Abstract Data‐ and code‐archiving are important components of open science, as both make research more transparent, reproducible, accountable and credible, allowing future researchers to build on previous work. Despite progress in implementing data‐ and code‐archiving policies in journals publishing ecology and evolution research, issues remain. To be more useful to future researchers, archived data and code must not only be archived but also meet good practice standards. We collected data from 1861 papers published between 2017 and 2024 in the seven British Ecological Society (BES) journals, during a hackathon event. We systematically checked associated data and/or code, metadata, help files and annotations to assess archiving practices. We determined if and where data and code files were archived, whether they could be located, downloaded and opened, and whether they had associated READMEs, digital object identifiers (DOI) and licences. We also recorded the file extensions used to save data/code files, and which programming languages code was written in. 93% of the 1861 papers we examined used data and ~90% used code. While 97% of the 1735 papers that used data also archived it, only 35% of the 1670 papers that used code also archived code. Over 85% of archived data and code could be located, downloaded and opened. Reusability, however, was more limited; around a third of papers did not have a README or similar to explain their data/code files, and the quality of READMEs varied substantially. We recommend that researchers archive their code and that archived code be explicitly mentioned in the Data (or Code) Availability statement. We also encourage researchers to provide more accessible and informative READMEs for data and code. To help achieve these recommendations, we advocate that journals employ Data/Code editors to review data and code quality, research institutions deliver more training in open science practices, and funding bodies set clear expectations on open data and code practices.
Citizen science in environmental and ecological sciences
Citizen science is an increasingly acknowledged approach applied in many scientific domains, and particularly within the environmental and ecological sciences, in which non-professional participants contribute to data collection to advance scientific research. We present contributory citizen science as a valuable method to scientists and practitioners within the environmental and ecological sciences, focusing on the full life cycle of citizen science practice, from design to implementation, evaluation and data management. We highlight key issues in citizen science and how to address them, such as participant engagement and retention, data quality assurance and bias correction, as well as ethical considerations regarding data sharing. We also provide a range of examples to illustrate the diversity of applications, from biodiversity research and land cover assessment to forest health monitoring and marine pollution. The aspects of reproducibility and data sharing are considered, placing citizen science within an encompassing open science perspective. Finally, we discuss its limitations and challenges and present an outlook for the application of citizen science in multiple science domains.
Knowledge infrastructures for the Anthropocene
The technosphere metabolizes not only energy and materials, but information and knowledge as well. This article first examines the history of knowledge about large-scale, long-term, anthropogenic environmental change. In the 19th and 20th centuries, major systems were built for monitoring both the environment and human activity of all kinds, for modeling geophysical processes such as climate change, and for preserving and refining scientific memory, i.e. data about the planetary past. Despite many failures, these knowledge infrastructures also helped achieve notable successes such as the Limited Test Ban Treaty of 1963, the ozone depletion accords of the 1980s, and the Paris Agreement on climate change of 2015. The article’s second part proposes that knowledge infrastructures for the Anthropocene might not only monitor and model the technosphere’s metabolism of energy, materials and information, but also integrate those techniques with new accounting practices aimed at sustainability. Scientific examples include remarkable recent work on long-term socio-ecological research, and the assessment reports of the Intergovernmental Panel on Climate Change. In terms of practical knowledge, one key to effective accounting may be ‘recycling’ of the vast amounts of ‘waste’ data created by virtually all online systems today. Examples include dramatic environmental efficiency gains by Ikea and United Parcel Service, through improved logistics, self-provision of renewable energy, and feedback from close monitoring of delivery trucks. Blending social ‘data exhaust’ with physical and environmental information, an environmentally focused logistics might trim away excess energy and materials in production, find new ways to re-use or recycle waste, and generate new ideas for eliminating toxic byproducts, greenhouse gas emissions and other metabolites.
Knowledge infrastructures for the Anthropocene
The technosphere metabolizes not only energy and materials, but information and knowledge as well. This article first examines the history of knowledge about large-scale, long-term, anthropogenic environmental change. In the 19th and 20th centuries, major systems were built for monitoring both the environment and human activity of all kinds, for modeling geophysical processes such as climate change, and for preserving and refining scientific memory, i.e. data about the planetary past. Despite many failures, these knowledge infrastructures also helped achieve notable successes such as the Limited Test Ban Treaty of 1963, the ozone depletion accords of the 1980s, and the Paris Agreement on climate change of 2015. The article’s second part proposes that knowledge infrastructures for the Anthropocene might not only monitor and model the technosphere’s metabolism of energy, materials and information, but also integrate those techniques with new accounting practices aimed at sustainability. Scientific examples include remarkable recent work on long-term socio-ecological research, and the assessment reports of the Intergovernmental Panel on Climate Change. In terms of practical knowledge, one key to effective accounting may be ‘recycling’ of the vast amounts of ‘waste’ data created by virtually all online systems today. Examples include dramatic environmental efficiency gains by Ikea and United Parcel Service, through improved logistics, self-provision of renewable energy, and feedback from close monitoring of delivery trucks. Blending social ‘data exhaust’ with physical and environmental information, an environmentally focused logistics might trim away excess energy and materials in production, find new ways to re-use or recycle waste, and generate new ideas for eliminating toxic byproducts, greenhouse gas emissions and other metabolites.
Crossref: The sustainable source of community-owned scholarly metadata
This paper describes the scholarly metadata collected and made available by Crossref, as well as its importance in the scholarly research ecosystem. Containing over 106 million records and expanding at an average rate of 11% a year, Crossref’s metadata has become one of the major sources of scholarly data for publishers, authors, librarians, funders, and researchers. The metadata set consists of 13 content types, including not only traditional types, such as journals and conference papers, but also data sets, reports, preprints, peer reviews, and grants. The metadata is not limited to basic publication metadata, but can also include abstracts and links to full text, funding and license information, citation links, and the information about corrections, updates, retractions, etc. This scale and breadth make Crossref a valuable source for research in scientometrics, including measuring the growth and impact of science and understanding new trends in scholarly communications. The metadata is available through a number of APIs, including REST API and OAI-PMH. In this paper, we describe the kind of metadata that Crossref provides and how it is collected and curated. We also look at Crossref’s role in the research ecosystem and trends in metadata curation over the years, including the evolution of its citation data provision. We summarize the research used in Crossref’s metadata and describe plans that will improve metadata quality and retrieval in the future.
Manuscript submission systems and metadata completeness in Crossref: Patterns and associations
The importance of open research information, particularly publication metadata, is widely recognised. Crossref is one of the most important infrastructures for registering open metadata as part of DOI record registration. It is widely known, however, that the metadata of many publications is far from complete, with many publishers making certain metadata openly available, but failing to do so for other metadata elements. Publishers’ ability to register this metadata with Crossref depends on their capacity to capture and retain this data in their production workflows. Manuscript submission systems are an important, yet largely overlooked, factor in the extent to which publishers make metadata available through Crossref. In this paper, we present the results of an analysis investigating the relation between the level of metadata that publishers deposit with Crossref and the submission systems that they deploy for their journals. We have looked at the 153 publishers with the largest amounts of publications in Crossref and concentrate on the four most commonly used systems: Editorial Manager, ScholarOne, Open Journal Systems (OJS) and eJournalPress. We show that some submission systems appear better suited to capturing certain metadata elements. However, there are always cases where publishers using the same system differ widely in the level of metadata they register, suggesting that technology is not the only prohibiting factor and other considerations are at play.
Ten quick tips to SNIFF out sustainable and secure scientific software
Modern computational biology depends heavily on open-source software tools, analysis pipelines, and containerized workflows developed and shared by the research community. While there is extensive guidance (including Quick Tips and Simple Rules articles) on how to build robust and sustainable scientific software, far less has been written for researchers in the role of software users evaluating whether an existing tool is reliable, secure, and sustainable enough for their work. Here we present ten quick tips to help researchers critically assess the tools they adopt. Our tips are organized around a framework that centers on key evaluation features: source, network, interaction, fit, and fragility (SNIFF). These dimensions prompt researchers to consider who maintains a tool and why, whether it is embedded in a broader ecosystem, how actively its developers and users engage, whether it matches the intended use case and licensing requirements, and how robust its dependencies and security practices are. By applying these tips, researchers can make more informed decisions, reduce the risk of relying on abandoned or insecure software, and contribute to a more sustainable scientific software ecosystem.
Linking the world's research to the code it runs on - OpenAlex blog
Research relies on software. Software written by scientists, for science, runs through the entire modern research stack: NumPy and SciPy, R and ggplot2, Jupyter, BLAST, ImageJ, AlphaFold. Yet in the scholarly record, that software is nearly invisible. Software is not usually cited formally in publications and is usually just mentioned in the text, which means […]
State of Conservation Technology | WILDLABS
Explore insights from the first community-sourced assessment of the State of Conservation Technology
Incorporating Archives
Ready to make it official? Here you’ll find guides, samples and examples to make the process a bit easier.
Scientific production in the era of Large Language Models
Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft manuscripts demonstrate a large increase in paper production, ranging from 23.7-89.3% depending on scientific field and author background, 2) LLM use has reversed the relationship between writing complexity and paper quality, leading to an influx of manuscripts that are linguistically complex but substantively underwhelming, and 3) LLM adopters access and cite more diverse prior work, including books and younger, less-cited documents. These findings highlight a stunning shift in scientific production that will likely require a change in how journals, funding agencies, and tenure committees evaluate scientific works.
COS goes FOSS The sorry state of scientific publishing and how we could move to an open and resilient infrastructure
“The data files remains our property and are not deposited for free access.”
The Micro-Paper: Towards cheaper, citable research ideas and conversations
Academic, peer-reviewed short papers are a common way to present a late-breaking work to the academic community that outlines preliminary findings, research ideas, and novel conversations. By comparison, blogging or writing posts on social media are an unstructured and open way to discuss ideas and start new conversations. Both have limitations in the proliferation of research ideas. The short paper format relies on the conference and journal submission process while blogging does not operate within a structured format or set of expectations at all. However, at times the demand exists for late-breaking ideas and conversations to arise in a raw form or with urgency but should still be archived and recorded in a way that promotes citational honesty and integrity. To address this, I present: The Micro-Paper, as a micro-paper itself. The Micro-Paper is a small, cheap, accessible, digital document that is self-published and archived, akin to a pre-print of a short paper. This meta micro-paper discusses the context, goals, and considerations of micro-paper authoring.
Resilient Data Futures — Discourse Graph
A living, content-addressed, contributable form of the SciOS Resilient Data Futures whitepaper.
Resilient Data Futures — Discourse Graph
A living, content-addressed, contributable form of the SciOS Resilient Data Futures whitepaper.
Resilient Data Futures — Discourse Graph
A living, content-addressed, contributable form of the SciOS Resilient Data Futures whitepaper.
Back into blogging and just published something I've been thinking about for a while now – making archival content more resilient and discoverable on atproto. Oral history, interactive transcripts, content addressing, and keeping important stories from being quietly erased. maboa.it/resilient-archives-on-the-at-…
Keeping Archives Alive: Resilience and Discovery on ATProto
maboa.it