 2018 as part of our manuscript submission at Methods in Ecology and Evolution. During the review, I was thoroughly impressed by how welcoming the community was, and how effective the process was in helping me learn how to improve the package. rOpenSci is a landmark in the R and open science ecosystems with an ever evolving community to learn from and to be a part of. I am very grateful to be given the opportunity to continue contributing to rOpenSci in this new role as editor.
![\"headshot](\"https://ropensci.org/img/team/lucy-dagonstino-mcgowan.jpg\")
Lucy D\u2019Agostino McGowan is an associate professor in the Department of Statistical Sciences at Wake Forest University. She received her PhD in Biostatistics from Vanderbilt University and completed her postdoctoral training at Johns Hopkins University Bloomberg School of Public Health. Her research focuses on causal inference, statistical communication, analytic design theory, and data science pedagogy. Lucy can be found blogging at livefreeordichotomize.com, on Blue Sky @LucyStats.bsky.social, and podcasting on Casual Inference.
I am so thrilled to join the rOpenSci editorial team! I love the rOpenSci community and mission and am grateful for the opportunity to contribute.
rOpenSci\u2019s software peer review program brings together volunteers to collaboratively review scientific and statistical software according to transparent, constructive, and open standards. Editors manage submissions, coordinate reviewers, and help guide packages through review to improve code quality, documentation, and usability.
This program is possible thanks to the many community members: authors submitting their packages, reviewers volunteering their time and expertise, and editors like Alec and Lucy who help managing reviews and maintaining a supportive process.
Are you considering submitting your package for review? These resources will help:
Would you like to review packages? Fill out the rOpenSci Reviewer Sign-Up Form to volunteer to review.
Welcome again Alec and Lucy! We\u2019re thrilled to have you join the editorial team.
","doi":"https://doi.org/10.59350/v939m-qkj86","funding_references":null,"guid":"https://doi.org/10.59350/v939m-qkj86","id":"20501628-637e-4ec4-8b35-bdf597339d8c","image":null,"images":[{"alt":"headshot of Alec Robitaille","src":"https://ropensci.org/img/team/alec-robitaille.png"},{"alt":"headshot of Lucy D'Agostino McGowan","src":"https://ropensci.org/img/team/lucy-dagonstino-mcgowan.jpg"},{"src":"https://ropensci.org/img/team/alec-robitaille.png"},{"src":"https://ropensci.org/img/team/lucy-dagonstino-mcgowan.jpg"}],"indexed":true,"indexed_at":1776313654,"language":"en","parent_doi":null,"published_at":1776297600,"reference":[],"registered_at":0,"relationships":[],"rid":"w9g9j-0bd39","status":"active","summary":"At rOpenSci, we\u2019re continually grateful for the support and engagement of our community, who help make research open-source stronger, more inclusive, and more collaborative. The software peer review program continues to grow, and today we announce that our editorial team keeps expanding: We\u2019re excited to welcome\n\n Alec Robitaille\n\nand\n\n Lucy D\u2019Agostino McGowan\n\nas new editors.","tags":["Software Peer Review","Editors","ROpenSci Team"],"title":"Expanding the Editorial Team: Alec Robitaille and Lucy D'Agostino McGowan Join as Editors","updated_at":1776311326,"url":"https://ropensci.org/blog/2026/04/16/editors2026/","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Strecker","given":"Dorothea","url":"https://orcid.org/0000-0002-9754-3807"},{"affiliation":[{"id":"https://ror.org/01hcx6992","name":"Humboldt-Universit\u00e4t zu Berlin"}],"contributor_roles":[],"family":"Ochsner","given":"Catharina","url":"https://orcid.org/0009-0005-3885-3951"}],"blog":{"archive_collection":24081,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"computerAndInformationSciences","community_id":"53174590-b8d0-4c88-b121-4ca75f7de145","created_at":1717668020,"current_feed_url":null,"description":"Research Group Information Management @ Humboldt-Universit\u00e4t zu Berlin","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/rss+xml","feed_url":"https://infomgnt.org/index.xml","filter":null,"funding":null,"generator":"Quarto","generator_raw":"Quarto 1.4.555","home_page_url":"https://infomgnt.org","id":"17927ce5-1239-43fb-a3c9-2acb8a679d11","indexed":true,"issn":"2944-6848","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729503399,"relative_url":null,"ror":null,"secure":true,"slug":"infomgnt","status":"active","subfield":"3309","subfield_validated":null,"title":"Research Group Information Management @ Humboldt-Universit\u00e4t zu Berlin","updated_at":1776327809.623468,"use_api":null,"use_mastodon":false,"user_id":"dbffda7d-f391-48fb-a6d2-d4a284c59c8d"},"blog_name":"Research Group Information Management @ Humboldt-Universit\u00e4t zu Berlin","blog_slug":"infomgnt","content_html":"![](\"https://infomgnt.org/posts/2026-04-16-recap-writing-retreat-for-doctoral-students-of-the-philosophical-faculty/doctoral_writing_retreat.jpg\"/)
The Ambassadors for Open Science program of the Berlin University Alliance (BUA) aims to promote the knowledge and application of Open Science in various subjects. As Open Science experts and enthusiasts, the ambassadors show researchers, students and decision-makers at their institution how they can benefit from and make use of Open Science infrastructure (\u201cAuftakt F\u00fcr Neu Ernannte BUA Open Science Ambassadors\u201d (2024)).
\nAs ambassadors for the philosophical faculty Dorothea Strecker and Catharina Ochsner organized a writing retreat in collaboration with Dr.\u00a0Kaitlin Montague for the PhD students of the philosophical faculty of the Humboldt-Universit\u00e4t zu Berlin (HU Berlin). The retreat took place at the workation space coconat and offered 16 doctoral students the opportunity of focused writing time, writing workshops, and Open Science-focused discussions in a new and peaceful environment. The workshop was designed for doctoral students to discuss and develop essential skills of academic writing. During the weekend we addressed Open Access publication strategies for doctoral students as well as a range of writing-related issues, including style and voice, writing discipline, drafting, narrative development and organization, models of accountability, taming the \u201cinternal editor,\u201d and peer review.
\nWe thank the BUA for making this writing retreat possible and we thank all participants for joining us and engaging in many discussions on their writing process, Open Access, and Open Science. In addition to focused writing time and courses on Open Science and Open Access the students were able to socialize with their peers and build a small community that will hopefully continue beyond the retreat.
\nFurther information about the research group can be found on our official website.
\nThis text \u2013 excluding quotes and otherwise labelled parts \u2013 is licensed under the CC BY 4.0 DEED.
\n@online{strecker2026,\n author = {Strecker, Dorothea and Ochsner, Catharina},\n title = {Recap: {Writing} {Retreat} for {Doctoral} {Students} of the\n {Philosophical} {Faculty}},\n date = {2026-04-16},\n url = {https://infomgnt.org/posts/2026-04-16-recap-writing-retreat-for-doctoral-students-of-the-philosophical-faculty},\n langid = {en}\n}\n![\"Global](\"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg\"){kind=tracking}
The 2025 Beacon dataset is out. It shows OJS powering 58,000 journals across 156 countries, with 2.5 million articles published and steady growth in software upgrades. Here\u2019s what the data tells us.
\nOpen Journal Systems (OJS) launched in 2002. By 2010, around 4,000 journals were using it. What followed was more than a decade of steady growth, typically between 5,000 and 7,000 new journals per year. That growth has now levelled off, and we think that deserves to be read for what it is: a sign of maturity, not of stagnation.
\nThe 2025 figures stand at 58,000 journals across 156 countries. That is the footprint of infrastructure.
\nFor librarians and open access advocates who have spent years making the case for community-owned publishing platforms, this scale carries real weight. For tens of thousands of journals, many of them Diamond Open Access charging neither authors nor readers, OJS is how publishing gets done.
\nGeography has always been one of the most revealing dimensions of the Beacon data, and this year is no different.
\nIndonesia continues to lead the world by a wide margin, accounting for more than 40% of all OJS journals globally. That position reflects both a sustained national commitment to open access policy and a publishing culture that has embraced open infrastructure at scale.
\nBrazil retains its place among the top countries, underpinned by a long tradition of publicly funded, openly accessible research communication.
\nAnd in a development worth noting, the United States has recently moved into the top three, as more North American institutions look seriously at open infrastructure as a sustainable path forward.
\nUnderstanding where open publishing is growing, and where it has not yet been adopted, is one of the main reasons PKP collects this data in the first place. It directly shapes decisions about language support, documentation, outreach, and development priorities. The geography of OJS adoption is not just interesting; it is useful.
\nThe Beacon data tracks article output per journal over time. The average number of articles per journal peaked at 62 in 2015, fell to 30 in 2022, and has since recovered to just over 40. Across all OJS journals, total article output now stands at 2.5 million articles.
\nThese figures matter beyond the headline numbers. They represent peer-reviewed research that, in many cases, would not be openly available without community-owned infrastructure. A significant portion of it comes from institutions and regions with no viable route into commercial open access. That research belongs to the communities that produced it, and it is accessible because of the infrastructure those communities chose to use.
\nSince 2024, there has been a 10% increase in journals upgrading to OJS version 3.3, with more than 4,000 journals completing the transition. Software upgrades take real effort: technical capacity, planning time, and often institutional backing. The fact that so many journals have made this move reflects the work PKP has put into making upgrades accessible, through sprint events, hosting support, education, and documentation.
\nThis data connects directly to PKP\u2019s focus on supporting the health and sustainability of journals already using OJS, not just reaching new ones.
\nPKP collects this data to inform its own decisions: where to focus development, where documentation is needed, where the software is being used in ways that point to unmet needs. The dataset is published openly on Harvard Dataverse so that researchers, institutions, and partners can draw their own conclusions from it. The global OJS community is not the subject of this research. It is the reason for it, and its primary beneficiary.
\nThat approach is central to how PKP works. Research informs what we build and how we prioritize. The Beacon project is one of the clearest expressions of that commitment.
\nIf you are a journal manager, a librarian building the case for open infrastructure, or a researcher working on scholarly communications, this data is freely available for you to use.
\nThe Beacon project is a longitudinal research initiative tracking the global adoption and use of Open Journal Systems. Data is collected annually and published openly under Creative Commons licensing.
\nThe post 58,000 Journals and Counting: The 2025 OJS Beacon Data appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/1v51v-e0831","funding_references":null,"guid":"https://pkp.sfu.ca/?p=18753","id":"80dc8e48-77f4-413f-9642-674f294a9334","image":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg","images":[{"alt":"Global OJS usage map. The numbers represented are in the article text.","height":"576","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg","srcset":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-300x169.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-768x432.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1536x864.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue.jpg","width":"1024"},{"alt":"This map of active OJS presence in 2025 is by Saurabh Khanna, published on RPubs. The scale is 0 = lightest blue; 500+ = darkest blue.","src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg"}],"indexed":true,"indexed_at":1776278904,"language":"en","parent_doi":null,"published_at":1776278154,"reference":[],"registered_at":0,"relationships":[],"rid":"a50g6-y8p95","status":"active","summary":"\n \n The 2025 Beacon dataset is out. It shows OJS powering 58,000 journals across 156 countries, with 2.5 million articles published and steady growth in software upgrades. Here\u2019s what the data tells us.\n \n\nOpen Journal Systems (OJS) launched in 2002. By 2010, around 4,000 journals were using it. What followed was more than a decade of steady growth, typically between 5,000 and 7,000 new journals per year.","tags":["Community Newsletter","Beacon","OJS","Open Access","Open Infrastructure"],"title":"58,000 Journals and Counting: The 2025 OJS Beacon Data","updated_at":1776278154,"url":"https://pkp.sfu.ca/2026/04/15/58000-journals-2025-ojs-beacon-data/","version":"v1"}},{"document":{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Moresi","given":"Louis","url":"https://orcid.org/0000-0003-3685-174X"}],"blog":{"archive_collection":22163,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22163/20231105111054/","archive_timestamps":[20231105111054,20240505190806,20241105111040,20250505111057],"authors":null,"canonical_url":null,"category":"earthAndRelatedEnvironmentalSciences","community_id":"54ca6482-0a9b-420d-be44-b0e93fbad952","created_at":1697760000,"current_feed_url":null,"description":"Geodynamics, Computation and Education","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/c8b71a5b-b872-47ab-89f7-7c84741d68fc/logo","feed_format":"application/rss+xml","feed_url":"https://www.underworldcode.org/rss/","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.69","home_page_url":"https://www.underworldcode.org/","id":"9f6ce29f-3545-4482-987d-1a15bc256f4e","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1719281103,"relative_url":null,"ror":null,"secure":true,"slug":"underworldcode","status":"active","subfield":"1908","subfield_validated":null,"title":"Underworld Geodynamics Community","updated_at":1776328962.44395,"use_api":true,"use_mastodon":false,"user_id":"4f890b77-39e3-49c8-81cf-d159b5ad2f81"},"blog_name":"Underworld Geodynamics Community","blog_slug":"underworldcode","content_html":"A symbolic time derivatives: you can inspect it, display it in a notebook, and verify that the time discretisation is doing what you expect. And you can swap between Lagrangian, Semi-Lagrangian, and Eulerian approaches without rewriting the solver. Sympy introduces incredible flexibility for on-the-fly composition of time-dependent problems.
Many geodynamics equations involve a material derivative. Temperature advection-diffusion, viscoelastic stress transport, Navier-Stokes momentum. The material derivative $D\\phi/Dt$ combines the time rate of change with transport by the flow:
$$
\\frac{D\\phi}{Dt} = \\frac{\\partial \\phi}{\\partial t} + \\mathbf{v} \\cdot \\nabla\\phi
$$
Discretising this equation requires making specific choices. How do you handle the advection term? How many previous timesteps do you use? How do you deal with variable timestep sizes? These choices affect accuracy, stability, and computational cost.
In many finite element codes, these choices are baked into the solver implementation. A classic example is Crank-Nicolson time stepping: the flux must be evaluated at both the current and previous timestep, which means adding history terms to the left-hand side of the assembled system as well as introducing a time-derivative term on the right-hand side. The time discretisation reaches into both sides of the equation, and changing it means restructuring the solver. In UW3, the time derivative offers flux and force terms to the solver to handle symbolically as it sees fit.
UW3 provides four implementations of the time derivative, all sharing the same calling interface and working for scalar, vector and tensor quantities.
Eulerian stores history on the mesh. The material derivative is approximated by finite differences in time at fixed grid points, with an advection correction. This is the classical approach: simple, mesh-based, but subject to CFL stability constraints when advection dominates.
Semi-Lagrangian traces characteristics backward in time. At each mesh node, it asks: where was this material parcel at the previous timestep? It then interpolates the previous solution at that departure point. This is unconditionally stable because the material derivative is evaluated along the characteristic, not at a fixed grid point. There is no CFL constraint, though the user needs to be conscious of accuracy trade-offs inherent in the scheme.
Lagrangian follows particles through the flow. The history is stored on swarm variables and advected with the particles. This is the natural choice when material history matters physically, as in viscoelastic stress transport where the stress tensor must be advected and rotated with the material. Accuracy of this scheme depends upon the quality of the particle-layout (density of particles, presence of gaps).
Symbolic provides pure symbolic history without mesh or swarm storage. It is used internally by the constitutive model system for building expressions that involve time derivatives.
All four produce the same thing: a symbolic expression that can be embedded in a solver's weak form. The solver does not need to know which implementation is providing the time derivative. It sees a SymPy expression for $D\\phi/Dt$ and includes those terms when it differentiates the equation system to determine the Jacobians.
The time discretisation uses backward differentiation formulas (BDF). These are implicit multi-step methods that use solution values from previous timesteps to approximate the time derivative at the current step.
At order 1, this is the backward Euler method:
$$
\\frac{D\\phi}{Dt} \\approx \\frac{\\phi^n - \\phi^{n-1}}{\\Delta t}
$$
At order 2, BDF-2 uses two previous values for second-order accuracy:
$$
\\frac{D\\phi}{Dt} \\approx \\frac{\\frac{3}{2}\\phi^n - 2\\phi^{n-1} + \\frac{1}{2}\\phi^{n-2}}{\\Delta t}
$$
The coefficients $[3/2, -2, 1/2]$ are for constant timesteps. When the timestep varies, the coefficients adapt. If the current timestep is $\\Delta t _ n$ and the previous was $\\Delta t _ {n-1}$, with ratio $r = \\Delta t _ n / \\Delta t _ {n-1}$, the BDF-2 coefficients become:
$$
c _ 0 = \\frac{1+2r}{1+r}, \\quad c _ 1 = -(1+r), \\quad c _ 2 = \\frac{r^2}{1+r}
$$
This matters in practice. Underworld simulations usually adjust the timestep as the flow evolves.
The coefficients are computed as exact sympy.Rational values and wrapped in UWexpression objects. This means they are routed through PETSc's constants array, just like viscosity or density parameters. When the timestep changes, the coefficient values update without recompilation. The compiled C code has the same structure at every step, only the values in the constants array will change.
BDF handles the time derivative of the unknown. But transient problems may also need a time-weighted evaluation of the flux (the spatial operator). The Adams-Moulton (AM) family provides this.
At order 0, the flux is evaluated purely at the current time (fully implicit). At order 1 with $\\theta = 1/2$, we have the Crank-Nicolson scheme: the flux is averaged between the current and previous timestep. Higher order variants use more history values.
In UW3, the solver's flux time derivative (DFDt) provides an adams _ moulton _ flux() method that returns the appropriately weighted combination of the current flux and previous flux values as symbolic forms backed by stored evaluations. This expression then appears in the solver's $F _ 1$ template as a symbolic expression. Like the BDF coefficients, the AM weights are UWexpressions that update between timesteps.
The combination of BDF for the time derivative and AM for the flux evaluation gives a family of time integration schemes. BDF-1 with AM-0 is backward Euler. BDF-2 with AM-1 gives second-order accuracy in both the time derivative and the flux evaluation. The user controls this through the order parameter when creating the solver.
A BDF-2 scheme needs two previous solutions. At the start of a simulation, there is only one (the initial condition). UW3 handles this through automatic order ramping.
The DDt object tracks an effective _ order that starts at 1 and increases with each completed solve, up to the requested order. On the first timestep, BDF-1 is used regardless of what order was requested. On the second timestep, BDF-2 becomes available. On the third, BDF-3. The transition is automatic and the coefficients adjust accordingly.
History slots are initialised with the current solution value on the first call. This means the first BDF-1 step sees $\\phi^{n-1} = \\phi^n$, giving a zero time derivative. This is correct for a system starting from rest or from a steady-state initial condition. For impulsive starts, the first-order accuracy of the initial step is usually acceptable because the solution is changing rapidly and the timestep is typically small.
Consider advection-diffusion of temperature:
$$
\\frac{D T}{Dt} = \\nabla \\cdot (k \\nabla T) + H
$$
The solver setup looks like this:
adv_diff = uw.systems.AdvDiffusion(\n mesh, u_Field=T, V_fn=v,\n order=2, # BDF-2 / AM-1\n)\nadv_diff.constitutive_model.Parameters.diffusivity = k\nadv_diff.f = H\nadv_diff.solve(timestep=dt)\nThe order parameter controls the time discretisation. The solver builds its weak form from two template expressions, $F _ 0$ (force-like, paired with the test function) and $F _ 1$ (flux-like, paired with the test function gradient):
F0 = DuDt.bdf() / delta_t - H\nF1 = DFDt.adams_moulton_flux()\nAt order 1 (backward Euler / fully implicit), these expand to:
$$
F _ 0 = \\frac{T^n - T^{n-1}}{\\Delta t} - H
$$
$$
F _ 1 = k \\nabla T^n
$$
The flux is evaluated entirely at the current timestep. The system is first-order accurate in time.
At order 2 (BDF-2 / Crank-Nicolson), the expressions become:
$$
F _ 0 = \\frac{\\frac{3}{2}T^n - 2T^{n-1} + \\frac{1}{2}T^{n-2}}{\\Delta t} - H
$$
$$
F _ 1 = \\frac{1}{2} k \\nabla T^n + \\frac{1}{2} k \\nabla T^{n-1}
$$
The flux is now averaged between the current and previous timesteps. Both the time derivative and the flux evaluation are second-order accurate. The history terms $T^{n-1}$ and $T^{n-2}$ are mesh variable symbols managed by the DDt objects. The coefficients ($3/2$, $-2$, $1/2$, etc.) are UWexpression symbolic constants whose values update each step, if $\\Delta t$ changes, and no recompilation is required.
Note: terms that contain $T^n$ automatically populate the implicit parts of the solver, while history terms are automatically treated explicitly. They are also automatically combined with history terms originating, for example, from the constitutive law.
The solver differentiates through all of this for the Jacobian. The JIT compiler handles it like any other symbolic expression. From PETSc's perspective, the time-stepping machinery is invisible. It sees compiled C functions at quadrature points, with coefficient values arriving through the constants array.
The solve sequence for each timestep is:
The choice of DDt type is a one-parameter decision at solver construction:
# Default for advection-diffusion: Semi-Lagrangian (unconditionally stable)\nadv_diff = uw.systems.AdvDiffusion(mesh, u_Field=T, V_fn=v)\n\n# Override with Lagrangian (particle-based, requires a swarm)\nDTdt = uw.systems.Lagrangian_Swarm_DDt(\n swarm, psi_fn=T.sym,\n vtype=uw.VarType.SCALAR, degree=T.degree,\n continuous=True, order=2\n)\nadv_diff = uw.systems.AdvDiffusion(mesh, u_Field=T, V_fn=v, DuDt=DTdt)\n\n# Or Eulerian (mesh-based, for problems without strong advection)\nDTdt = uw.systems.Eulerian_DDt(\n mesh, T, vtype=uw.VarType.SCALAR,\n degree=T.degree, continuous=True, order=2\n)\nadv_diff = uw.systems.AdvDiffusion(mesh, u_Field=T, V_fn=v, DuDt=DTdt)\nThe solver does not need to be made aware of which DDt type you chose. It calls bdf() and adams _ moulton _ flux() and gets SymPy expressions. The physics of the time discretisation is encapsulated in the DDt object. The numerics of the spatial discretisation are encapsulated in the solver. They communicate through symbolic expressions.
Each solver type has a sensible default. Advection-diffusion and Stokes default to Semi-Lagrangian. Pure diffusion defaults to Eulerian. Viscoelastic solvers use the DFDt infrastructure for stress history on particles. You only need to override the default when your problem requires it.
In UW2, if you wanted to change from explicit particle advection to a semi-Lagrangian scheme, you would need to plug in a different solver. The time discretisation was part of the solver's implementation, not a separable component. When history terms appeared through the constitutive model, we had to develop a matrix of all potential interactions and code them independently.
In UW3, the time derivative is an object you can create, configure, inspect, and swap. The BDF coefficients are visible as symbolic expressions. The history terms are mesh variables you can plot. The AM flux weighting is a symbolic combination you can display in a notebook.
This is the same design principle we described in the constitutive models post: separate the physics from the numerics, connect them through symbolic expressions, and make both sides inspectable. For constitutive models, the boundary is the stress tensor. For time derivatives, it is the BDF/AM expression. In both cases, the solver sees a SymPy expression and does not need to know how it was constructed.
","doi":"https://doi.org/10.59350/2bbxk-1gv97","funding_references":null,"guid":"69dfb91eeb7226563af54429","id":"3bd33871-0c4a-4c00-84ce-34ea4a24bd3b","image":"https://images.unsplash.com/photo-1501139083538-0139583c060f?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDF8fHRpbWV8ZW58MHx8fHwxNzc2MjY5Njk5fDA&ixlib=rb-4.1.0&q=80&w=2000","images":[],"indexed":true,"indexed_at":1776321304,"language":"en","parent_doi":null,"published_at":1776270000,"reference":[],"registered_at":0,"relationships":[],"rid":"yjgtg-ke420","status":"active","summary":"A symbolic time derivatives: you can inspect it, display it in a notebook, and verify that the time discretisation is doing what you expect. And you can swap between Lagrangian, Semi-Lagrangian, and Eulerian approaches without rewriting the solver. Sympy introduces incredible flexibility for on-the-fly composition of time-dependent problems. Many geodynamics equations involve a material derivative.","tags":["Underworld Code","Tricks Of The Trade","Documentation","Development"],"title":"Symbolic Time Derivatives in Underworld3","updated_at":1776270000,"url":"https://www.underworldcode.org/symbolic-time-derivatives-in-underworld3/","version":"v1"}}],"items":[{"abstract":"The PHP library that renders article citations in Open Journal Systems (OJS) will change maintenance hands from the library\u2019s creator, Sebastian B\u00f6ttger, to PKP. What does citeproc-php do? For those of you unfamiliar, citeproc-php is the server-side library that takes raw article metadata (author names, titles, publication dates, DOIs) and transforms it into formatted citations [\u2026] The post citeproc-php is under new management!","archive_url":null,"authors":[{"contributor_roles":[],"family":"Racy","given":"Famira"}],"blog":{"archive_collection":null,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"socialScience","community_id":"77c8c2e4-ebda-4e7c-9458-6c06b604344b","created_at":1752226126.418889,"current_feed_url":null,"description":null,"doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/77c8c2e4-ebda-4e7c-9458-6c06b604344b/logo","feed_format":"application/atom+xml","feed_url":"https://pkp.sfu.ca/feed/atom","filter":null,"funding":null,"generator":"WordPress","generator_raw":"WordPress","home_page_url":"https://pkp.sfu.ca/news/","id":"1fc8db8d-6943-4efd-8a78-7723c41ab59f","indexed":true,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":0,"relative_url":null,"ror":null,"secure":true,"slug":"pkp","status":"active","subfield":"1710","subfield_validated":null,"title":"Public Knowledge Project","updated_at":1776328463.946064,"use_api":null,"use_mastodon":false,"user_id":null},"blog_name":"Public Knowledge Project","blog_slug":"pkp","content_html":"The PHP library that renders article citations in Open Journal Systems (OJS) will change maintenance hands from the library\u2019s creator, Sebastian B\u00f6ttger, to PKP.
\nFor those of you unfamiliar, citeproc-php is the server-side library that takes raw article metadata (author names, titles, publication dates, DOIs) and transforms it into formatted citations using Citation Style Language (CSL) styles including APA, MLA, Chicago, Vancouver, and more.
\nIn OJS, this library works behind the scenes of the Citation Style Language plugin. When a reader clicks \u201cHow to Cite\u201d on an article page, or when a journal manager selects a citation style under Settings, citeproc-php generates the formatted output.
\nFor OJS users: Nothing immediate. Your citation displays will continue working exactly as they do now. The plugin settings in your dashboard remain unchanged.
\nFor developers: The citeproc-php library has moved to the PKP organization under Github and PKP will take an active role in its steering and release management. Little else will change \u2013 PKP would like to continue working with the active contributor community already supporting the library.
\nFor the broader audience: PKP has long benefited from, and frequently contributed to, 3rd-party Free and Open Source (FOSS) libraries like citeproc-php. As good FOSS citizens, PKP recognizes the value of a strong ecosystem of support and contribution; this move to formally maintaining citeproc-php is done in recognition of the need for material support for the tools we all rely upon.
\nThe handover to PKP makes strategic sense since citeproc-php\u2019s primary consumer is OJS and therefore PKP has a strong interest in keeping the library secure and updated.
\nThe PKP team would like to thank Sebiastian B\u00f6ttger for his excellent work supporting the scholarly publishing community for many years!
\nThe handover discussion can be found in GitHub #200. The new pkp/citeproc-php GitHub page can be found here.\u00a0
The post citeproc-php is under new management! appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/r58bv-8dm69","funding_references":null,"guid":"https://pkp.sfu.ca/?p=18764","id":"d8fec180-e406-436f-9a9f-00613a903c03","image":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg","images":[{"alt":"Stockholm Archipelago photo by PKP's Jason Nugent represents the distinct yet deeply interconnected nature of landscapes, ecosystems, and infrastructures, a theme of PKP's Community Newsletter, Archipelago.","height":"576","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg","srcset":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-300x169.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-768x432.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1536x864.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue.jpg","width":"1024"},{"src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Citeproc-php-April-2026-WP-May-issue-1024x576.jpg"}],"indexed":true,"indexed_at":1776374604,"language":"en","parent_doi":null,"published_at":1776373941,"reference":[],"registered_at":0,"relationships":[],"rid":"gg4fn-qbc59","status":"active","summary":"\n \n The PHP library that renders article citations in Open Journal Systems (OJS) will change maintenance hands from the library\u2019s creator, Sebastian B\u00f6ttger, to PKP.\n \n\nWhat does citeproc-php do? For those of you unfamiliar, citeproc-php is the server-side library that takes raw article metadata (author names, titles, publication dates, DOIs) and transforms it into formatted citations using Citation Style Language (CSL)","tags":["Community Newsletter","Citations","Citeproc-php","FOSS","Metadata"],"title":"citeproc-php is under new management!","updated_at":1776373941,"url":"https://pkp.sfu.ca/2026/04/16/citeproc-php-is-now-managed-by-pkp/","version":"v1"},{"abstract":"Matt and I were discussing a paper from last year, Korneisel and Maddin (2025) on the evolution of the atlas\u2013axis complex. (It\u2019s excellent, by the way. Really comprehensive.) Mike: Atlases are so weird.","archive_url":null,"authors":[{"affiliation":[{"id":"https://ror.org/0524sp257","name":"University of Bristol"}],"contributor_roles":[],"family":"Taylor","given":"Mike","url":"https://orcid.org/0000-0002-1003-5675"}],"blog":{"archive_collection":22153,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22153/20231105213934/","archive_timestamps":null,"authors":[{"name":"Mike Taylor"}],"canonical_url":null,"category":"earthAndRelatedEnvironmentalSciences","community_id":"0e13541f-417e-46c0-a859-65927249df72","created_at":1675209600,"current_feed_url":null,"description":"SV-POW! ... All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/atom+xml","feed_url":"https://svpow.com/feed/atom/","filter":null,"funding":null,"generator":"WordPress.com","generator_raw":"WordPress.com","home_page_url":"https://svpow.com","id":"c6cbbd2e-4675-4680-8a3f-784388009821","indexed":false,"issn":"3033-3695","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729882329,"relative_url":null,"ror":null,"secure":true,"slug":"svpow","status":"active","subfield":"1911","subfield_validated":true,"title":"Sauropod Vertebra Picture of the Week","updated_at":1776328855.589855,"use_api":true,"use_mastodon":false,"user_id":"04d03585-c8bb-40f2-9619-5076a5e0aed2"},"blog_name":"Sauropod Vertebra Picture of the Week","blog_slug":"svpow","content_html":"Matt and I were discussing a paper from last year, Korneisel and Maddin (2025) on the evolution of the atlas\u2013axis complex. (It’s excellent, by the way. Really comprehensive.)
\nMike: Atlases are so weird. It occurs to me that had the nomenclatural dice fallen differently, we might not even consider them vertebrae at all, just as we don’t consider metacarpals to be manual phalanges. We might have considered the axial skeleton to consist of skull, atlas, and a sequence of vertebrae starting with the axis. (And, yes, ribs, chevrons and sternal plates.)
\nMatt: Good point on […] the weirdness of the atlas. The atlas in particular really does feel like an embryonic segmentation error codified and exapted into a useful structure.
\nMike: Your mom is an embryonic segmentation error codified and exapted into a useful structure.
\n\n
Dise\u00f1ar la bibliograf\u00eda para los cursos de Biolog\u00eda
Generar la lista de la bibliograf\u00eda, tanto b\u00e1sica como complementar\u00eda, constituye un proceso fundamental en el dise\u00f1o curricular porque conforma el n\u00facleo te\u00f3rico, metodol\u00f3gico y conceptual de la clase, establece el enfoque, las dimensiones, el contexto, el alcance y define claramente el marco conceptual que guiar\u00e1 el curso.
Dada su relevancia el proceso de seleccionar y crear la bibliograf\u00eda que se usar\u00e1 debe ser meticuloso, detallado y formal porque ser\u00e1 el sustento que utilizar\u00e1n los alumnos para obtener definiciones, establecer marcos de referencia, localizar bibliograf\u00eda relacionada y\u00a0
Este elemento es com\u00fanmente relegado en el dise\u00f1o curricular, esto es evidente porque existe muy poca literatura\u00a0 que aborde este tema,\u00a0 no se le da la importancia suficiente, al parecer, en muchos casos se ve como un requisito, no c\u00f3mo un componente fundamental del programa de una clase.
En este texto escribe algunas ideas que se deben considerar para generar la lista de la bibliograf\u00eda de un curso de ciencia, o de cualquier otro tema aunque yo me centrar\u00e9 en uno de biolog\u00eda.
El dise\u00f1o
La primera etapa es el dise\u00f1o de la bibliograf\u00eda del plan.
I wanted to know:
\n\n\n\nAfter a little bit of searching, I couldn’t find any answers. So I decided to use R to retrieve the necessary info from Uniprot and calculate it myself. I thought I’d post it here in case it’s useful for others.
\n\n\n\nWe’ll start with the info I wanted. According to Uniprot there are 20,659 proteins in the human proteome. One quarter of these have one or more TMD. The majority have one TMD and there are almost 1,000 7TM proteins (all those GPCRs, I guess). There’s 413 4TM and 327 2TM proteins. We can find examples of 1 through 17 TMDs, there’s no proteins with 18, 4 proteins with 19TM, 21 with 24TM and 2 with 38TM.
\n\n\n\nThe analysis is done simply by looking at how many TRANSMEM Uniprot has for each IDs in the reference proteome. I have not distinguished between helical and partial entries, and of course it’s possible that the annotations are not quite correct.
| TMDs | Count | Frequency (proteome as %) | Frequency (TMDPs as %) |
| 1 | 2402 | 11.6 | 45.8 |
| 2 | 327 | 1.6 | 6.2 |
| 3 | 159 | 0.8 | 3.0 |
| 4 | 413 | 2.0 | 7.9 |
| 5 | 77 | 0.4 | 1.5 |
| 6 | 276 | 1.3 | 5.3 |
| 7 | 947 | 4.6 | 18.0 |
| 8 | 83 | 0.4 | 1.6 |
| 9 | 63 | 0.3 | 1.2 |
| 10 | 123 | 0.6 | 2.3 |
| 11 | 75 | 0.4 | 1.4 |
| 12 | 202 | 1.0 | 3.8 |
| 13 | 24 | 0.1 | 0.5 |
| 14 | 26 | 0.1 | 0.5 |
| 15 | 13 | 0.1 | 0.2 |
| 16 | 1 | 0.0 | 0.0 |
| 17 | 9 | 0.0 | 0.2 |
| 19 | 4 | 0.0 | 0.1 |
| 24 | 21 | 0.1 | 0.4 |
| 38 | 2 | 0.0 | 0.0 |
Having written this code, I decided to run some other proteomes to see how they compare.
\n\n\n\nThese four organisms have between 18% and 29% of the proteome made of proteins with TMDs. The pattern of numbers of TMDs are kind of similar although there’s no peak in yeast for 7TM and the peaks for 2, 4, 6 or 12 TMs differ from human.
\n\n\n\nMaybe this information is out there in some database or other. As I said, I couldn’t find something easily. Even if there are more precise ways of determining the TMDs, I think this data is good enough to know roughly what the proportions are.
\n\n\n\nI manually downloaded the fasta.gz files for the reference proteomes. They are currently linked here.
\n\n\n\nTo extract all the Uniprot IDs, I used a shell one-liner:
\n\n\n\nawk '/^>sp\\|.*\\|/{gsub(/^>sp\\|/,""); gsub(/\\|.*/,""); print ">" $0; next} {print}' file.fasta | grep "^>" | sed 's/>//g' > species_uniprot.txt\nThen I used this R script. The main function can probably be simplified. I had to add several checks to make sure I got all the data back from the API. Before posting, I tried to cut it back to make it easier to read, but Ionly succeeded in breaking the script! This is the working version.
\n\n\n\n# if (!require("BiocManager", quietly = TRUE)) {\n# install.packages("BiocManager") \n# }\n# BiocManager::install("biomaRt")\nlibrary(httr)\nlibrary(stringr)\nlibrary(ggplot2)\nlibrary(biomaRt)\nlibrary(dplyr)\nlibrary(tidyr)\nlibrary(cowplot)\n\n## FUNCTIONS ----\n\nisJobReady <- function(jobId, pollingInterval = 5, maxWaitSeconds = 3600) {\n if (is.null(jobId) || length(jobId) == 0 || is.na(jobId) || !nzchar(jobId)) {\n return(FALSE)\n }\n nTries <- ceiling(maxWaitSeconds / pollingInterval)\n for (i in 1:nTries) {\n url <- paste("https://rest.uniprot.org/idmapping/status/", jobId, sep = "")\n r <- GET(url = url, accept_json())\n status <- content(r, as = "parsed")\n if (!is.null(status[["results"]]) || !is.null(status[["failedIds"]])) {\n return(TRUE)\n }\n if (!is.null(status[["messages"]])) {\n print(status[["messages"]])\n return(FALSE)\n }\n Sys.sleep(pollingInterval)\n }\n return(FALSE)\n}\n\nretrieveUniprotInfo <- function(x,\n chunk_size = 5000,\n maxWaitSeconds = 3600,\n taxId = "9606",\n progress = TRUE) {\n normalize_uniprot_ids <- function(values) {\n values <- trimws(values)\n # Accept FASTA-style headers like: sp|P12345|... or tr|A0A...|...\n m <- str_match(values, regex("^>?\\\\s*(?:sp|tr)\\\\|([^|]+)\\\\|", ignore_case = TRUE))\n values <- ifelse(!is.na(m[, 2]), m[, 2], values)\n values\n }\n \n ids <- unique(normalize_uniprot_ids(x))\n ids <- ids[!is.na(ids) & nzchar(ids)]\n if (length(ids) == 0) {\n stop("No valid identifiers were provided to retrieveUniprotInfo().")\n }\n \n fields <- "accession,id,protein_name,gene_names,ft_transmem,length,cc_function,cc_subcellular_location,go_p,go_c"\n acc_pattern <- "^[OPQ][0-9][A-Z0-9]{3}[0-9](-[0-9]+)?$|^[A-NR-Z][0-9](?:[A-Z][A-Z0-9]{2}[0-9]){1,2}(-[0-9]+)?$"\n is_accession <- str_detect(ids, acc_pattern)\n \n split_into_chunks <- function(values, chunk_size = chunk_size) {\n split(values, ceiling(seq_along(values) / chunk_size))\n }\n \n get_next_link <- function(link_header) {\n if (is.null(link_header)) {\n return(NULL)\n }\n links <- unlist(strsplit(link_header, ",\\\\s*"))\n next_link <- links[str_detect(links, "rel=\\\\\\"next\\\\\\"")]\n if (length(next_link) == 0) {\n return(NULL)\n }\n next_url <- str_extract(next_link[1], "(?<=<).+?(?=>)")\n if (is.na(next_url) || !nzchar(next_url)) {\n return(NULL)\n }\n next_url\n }\n \n read_tsv_response <- function(resp) {\n read.table(\n text = content(resp, as = "text", encoding = "UTF-8"),\n sep = "\\t",\n header = TRUE,\n fill = TRUE,\n quote = "",\n comment.char = "",\n check.names = FALSE\n )\n }\n \n fetch_from_redirect <- function(redirect_url) {\n if (is.null(redirect_url) || length(redirect_url) == 0 ||\n is.na(redirect_url) || !nzchar(redirect_url)) {\n return(NULL)\n }\n \n # The paged idmapping results endpoint is capped at size <= 500.\n # Use stream endpoint to retrieve the full chunk in one response.\n stream_url <- gsub("/results/", "/results/stream/", redirect_url)\n sep <- ifelse(str_detect(stream_url, "\\\\?"), "&", "?")\n stream_url <- paste0(\n stream_url,\n sep,\n "fields=", URLencode(fields, reserved = TRUE),\n "&format=tsv"\n )\n \n r <- GET(url = stream_url)\n if (status_code(r) < 400) {\n return(read_tsv_response(r))\n }\n \n # Fallback to paged endpoint if stream is unavailable.\n sep <- ifelse(str_detect(redirect_url, "\\\\?"), "&", "?")\n url <- paste0(\n redirect_url,\n sep,\n "fields=", URLencode(fields, reserved = TRUE),\n "&format=tsv&size=500"\n )\n \n r <- GET(url = url)\n stop_for_status(r)\n resultsTable <- read_tsv_response(r)\n \n next_url <- get_next_link(headers(r)[["link"]])\n while (!is.null(next_url) && !is.na(next_url) && nzchar(next_url)) {\n r <- GET(url = next_url)\n stop_for_status(r)\n resultsTable <- rbind(resultsTable, read_tsv_response(r))\n next_url <- get_next_link(headers(r)[["link"]])\n }\n \n resultsTable\n }\n \n map_ids <- function(values, from_db, to_db, chunk_size, taxId = NULL,\n label = "ids") {\n if (length(values) == 0) {\n return(NULL)\n }\n \n results_list <- list()\n chunks <- split_into_chunks(values, chunk_size = chunk_size)\n n_chunks <- length(chunks)\n for (i in seq_along(chunks)) {\n chunk <- chunks[[i]]\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d (%d ids)\\n",\n label, i, n_chunks, length(chunk)))\n }\n \n files <- list(\n ids = paste0(chunk, collapse = ","),\n from = from_db,\n to = to_db\n )\n if (!is.null(taxId)) {\n files$taxId <- taxId\n }\n \n r <- POST(url = "https://rest.uniprot.org/idmapping/run", body = files,\n encode = "multipart", accept_json())\n stop_for_status(r)\n submission <- content(r, as = "parsed", encoding = "UTF-8")\n \n job_id <- submission[["jobId"]]\n if (is.null(job_id) || length(job_id) == 0 || is.na(job_id) || !nzchar(job_id)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: no jobId returned\\n",\n label, i, n_chunks))\n }\n next\n }\n if (!isJobReady(job_id, maxWaitSeconds = maxWaitSeconds)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: timeout/not ready\\n",\n label, i, n_chunks))\n }\n next\n }\n \n details_url <- paste("https://rest.uniprot.org/idmapping/details/",\n job_id, sep = "")\n r <- GET(url = details_url, accept_json())\n stop_for_status(r)\n details <- content(r, as = "parsed", encoding = "UTF-8")\n \n redirect_url <- details[["redirectURL"]]\n if (is.null(redirect_url) || length(redirect_url) == 0 ||\n is.na(redirect_url) || !nzchar(redirect_url)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: missing redirectURL\\n",\n label, i, n_chunks))\n }\n next\n }\n chunk_result <- fetch_from_redirect(redirect_url)\n if (is.null(chunk_result)) {\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: invalid redirectURL\\n",\n label, i, n_chunks))\n }\n next\n }\n \n results_list[[length(results_list) + 1]] <- chunk_result\n if (isTRUE(progress)) {\n cat(sprintf("[UniProt] %s chunk %d/%d: completed\\n",\n label, i, n_chunks))\n }\n }\n \n if (length(results_list) == 0) {\n return(NULL)\n }\n do.call(rbind, results_list)\n }\n \n accession_ids <- ids[is_accession]\n gene_like_ids <- ids[!is_accession]\n \n acc_results <- map_ids(\n values = accession_ids,\n from_db = "UniProtKB_AC-ID",\n to_db = "UniProtKB",\n chunk_size = chunk_size,\n label = "accessions"\n )\n \n gene_results <- map_ids(\n values = gene_like_ids,\n from_db = "Gene_Name",\n to_db = "UniProtKB-Swiss-Prot",\n chunk_size = chunk_size,\n taxId = taxId,\n label = "gene_names"\n )\n \n results_list <- list()\n if (!is.null(acc_results)) {\n results_list[[length(results_list) + 1]] <- acc_results\n }\n if (!is.null(gene_results)) {\n results_list[[length(results_list) + 1]] <- gene_results\n }\n \n if (length(results_list) == 0) {\n stop("No UniProt results were returned. Check identifiers and taxId.")\n }\n \n resultsTable <- do.call(rbind, results_list)\n if ("Entry" %in% colnames(resultsTable)) {\n resultsTable <- resultsTable[!duplicated(resultsTable$Entry), ]\n }\n return(resultsTable)\n}\n\n\n\n## SCRIPT ----\n\nspecies <- c("human", "zebrafish", "drosophila", "worm", "yeast")\nsci_names <- c("human" = "Homo sapiens", "zebrafish" = "Danio rerio", "drosophila" = "Drosophila melanogaster",\n "worm" = "Caenorhabditis elegans", "yeast" = "Saccharomyces cerevisiae")\n\nfor (org in species) {\n # look up scientific name of org\n sci_name <- sci_names[org]\n output_path <- paste0("Output/Data/", org, "_uniprot.csv")\n if(file.exists(output_path)) {\n message(paste("File", output_path, "already exists. Loading", org))\n df <- read.csv(output_path)\n } else {\n message(paste("Retrieving UniProt info for", org))\n species_ids <- read.delim(paste0("Data/", org, "_uniprot.txt"), header = FALSE)\n names(species_ids) <- c("uniprot_id")\n df <- retrieveUniprotInfo(species_ids$uniprot_id)\n # save this result\n write.csv(df, output_path, row.names = FALSE)\n }\n \n df$tms <- str_count(df$Transmembrane, "TRANSMEM")\n tm_counts <- df %>%\n group_by(tms) %>%\n summarise(count = n()) %>% \n filter(tms > 0)\n \n p1 <- ggplot(tm_counts, aes(x = tms, y = count)) +\n geom_col(fill = "#009988") +\n labs(x = "Transmembrane domains", y = "Count",\n title = sci_name) +\n lims(x = c(0.5,NA), y = c(0,NA)) +\n theme_bw(10)\n \n p2 <- SuperPlotR::pieplot(x1 = c(sum(df$tms == 0), sum(df$tms > 0)),\n cols = c("#bbbbbb", "#009988")) +\n # blank background and no legend\n theme_void() +\n theme(legend.position = "none")\n \n # inset p2 in p1 and add information about the percentages\n p <- ggdraw() +\n draw_plot(p1) +\n # top right\n draw_plot(p2, x = 0.9, y = 0.9, hjust = 1, vjust = 1, width = 0.4, height = 0.4) +\n draw_label(paste0("Total Proteins: ", nrow(df),\n "\\nNo TM: ", round(sum(df$tms == 0) / nrow(df) * 100, 1),\n "%\\nWith TM(s): ",\n round(sum(df$tms > 0) / nrow(df) * 100, 1), "%"),\n x = 0.97, y = 0.85, hjust = 1, vjust = 1, size = 8)\n plot_path <- paste0("Output/Plots/", org, "_uniprot_tm_counts.png")\n ggsave(plot_path, p, width = 1600, height = 900, units = "px", dpi = 300)\n}\nNote that I am using {cowplot} at the end to inset the pie chart and to add the text onto the main plot.
—
\n\n\n\nThe post title comes from “My Domain” by Bernard Butler from his People Move On album.
\n","doi":"https://doi.org/10.59350/0ahfj-mxf51","funding_references":null,"guid":"https://quantixed.org/?p=3753","id":"70276eb9-e532-42ea-b09c-8d05e8df0b84","image":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts.png","images":[{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts.png","width":"1024"},{"height":"576","sizes":"auto, (max-width: 1024px) 100vw, 1024px","src":"https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1024x576.png","srcset":"https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1024x576.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-300x169.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-768x432.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1536x864.png, https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts.png","width":"1024"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/human_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/drosophila_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/worm_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/yeast_uniprot_tm_counts-1024x576.png"},{"src":"https://quantixed.org/wp-content/uploads/2026/04/zebrafish_uniprot_tm_counts-1024x576.png"}],"indexed":true,"indexed_at":1776341368,"language":"en","parent_doi":null,"published_at":1776341040,"reference":[],"registered_at":0,"relationships":[],"rid":"k78xz-cmp18","status":"active","summary":"I wanted to know: how many proteins in the human proteome have transmembrane domains? of those that do, how many have 1 or 2 or n transmembrane domains? After a little bit of searching, I couldn\u2019t find any answers. So I decided to use R to retrieve the necessary info from Uniprot and calculate it myself. I thought I\u2019d post it here in case it\u2019s useful for others. Human We\u2019ll start with the info I wanted.","tags":["Science","Bioinformatics","Cell Biology","Proteins","Rstats"],"title":"My Domain: proteome-wide scanning of TMDs","updated_at":1776112087,"url":"https://quantixed.org/2026/04/16/my-domain-proteome-wide-scanning-of-tmds/","version":"v1"},{"abstract":null,"archive_url":null,"authors":[{"affiliation":[{"name":"Front Matter"}],"contributor_roles":[],"family":"Fenner","given":"Martin","url":"https://orcid.org/0000-0003-1419-2405"}],"blog":{"archive_collection":22096,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22096/20231101172748/","archive_timestamps":[20231101172748,20240501180447,20241101172601],"authors":[{"name":"Martin Fenner","url":"https://orcid.org/0000-0003-1419-2405"}],"canonical_url":null,"category":"computerAndInformationSciences","community_id":"91dd2c24-5248-4510-9c2b-30b772bf8b60","created_at":1672561153,"current_feed_url":"","description":"The Front Matter Blog covers the intersection of science and technology since 2007.","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/15a362ea-8138-42b8-917f-1840a92addf8/logo","feed_format":"application/atom+xml","feed_url":"https://blog.front-matter.de/atom","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.52","home_page_url":"https://blog.front-matter.de","id":"74659bc5-e36e-4a27-901f-f0c8d5769cb8","indexed":null,"issn":"2749-9952","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":"https://hachyderm.io/@mfenner","prefix":"10.53731","registered_at":1729685319,"relative_url":null,"ror":null,"secure":true,"slug":"front_matter","status":"active","subfield":"1710","subfield_validated":null,"title":"Front Matter","updated_at":1776327653.24951,"use_api":true,"use_mastodon":true,"user_id":"8498eaf6-8c58-4b58-bc15-27eda292b1aa"},"blog_name":"Front Matter","blog_slug":"front_matter","content_html":"All science blogs participating in the Rogue Scholar science blog archive provide a feed (in RSS, Atom or JSON Feed format) that includes not only metadata and full-text content for all archived blog posts, but also metadata about the blog itself. This includes required metadata such as the blog homepage and feed URL, but also an optional description, logo, license, language and science subject area, using the OpenAlex subfield, which maps to the Scopus All Science Journal Classification (ASJC) widely used to classify journals. Rogue Scholar also supports an optional International Serial Number (ISSN) as globally unique identifier for the blog \u2013 this blog has ISSN 2749-9952. While I encourage all Rogue Scholar blogs to obtain an ISSN, there are two important limitations of the ISSN:
An alternative approach is using DOIs as globally unique persistent identifier for the blog, and this aligns with using DOIs to register blog posts. The DOI registration agencies Crossref and DataCite support the resource type Journal and some other periodical publication formats (e.g. Book Series, Proceedings Series), but unfortunately not Blog. And they support the optional inclusion of one or more ISSNs.
Until Crossref and DataCite support the resource type Blog, Rogue Scholar will register blogs as resource type Journal, and include the optional ISSN. Front Matter is a Crossref member and can do this for all blogs participating in Rogue Scholar where it registers the DOIs (almost all of them). The Crossref resource type Journal unfortunately doesn't support many metadata (e.g. no subject classification), and Journal is mainly used as a container to describe a collection of Journal Articles. The DOI uses the same DOI prefix as the corresponding blog posts, and the blog slug (unique identifier used everywhere in Rogue Scholar) as the suffix.
The new blog DOI is displayed on the blog community page (e.g. here for this blog), and clicking on the DOI redirects users to the blog homepage (also shown, as is the ISSN).
It will take a few days until the DOIs for all blogs where Rogue Scholar manages DOI registration are registered.
Periodical publications typically have page numbers, issues and volumes. While page numbers and issues are typically not used for online publication formats such as blogs, volumes still make sense in a different context. One particular challenge with science blogs that the Rogue Scholar science blog archive tries to address is long-term archiving. While different approaches can address is problem, the basic idea is Lots of Copies Keep Stuff Safe (LOCKSS). And blog volumes \u2013 all blog posts from a particular blog from a given year \u2013 can simplify long-term blog archiving in multiple places.
This week Rogue Scholar implemented blog volumes for all participating blogs:
Clicking on a year shows all blog posts from that blog published in that year:
Currently this is an automatically generated search in Rogue Scholar. The next step is generating a downloadable file with all content and metadata of the blog posts published in that year that can be archived in Rogue Scholar and elsewhere. I am working on generating these blog volumes in ePub format. These blog volumes will be registered with DOIs using the Crossref Journal Volume resource type (again, Blog Volume as resource type doesn't exist yet, and DataCite also has no Journal Volume ResourceTypeGeneral, Collection is the closest match).
These blog volumes can then be archived also in other repositories, and the ePub format makes reuse easier compare to PDF, as it is much easier to extract content from ePub. It has not escaped our notice that the specific archiving we are postulating immediately suggests a possible copying mechanism for a blog when its authors want to migrate to a different blogging platform.
Please use Slack, email, Mastodon, or Bluesky if you have any questions or comments, in particular if you are interested in archiving blogs via blog volumes generated by Rogue Scholar.
At rOpenSci, we\u2019re continually grateful for the support and engagement of our community, who help make research open-source stronger, more inclusive, and more collaborative. The software peer review program continues to grow, and today we announce that our editorial team keeps expanding:
We\u2019re excited to welcome Alec Robitaille and Lucy D\u2019Agostino McGowan as new editors. Alec joins our general review team, and Lucy our statistical software review team. Their expertise and dedication will help sustain and strengthen software peer review, ensuring that software reviews continue to meet the highest standards of quality, transparency, and impact.
Meet our new editors!
Alec is a graduate student at Memorial University of Newfoundland and Labrador (Canada) studying foraging ecology, habitat selection and social networks in caribou and other ungulates. He has been involved in many projects along the way, from measuring muskrat habitat and lake ice dynamics at McGill University to estimating drought sensitivity in Canada\u2019s forests with the Canadian Forestry Service. Passionate about teaching open science and programming, he regularly mentors peers, runs workshops, develops example repositories, and organizes a Bayesian stats colearning group. He maintains the package spatsoc (reviewed by rOpenSci in 2018) and has developed smaller packages with diverse applications including remote sensing (irg), social networks (hwig), camera trap monitoring (camtrapmonitoring), and animal movement (distanceto). He reviewed the ohun, chopin, and emodnet.wfs packages for rOpenSci, guest edited for the rredlist package and is currently handling the reviews for the ActiGlobe, and saperlipopette packages.
I first connected with the rOpenSci community through the review process for the package spatsoc in 2018 as part of our manuscript submission at Methods in Ecology and Evolution. During the review, I was thoroughly impressed by how welcoming the community was, and how effective the process was in helping me learn how to improve the package. rOpenSci is a landmark in the R and open science ecosystems with an ever evolving community to learn from and to be a part of. I am very grateful to be given the opportunity to continue contributing to rOpenSci in this new role as editor.
Lucy D\u2019Agostino McGowan is an associate professor in the Department of Statistical Sciences at Wake Forest University. She received her PhD in Biostatistics from Vanderbilt University and completed her postdoctoral training at Johns Hopkins University Bloomberg School of Public Health. Her research focuses on causal inference, statistical communication, analytic design theory, and data science pedagogy. Lucy can be found blogging at livefreeordichotomize.com, on Blue Sky @LucyStats.bsky.social, and podcasting on Casual Inference.
I am so thrilled to join the rOpenSci editorial team! I love the rOpenSci community and mission and am grateful for the opportunity to contribute.
rOpenSci\u2019s software peer review program brings together volunteers to collaboratively review scientific and statistical software according to transparent, constructive, and open standards. Editors manage submissions, coordinate reviewers, and help guide packages through review to improve code quality, documentation, and usability.
This program is possible thanks to the many community members: authors submitting their packages, reviewers volunteering their time and expertise, and editors like Alec and Lucy who help managing reviews and maintaining a supportive process.
Are you considering submitting your package for review? These resources will help:
Would you like to review packages? Fill out the rOpenSci Reviewer Sign-Up Form to volunteer to review.
Welcome again Alec and Lucy! We\u2019re thrilled to have you join the editorial team.
","doi":"https://doi.org/10.59350/v939m-qkj86","funding_references":null,"guid":"https://doi.org/10.59350/v939m-qkj86","id":"20501628-637e-4ec4-8b35-bdf597339d8c","image":null,"images":[{"alt":"headshot of Alec Robitaille","src":"https://ropensci.org/img/team/alec-robitaille.png"},{"alt":"headshot of Lucy D'Agostino McGowan","src":"https://ropensci.org/img/team/lucy-dagonstino-mcgowan.jpg"},{"src":"https://ropensci.org/img/team/alec-robitaille.png"},{"src":"https://ropensci.org/img/team/lucy-dagonstino-mcgowan.jpg"}],"indexed":true,"indexed_at":1776313654,"language":"en","parent_doi":null,"published_at":1776297600,"reference":[],"registered_at":0,"relationships":[],"rid":"w9g9j-0bd39","status":"active","summary":"At rOpenSci, we\u2019re continually grateful for the support and engagement of our community, who help make research open-source stronger, more inclusive, and more collaborative. The software peer review program continues to grow, and today we announce that our editorial team keeps expanding: We\u2019re excited to welcome\n\n Alec Robitaille\n\nand\n\n Lucy D\u2019Agostino McGowan\n\nas new editors.","tags":["Software Peer Review","Editors","ROpenSci Team"],"title":"Expanding the Editorial Team: Alec Robitaille and Lucy D'Agostino McGowan Join as Editors","updated_at":1776311326,"url":"https://ropensci.org/blog/2026/04/16/editors2026/","version":"v1"},{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Strecker","given":"Dorothea","url":"https://orcid.org/0000-0002-9754-3807"},{"affiliation":[{"id":"https://ror.org/01hcx6992","name":"Humboldt-Universit\u00e4t zu Berlin"}],"contributor_roles":[],"family":"Ochsner","given":"Catharina","url":"https://orcid.org/0009-0005-3885-3951"}],"blog":{"archive_collection":24081,"archive_host":null,"archive_prefix":null,"archive_timestamps":null,"authors":null,"canonical_url":null,"category":"computerAndInformationSciences","community_id":"53174590-b8d0-4c88-b121-4ca75f7de145","created_at":1717668020,"current_feed_url":null,"description":"Research Group Information Management @ Humboldt-Universit\u00e4t zu Berlin","doi":null,"doi_as_guid":false,"favicon":null,"feed_format":"application/rss+xml","feed_url":"https://infomgnt.org/index.xml","filter":null,"funding":null,"generator":"Quarto","generator_raw":"Quarto 1.4.555","home_page_url":"https://infomgnt.org","id":"17927ce5-1239-43fb-a3c9-2acb8a679d11","indexed":true,"issn":"2944-6848","language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1729503399,"relative_url":null,"ror":null,"secure":true,"slug":"infomgnt","status":"active","subfield":"3309","subfield_validated":null,"title":"Research Group Information Management @ Humboldt-Universit\u00e4t zu Berlin","updated_at":1776327809.623468,"use_api":null,"use_mastodon":false,"user_id":"dbffda7d-f391-48fb-a6d2-d4a284c59c8d"},"blog_name":"Research Group Information Management @ Humboldt-Universit\u00e4t zu Berlin","blog_slug":"infomgnt","content_html":"
The Ambassadors for Open Science program of the Berlin University Alliance (BUA) aims to promote the knowledge and application of Open Science in various subjects. As Open Science experts and enthusiasts, the ambassadors show researchers, students and decision-makers at their institution how they can benefit from and make use of Open Science infrastructure (\u201cAuftakt F\u00fcr Neu Ernannte BUA Open Science Ambassadors\u201d (2024)).
\nAs ambassadors for the philosophical faculty Dorothea Strecker and Catharina Ochsner organized a writing retreat in collaboration with Dr.\u00a0Kaitlin Montague for the PhD students of the philosophical faculty of the Humboldt-Universit\u00e4t zu Berlin (HU Berlin). The retreat took place at the workation space coconat and offered 16 doctoral students the opportunity of focused writing time, writing workshops, and Open Science-focused discussions in a new and peaceful environment. The workshop was designed for doctoral students to discuss and develop essential skills of academic writing. During the weekend we addressed Open Access publication strategies for doctoral students as well as a range of writing-related issues, including style and voice, writing discipline, drafting, narrative development and organization, models of accountability, taming the \u201cinternal editor,\u201d and peer review.
\nWe thank the BUA for making this writing retreat possible and we thank all participants for joining us and engaging in many discussions on their writing process, Open Access, and Open Science. In addition to focused writing time and courses on Open Science and Open Access the students were able to socialize with their peers and build a small community that will hopefully continue beyond the retreat.
\nFurther information about the research group can be found on our official website.
\nThis text \u2013 excluding quotes and otherwise labelled parts \u2013 is licensed under the CC BY 4.0 DEED.
\n@online{strecker2026,\n author = {Strecker, Dorothea and Ochsner, Catharina},\n title = {Recap: {Writing} {Retreat} for {Doctoral} {Students} of the\n {Philosophical} {Faculty}},\n date = {2026-04-16},\n url = {https://infomgnt.org/posts/2026-04-16-recap-writing-retreat-for-doctoral-students-of-the-philosophical-faculty},\n langid = {en}\n}\nThe 2025 Beacon dataset is out. It shows OJS powering 58,000 journals across 156 countries, with 2.5 million articles published and steady growth in software upgrades. Here\u2019s what the data tells us.
\nOpen Journal Systems (OJS) launched in 2002. By 2010, around 4,000 journals were using it. What followed was more than a decade of steady growth, typically between 5,000 and 7,000 new journals per year. That growth has now levelled off, and we think that deserves to be read for what it is: a sign of maturity, not of stagnation.
\nThe 2025 figures stand at 58,000 journals across 156 countries. That is the footprint of infrastructure.
\nFor librarians and open access advocates who have spent years making the case for community-owned publishing platforms, this scale carries real weight. For tens of thousands of journals, many of them Diamond Open Access charging neither authors nor readers, OJS is how publishing gets done.
\nGeography has always been one of the most revealing dimensions of the Beacon data, and this year is no different.
\nIndonesia continues to lead the world by a wide margin, accounting for more than 40% of all OJS journals globally. That position reflects both a sustained national commitment to open access policy and a publishing culture that has embraced open infrastructure at scale.
\nBrazil retains its place among the top countries, underpinned by a long tradition of publicly funded, openly accessible research communication.
\nAnd in a development worth noting, the United States has recently moved into the top three, as more North American institutions look seriously at open infrastructure as a sustainable path forward.
\nUnderstanding where open publishing is growing, and where it has not yet been adopted, is one of the main reasons PKP collects this data in the first place. It directly shapes decisions about language support, documentation, outreach, and development priorities. The geography of OJS adoption is not just interesting; it is useful.
\nThe Beacon data tracks article output per journal over time. The average number of articles per journal peaked at 62 in 2015, fell to 30 in 2022, and has since recovered to just over 40. Across all OJS journals, total article output now stands at 2.5 million articles.
\nThese figures matter beyond the headline numbers. They represent peer-reviewed research that, in many cases, would not be openly available without community-owned infrastructure. A significant portion of it comes from institutions and regions with no viable route into commercial open access. That research belongs to the communities that produced it, and it is accessible because of the infrastructure those communities chose to use.
\nSince 2024, there has been a 10% increase in journals upgrading to OJS version 3.3, with more than 4,000 journals completing the transition. Software upgrades take real effort: technical capacity, planning time, and often institutional backing. The fact that so many journals have made this move reflects the work PKP has put into making upgrades accessible, through sprint events, hosting support, education, and documentation.
\nThis data connects directly to PKP\u2019s focus on supporting the health and sustainability of journals already using OJS, not just reaching new ones.
\nPKP collects this data to inform its own decisions: where to focus development, where documentation is needed, where the software is being used in ways that point to unmet needs. The dataset is published openly on Harvard Dataverse so that researchers, institutions, and partners can draw their own conclusions from it. The global OJS community is not the subject of this research. It is the reason for it, and its primary beneficiary.
\nThat approach is central to how PKP works. Research informs what we build and how we prioritize. The Beacon project is one of the clearest expressions of that commitment.
\nIf you are a journal manager, a librarian building the case for open infrastructure, or a researcher working on scholarly communications, this data is freely available for you to use.
\nThe Beacon project is a longitudinal research initiative tracking the global adoption and use of Open Journal Systems. Data is collected annually and published openly under Creative Commons licensing.
\nThe post 58,000 Journals and Counting: The 2025 OJS Beacon Data appeared first on Public Knowledge Project.
","doi":"https://doi.org/10.59350/1v51v-e0831","funding_references":null,"guid":"https://pkp.sfu.ca/?p=18753","id":"80dc8e48-77f4-413f-9642-674f294a9334","image":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg","images":[{"alt":"Global OJS usage map. The numbers represented are in the article text.","height":"576","sizes":"(max-width: 1024px) 100vw, 1024px","src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg","srcset":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-300x169.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-768x432.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1536x864.jpg, https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue.jpg","width":"1024"},{"alt":"This map of active OJS presence in 2025 is by Saurabh Khanna, published on RPubs. The scale is 0 = lightest blue; 500+ = darkest blue.","src":"https://pkp.sfu.ca/wp-content/uploads/2026/04/Community-Beacon-April-2026-WP-May-issue-1024x576.jpg"}],"indexed":true,"indexed_at":1776278904,"language":"en","parent_doi":null,"published_at":1776278154,"reference":[],"registered_at":0,"relationships":[],"rid":"a50g6-y8p95","status":"active","summary":"\n \n The 2025 Beacon dataset is out. It shows OJS powering 58,000 journals across 156 countries, with 2.5 million articles published and steady growth in software upgrades. Here\u2019s what the data tells us.\n \n\nOpen Journal Systems (OJS) launched in 2002. By 2010, around 4,000 journals were using it. What followed was more than a decade of steady growth, typically between 5,000 and 7,000 new journals per year.","tags":["Community Newsletter","Beacon","OJS","Open Access","Open Infrastructure"],"title":"58,000 Journals and Counting: The 2025 OJS Beacon Data","updated_at":1776278154,"url":"https://pkp.sfu.ca/2026/04/15/58000-journals-2025-ojs-beacon-data/","version":"v1"},{"abstract":null,"archive_url":null,"authors":[{"contributor_roles":[],"family":"Moresi","given":"Louis","url":"https://orcid.org/0000-0003-3685-174X"}],"blog":{"archive_collection":22163,"archive_host":null,"archive_prefix":"https://wayback.archive-it.org/22163/20231105111054/","archive_timestamps":[20231105111054,20240505190806,20241105111040,20250505111057],"authors":null,"canonical_url":null,"category":"earthAndRelatedEnvironmentalSciences","community_id":"54ca6482-0a9b-420d-be44-b0e93fbad952","created_at":1697760000,"current_feed_url":null,"description":"Geodynamics, Computation and Education","doi":null,"doi_as_guid":false,"favicon":"https://rogue-scholar.org/api/communities/c8b71a5b-b872-47ab-89f7-7c84741d68fc/logo","feed_format":"application/rss+xml","feed_url":"https://www.underworldcode.org/rss/","filter":null,"funding":null,"generator":"Ghost","generator_raw":"Ghost 5.69","home_page_url":"https://www.underworldcode.org/","id":"9f6ce29f-3545-4482-987d-1a15bc256f4e","indexed":false,"issn":null,"language":"en","license":"https://creativecommons.org/licenses/by/4.0/legalcode","mastodon":null,"prefix":"10.59350","registered_at":1719281103,"relative_url":null,"ror":null,"secure":true,"slug":"underworldcode","status":"active","subfield":"1908","subfield_validated":null,"title":"Underworld Geodynamics Community","updated_at":1776328962.44395,"use_api":true,"use_mastodon":false,"user_id":"4f890b77-39e3-49c8-81cf-d159b5ad2f81"},"blog_name":"Underworld Geodynamics Community","blog_slug":"underworldcode","content_html":"A symbolic time derivatives: you can inspect it, display it in a notebook, and verify that the time discretisation is doing what you expect. And you can swap between Lagrangian, Semi-Lagrangian, and Eulerian approaches without rewriting the solver. Sympy introduces incredible flexibility for on-the-fly composition of time-dependent problems.
Many geodynamics equations involve a material derivative. Temperature advection-diffusion, viscoelastic stress transport, Navier-Stokes momentum. The material derivative $D\\phi/Dt$ combines the time rate of change with transport by the flow:
$$
\\frac{D\\phi}{Dt} = \\frac{\\partial \\phi}{\\partial t} + \\mathbf{v} \\cdot \\nabla\\phi
$$
Discretising this equation requires making specific choices. How do you handle the advection term? How many previous timesteps do you use? How do you deal with variable timestep sizes? These choices affect accuracy, stability, and computational cost.
In many finite element codes, these choices are baked into the solver implementation. A classic example is Crank-Nicolson time stepping: the flux must be evaluated at both the current and previous timestep, which means adding history terms to the left-hand side of the assembled system as well as introducing a time-derivative term on the right-hand side. The time discretisation reaches into both sides of the equation, and changing it means restructuring the solver. In UW3, the time derivative offers flux and force terms to the solver to handle symbolically as it sees fit.
UW3 provides four implementations of the time derivative, all sharing the same calling interface and working for scalar, vector and tensor quantities.
Eulerian stores history on the mesh. The material derivative is approximated by finite differences in time at fixed grid points, with an advection correction. This is the classical approach: simple, mesh-based, but subject to CFL stability constraints when advection dominates.
Semi-Lagrangian traces characteristics backward in time. At each mesh node, it asks: where was this material parcel at the previous timestep? It then interpolates the previous solution at that departure point. This is unconditionally stable because the material derivative is evaluated along the characteristic, not at a fixed grid point. There is no CFL constraint, though the user needs to be conscious of accuracy trade-offs inherent in the scheme.
Lagrangian follows particles through the flow. The history is stored on swarm variables and advected with the particles. This is the natural choice when material history matters physically, as in viscoelastic stress transport where the stress tensor must be advected and rotated with the material. Accuracy of this scheme depends upon the quality of the particle-layout (density of particles, presence of gaps).
Symbolic provides pure symbolic history without mesh or swarm storage. It is used internally by the constitutive model system for building expressions that involve time derivatives.
All four produce the same thing: a symbolic expression that can be embedded in a solver's weak form. The solver does not need to know which implementation is providing the time derivative. It sees a SymPy expression for $D\\phi/Dt$ and includes those terms when it differentiates the equation system to determine the Jacobians.
The time discretisation uses backward differentiation formulas (BDF). These are implicit multi-step methods that use solution values from previous timesteps to approximate the time derivative at the current step.
At order 1, this is the backward Euler method:
$$
\\frac{D\\phi}{Dt} \\approx \\frac{\\phi^n - \\phi^{n-1}}{\\Delta t}
$$
At order 2, BDF-2 uses two previous values for second-order accuracy:
$$
\\frac{D\\phi}{Dt} \\approx \\frac{\\frac{3}{2}\\phi^n - 2\\phi^{n-1} + \\frac{1}{2}\\phi^{n-2}}{\\Delta t}
$$
The coefficients $[3/2, -2, 1/2]$ are for constant timesteps. When the timestep varies, the coefficients adapt. If the current timestep is $\\Delta t _ n$ and the previous was $\\Delta t _ {n-1}$, with ratio $r = \\Delta t _ n / \\Delta t _ {n-1}$, the BDF-2 coefficients become:
$$
c _ 0 = \\frac{1+2r}{1+r}, \\quad c _ 1 = -(1+r), \\quad c _ 2 = \\frac{r^2}{1+r}
$$
This matters in practice. Underworld simulations usually adjust the timestep as the flow evolves.
The coefficients are computed as exact sympy.Rational values and wrapped in UWexpression objects. This means they are routed through PETSc's constants array, just like viscosity or density parameters. When the timestep changes, the coefficient values update without recompilation. The compiled C code has the same structure at every step, only the values in the constants array will change.
BDF handles the time derivative of the unknown. But transient problems may also need a time-weighted evaluation of the flux (the spatial operator). The Adams-Moulton (AM) family provides this.
At order 0, the flux is evaluated purely at the current time (fully implicit). At order 1 with $\\theta = 1/2$, we have the Crank-Nicolson scheme: the flux is averaged between the current and previous timestep. Higher order variants use more history values.
In UW3, the solver's flux time derivative (DFDt) provides an adams _ moulton _ flux() method that returns the appropriately weighted combination of the current flux and previous flux values as symbolic forms backed by stored evaluations. This expression then appears in the solver's $F _ 1$ template as a symbolic expression. Like the BDF coefficients, the AM weights are UWexpressions that update between timesteps.
The combination of BDF for the time derivative and AM for the flux evaluation gives a family of time integration schemes. BDF-1 with AM-0 is backward Euler. BDF-2 with AM-1 gives second-order accuracy in both the time derivative and the flux evaluation. The user controls this through the order parameter when creating the solver.
A BDF-2 scheme needs two previous solutions. At the start of a simulation, there is only one (the initial condition). UW3 handles this through automatic order ramping.
The DDt object tracks an effective _ order that starts at 1 and increases with each completed solve, up to the requested order. On the first timestep, BDF-1 is used regardless of what order was requested. On the second timestep, BDF-2 becomes available. On the third, BDF-3. The transition is automatic and the coefficients adjust accordingly.
History slots are initialised with the current solution value on the first call. This means the first BDF-1 step sees $\\phi^{n-1} = \\phi^n$, giving a zero time derivative. This is correct for a system starting from rest or from a steady-state initial condition. For impulsive starts, the first-order accuracy of the initial step is usually acceptable because the solution is changing rapidly and the timestep is typically small.
Consider advection-diffusion of temperature:
$$
\\frac{D T}{Dt} = \\nabla \\cdot (k \\nabla T) + H
$$
The solver setup looks like this:
adv_diff = uw.systems.AdvDiffusion(\n mesh, u_Field=T, V_fn=v,\n order=2, # BDF-2 / AM-1\n)\nadv_diff.constitutive_model.Parameters.diffusivity = k\nadv_diff.f = H\nadv_diff.solve(timestep=dt)\nThe order parameter controls the time discretisation. The solver builds its weak form from two template expressions, $F _ 0$ (force-like, paired with the test function) and $F _ 1$ (flux-like, paired with the test function gradient):
F0 = DuDt.bdf() / delta_t - H\nF1 = DFDt.adams_moulton_flux()\nAt order 1 (backward Euler / fully implicit), these expand to:
$$
F _ 0 = \\frac{T^n - T^{n-1}}{\\Delta t} - H
$$
$$
F _ 1 = k \\nabla T^n
$$
The flux is evaluated entirely at the current timestep. The system is first-order accurate in time.
At order 2 (BDF-2 / Crank-Nicolson), the expressions become:
$$
F _ 0 = \\frac{\\frac{3}{2}T^n - 2T^{n-1} + \\frac{1}{2}T^{n-2}}{\\Delta t} - H
$$
$$
F _ 1 = \\frac{1}{2} k \\nabla T^n + \\frac{1}{2} k \\nabla T^{n-1}
$$
The flux is now averaged between the current and previous timesteps. Both the time derivative and the flux evaluation are second-order accurate. The history terms $T^{n-1}$ and $T^{n-2}$ are mesh variable symbols managed by the DDt objects. The coefficients ($3/2$, $-2$, $1/2$, etc.) are UWexpression symbolic constants whose values update each step, if $\\Delta t$ changes, and no recompilation is required.
Note: terms that contain $T^n$ automatically populate the implicit parts of the solver, while history terms are automatically treated explicitly. They are also automatically combined with history terms originating, for example, from the constitutive law.
The solver differentiates through all of this for the Jacobian. The JIT compiler handles it like any other symbolic expression. From PETSc's perspective, the time-stepping machinery is invisible. It sees compiled C functions at quadrature points, with coefficient values arriving through the constants array.
The solve sequence for each timestep is:
The choice of DDt type is a one-parameter decision at solver construction:
# Default for advection-diffusion: Semi-Lagrangian (unconditionally stable)\nadv_diff = uw.systems.AdvDiffusion(mesh, u_Field=T, V_fn=v)\n\n# Override with Lagrangian (particle-based, requires a swarm)\nDTdt = uw.systems.Lagrangian_Swarm_DDt(\n swarm, psi_fn=T.sym,\n vtype=uw.VarType.SCALAR, degree=T.degree,\n continuous=True, order=2\n)\nadv_diff = uw.systems.AdvDiffusion(mesh, u_Field=T, V_fn=v, DuDt=DTdt)\n\n# Or Eulerian (mesh-based, for problems without strong advection)\nDTdt = uw.systems.Eulerian_DDt(\n mesh, T, vtype=uw.VarType.SCALAR,\n degree=T.degree, continuous=True, order=2\n)\nadv_diff = uw.systems.AdvDiffusion(mesh, u_Field=T, V_fn=v, DuDt=DTdt)\nThe solver does not need to be made aware of which DDt type you chose. It calls bdf() and adams _ moulton _ flux() and gets SymPy expressions. The physics of the time discretisation is encapsulated in the DDt object. The numerics of the spatial discretisation are encapsulated in the solver. They communicate through symbolic expressions.
Each solver type has a sensible default. Advection-diffusion and Stokes default to Semi-Lagrangian. Pure diffusion defaults to Eulerian. Viscoelastic solvers use the DFDt infrastructure for stress history on particles. You only need to override the default when your problem requires it.
In UW2, if you wanted to change from explicit particle advection to a semi-Lagrangian scheme, you would need to plug in a different solver. The time discretisation was part of the solver's implementation, not a separable component. When history terms appeared through the constitutive model, we had to develop a matrix of all potential interactions and code them independently.
In UW3, the time derivative is an object you can create, configure, inspect, and swap. The BDF coefficients are visible as symbolic expressions. The history terms are mesh variables you can plot. The AM flux weighting is a symbolic combination you can display in a notebook.
This is the same design principle we described in the constitutive models post: separate the physics from the numerics, connect them through symbolic expressions, and make both sides inspectable. For constitutive models, the boundary is the stress tensor. For time derivatives, it is the BDF/AM expression. In both cases, the solver sees a SymPy expression and does not need to know how it was constructed.
","doi":"https://doi.org/10.59350/2bbxk-1gv97","funding_references":null,"guid":"69dfb91eeb7226563af54429","id":"3bd33871-0c4a-4c00-84ce-34ea4a24bd3b","image":"https://images.unsplash.com/photo-1501139083538-0139583c060f?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDF8fHRpbWV8ZW58MHx8fHwxNzc2MjY5Njk5fDA&ixlib=rb-4.1.0&q=80&w=2000","images":[],"indexed":true,"indexed_at":1776321304,"language":"en","parent_doi":null,"published_at":1776270000,"reference":[],"registered_at":0,"relationships":[],"rid":"yjgtg-ke420","status":"active","summary":"A symbolic time derivatives: you can inspect it, display it in a notebook, and verify that the time discretisation is doing what you expect. And you can swap between Lagrangian, Semi-Lagrangian, and Eulerian approaches without rewriting the solver. Sympy introduces incredible flexibility for on-the-fly composition of time-dependent problems. Many geodynamics equations involve a material derivative.","tags":["Underworld Code","Tricks Of The Trade","Documentation","Development"],"title":"Symbolic Time Derivatives in Underworld3","updated_at":1776270000,"url":"https://www.underworldcode.org/symbolic-time-derivatives-in-underworld3/","version":"v1"}],"out_of":49966,"page":1,"per_page":10,"total-results":49966}