Abstract: A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 274 sources, for 170 species, from 39 families, and 12 orders related to length (n?=?9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.
Abstract: Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in e.g. instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards, since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.
“Open Research, or Open Science is the movement to make the scientific process and research outputs more transparent, inclusive and accessible.
It supports validation, reproducibility and reduces cases of academic misconduct.
It helps to maximise the impact of one’s research and provides the foundations for others to build upon.
Held in conjunction with the International Open Access Week, the inaugural Singapore Open Research Conference, “Accelerating Research with Responsible Open Science”, will provide a great opportunity to interact with drivers and practitioners about their experiences and suggestions on Open Science/Open Research.
Though the discussions will be based on the bioscience field, researchers from various institutions are most welcome….”
Abstract: The Harvard Medical School Countway Library’s Massive Open Online Course (MOOC) Best Practices for Biomedical Research Data Management launched on Canvas in January 2018. This report analyzes learner reported data and course generated analytics from March 2020 through June 2021 for the course. This analysis focuses on three subsets of participant data during the pandemic to understand global learner demographics and interest in biomedical research data management.
“Species 2000/Catalogue of Life (COL), in collaboration with the Global Biodiversity Information Facility (GBIF) secretariat, is seeking a contractor to assemble a semi-automated part of the COL Checklist.
The COL Checklist itself is underpinned by global species databases maintained by taxonomists and experts and ssembled by an editorial team. The addition of a semi-automated part will make the COL Checklist more comprehensive by including extended information and enriched data. It will improve taxonomic coverage and usefulness of the COL Checklist also in delivering taxonomic services for GBIF-mediated occurrences….
In this role, the contractor will work with both the COL and GBIF secretariats as well as the advisory bodies of the COL governance, such as the COL taxonomic working group and the COL Global Team. This is an exciting opportunity to work with global open data infrastructures, a team of dedicated experts and the global taxonomic community to build a global resource with impact….”
Abstract: Metagenomics is a culture-independent method for studying the microbes inhabiting a particular environment. Comparing the composition of samples (functionally/taxonomically), either from a longitudinal study or cross-sectional studies, can provide clues into how the microbiota has adapted to the environment. However, a recurring challenge, especially when comparing results between independent studies, is that key metadata about the sample and molecular methods used to extract and sequence the genetic material are often missing from sequence records, making it difficult to account for confounding factors. Nevertheless, these missing metadata may be found in the narrative of publications describing the research. Here, we describe a machine learning framework that automatically extracts essential metadata for a wide range of metagenomics studies from the literature contained in Europe PMC. This framework has enabled the extraction of metadata from 114,099 publications in Europe PMC, including 19,900 publications describing metagenomics studies in European Nucleotide Archive (ENA) and MGnify. Using this framework, a new metagenomics annotations pipeline was developed and integrated into Europe PMC to regularly enrich up-to-date ENA and MGnify metagenomics studies with metadata extracted from research articles. These metadata are now available for researchers to explore and retrieve in the MGnify and Europe PMC websites, as well as Europe PMC annotations API.
What are the most influential articles in reproductive biology journals from 1980 to 2019 according to Altmetric Attention Score (AAS), number of citations and Relative Citation Ratio (RCR)?
Cross-sectional study of reproductive biology articles indexed in the National Institutes of Health Open Citation Collection from 1980 to 2019. Data were downloaded on 20 May 2021. The 100 articles with highest AAS, RCR and number of citations were analysed.
Twenty-one reproductive biology journals were identified, including 120,069 articles published from 1980 to 2019. In total 227 reproductive biology classics were identified due to some overlap between the three lists. Compared with the 100 articles with the highest AAS (after excluding articles featured on both lists), the 100 top-cited articles were older (2014 versus 2001, mean difference [95% confidence interval] 13.5 [11.5, 15.5]), less likely to be open access (64% versus 85%), more likely to be reviews (42% versus 12%) and less likely to be observational studies (9% versus 51%) and randomized clinical trials (0% versus 5%). These same trends were observed in analyses comparing the 100 articles with highest AAS to the 100 articles with highest RCR. The most common topic was assisted reproduction, but prominent topics included infertility for top AAS articles, reproductive technology in animals for top-cited articles, and polycystic ovary syndrome for top RCR articles.
Formerly, influential articles in reproductive biology journals were evaluated by absolute citation rates and subject to limitations of conventional bibliometric analysis. This is the first comprehensive study to use altmetrics and citation-based metrics to identify reproductive biology classics.
“Through our crowd preprint review activities we seek to draw on the collective input of a group of commenters who each can comment on the preprint according to their level of expertise and interest. We are midway through our activities for 2022 and we wanted to share an update on our progress.
What have we accomplished so far?
We had a great response from the community with over 120 crowd reviewers signed up so far, with strong representation of early career researchers. We have three groups which complete reviews of preprints in each of the disciplines below:
Cell biology – a crowd of 70 members reviews preprints posted on bioRxiv
Biochemistry – a crowd of 35 researchers reviews preprints from bioRxiv
Infectious diseases preprints in Portuguese – a crowd of 30 researchers provide reviews in Portuguese for preprints posted in SciELO Preprints
For each of the groups, a group of ASAPbio Fellows and partners from SciELO Preprints are involved in selecting preprints to review and summarizing the comments received. They also provide regular feedback on aspects of the process that can be adjusted or improved.
We circulate a new preprint to each group every week and invite comments via a Google document. We have seen a great level of engagement from reviewers, and are particularly pleased to see the interactions among reviewers in the collaborative documents, where they provide comments and feedback to each other, not only about the preprints but also about queries that may arise during their review….”
“We are delighted to announce that the number of institutions participating in our cost-neutral Read & Publish Open Access (OA) initiative has more than doubled since June 2021, increasing from 253 to 522.
The number of countries represented has also risen by more than 50% over the last year – from 24 to 39. We have signed new agreements with library consortia in Australia, Spain, Sweden and the United States and, following a successful two-year pilot, we signed a three-year renewal agreement with Jisc in the UK.
The success of our Read & Publish initiative has contributed to a significant growth in the proportion of OA research content in our hybrid journals – Development, Journal of Cell Science and Journal of Experimental Biology.
The journals were the first in the world to be afforded Transformative Journal status by Plan S and all three exceeded their targets for OA growth in 2021.
Our hybrid journals are on track to meet their Transformative Journal targets in 2022 and this takes us closer to our goal of converting them to full OA.
Building on the success of the initiative, libraries can now also include our fully Open Access journals – Disease Models & Mechanisms and Biology Open – in their Read & Publish agreements….”
“The Company of Biologists has been committed to Open Access since 2004 because we believe that immediate and free availability of high-quality research helps us to achieve our mission of advancing excellence in the biological and biomedical sciences worldwide.
We were one of the first not-for-profit journal publishers to launch a cost-neutral Read & Publish initiative and over 500 institutions in 39 countries are participating.
We have agreements with library consortia in eight countries and an agreement with an international library organisation covering a further 30 countries.
We are delighted that the initiative is continuing to go from strength to strength and that the number of participating institutions more than doubled from June 2021 to June 2022….”
“Improving scientific publishing is often framed as an issue of openness and speed and less often as one of context. In this post, Ludo Waltman and Jessica Polka make the case for a more contextualised approach to open access publishing and preprinting, and introduce the Publish Your Reviews initiative. Launched today by ASAPbio, the initiative allows reviewers to provide richer contextual information to preprints by publishing peer reviews and linking them to the preprint versions of the articles under review….”
“From today, determining the 3D shape of almost any protein known to science will be as simple as typing in a Google search.
Researchers have used AlphaFold — the revolutionary artificial-intelligence (AI) network — to predict the structures of some 200 million proteins from 1 million species, covering nearly every known protein on the planet.
The data dump will be freely available on a database set up by DeepMind, Google’s London-based AI company that developed AlphaFold, and the European Molecular Biology Laboratory’s European Bioinformatics Institute (EMBL-EBI), an intergovernmental organization near Cambridge, UK….”
“It’s been one year since we released and open sourced AlphaFold and created the AlphaFold Protein Structure Database (AlphaFold DB) to freely share this scientific knowledge with the world. Proteins are the building blocks of life, they underpin every biological process in every living thing. And, because a protein’s shape is closely linked with its function, knowing a protein’s structure unlocks a greater understanding of what it does and how it works. We hoped this groundbreaking resource would help accelerate scientific research and discovery globally, and that other teams could learn from and build on the advances we made with AlphaFold to create further breakthroughs. That hope has become a reality far quicker than we had dared to dream. Just twelve months later, AlphaFold has been accessed by more than half a million researchers and used to accelerate progress on important real-world problems ranging from plastic pollution to antibiotic resistance.
Today, I’m incredibly excited to share the next stage of this journey. In partnership with EMBL’s European Bioinformatics Institute (EMBL-EBI), we’re now releasing predicted structures for nearly all catalogued proteins known to science, which will expand the AlphaFold DB by over 200x – from nearly 1 million structures to over 200 million structures – with the potential to dramatically increase our understanding of biology….
All 200+ million structures will also be available for bulk download via Google Cloud Public Datasets, making AlphaFold even more accessible to scientists around the world….”
“ZSL [Zoological Society of London], as a sub-grantee alongside Global Canopy, will be launching a revolutionary platform in 2022 bringing together the best data available on corporate exposure to, and reporting on, deforestation and other related environmental, social and governance (ESG) issues.
The project aims to provide market-leading data to help financial institutions identify risks and find opportunities for sustainable investments to meet the growing demand for responsible financial products in light of the biodiversity and climate crises.
The database will be underpinned by the data collected through ZSL’s SPOTT assessments, Global Canopy’s Forest 500 assessments and the Stockholm Environment Institute, Global Canopy and Neural Alpha’s Trase Supply Chains and Trase Finance data, and will be aligned with the Accountability Framework Initiative and its guidance.
Supported by a five-year grant from the Norwegian government, the resulting data and metrics will provide a more comprehensive view of company performance on deforestation, conversion and associated human rights risks. The dataset will also provide broader coverage of the most exposed forest risk supply chains (in particular: palm oil, soy, timber, pulp, rubber and cattle products) and geographies where corporate performance data on these topics is currently missing. By mapping and integrating data from aligned initiatives and external datasets, more complete and in-depth coverage of corporate performance data will be available….”