These data were generated for an investigation of research data repository (RDR) mentions in biuomedical research articles.

Supplementary Table 1 is a discrete subset of SciCrunch RDRs used to study RDR mentions in biomedical literature. We generated this list by starting with the top 1000 entries in the SciCrunch database, measured by citations, removed entries for organizations (such as universities without a corresponding RDR) or non-relevant tools (such as reference managers), updated links, and consolidated duplicates resulting from RDR mergers and name variations. The resulting list of 737 RDRs is shown in with as a base based on a source list of RDRs in the SciCrunch database. The file includes the Research Resource Identifier (RRID), the RDR name, and a link to the RDR record in the SciCrunch database.

Supplementary Table 2 shows the RDRs, associated journals, and article-mention pairs (records) with text snippets extracted from mined Methods text in 2020 PubMed articles. The dataset has 4 components. The first shows the list of repositories with RDR mentions, and includes the Research Resource Identifier (RRID), the RDR name, the number of articles that mention the RDR, and a link to the record in the SciCrunch database. The second shows the list of journals in the study set with at least 1 RDR mention, andincludes the Journal ID, nam, ESSN/ISSN, the total count of publications in 2020, the number of articles that had text available to mine, the number of article-mention pairs (records), number of articles with RDR mentions, the number of unique RDRs mentioned, % of articles with minable text. The third shows the top 200 journals by RDR mention, normalized by the proportion of articles with available text to mine, with the same metadata as the second table. The fourth shows text snippets for each RDR mention, and includes the RRID, RDR name, PubMedID (PMID), DOI, article publication date, journal name, journal ID, ESSN/ISSN, article title, and snippet.

Data from survey's conducted as part of PLOS' project on incentivising best practice for data sharing. Surveys were run to assess the impact of two solutions that were being tested - the integration of the Dryad repository with the PLOS Pathogens submission system and the possible addition of an Accessible Data icon to articles in any PLOS journal using either Dryad, Figshare or OSF repositories. Submitting authors were emailed a survey shortly after completing their submission. This dataset contains the following files: 1) S1_DryadIntegration_Public.xlsx Results from the survey sent to PLOS Pathogens submitting authors about the Dryad integration. 2) Dryad Integration Survey Instrument.pdf Survey questions sent to PLOS Pathogens submitting authors. 3) S2_AccessibleDataLinks_Public.xlsx Results from the survey sent to submitting authors at PLOS Biology, PLOS Computational Biology, PLOS Genetics, PLOS Medicine, PLOS Neglected Tropical Diseases, PLOS ONE and PLOS Pathogens about the Accessible Data feature. 4) Accessible Data Survey Instrument.pdf Survey questions sent to PLOS Biology, PLOS Computational Biology, PLOS Genetics, PLOS Medicine, PLOS Neglected Tropical Diseases, PLOS ONE and PLOS Pathogens submitting authors. Note, PLOS Pathogens authors were asked to complete one survey, which comprised of both the Dryad integration and Accessible Data questions. Free text answers have been removed for the survey data for anonymisation purposes. The question on country has also been adjusted to show the author's region.

What do the alt-metrics of figshare items tell us? This dataset lists Altmetric data for the top 100 figshare repository items, categorised by type (retrieved on 9 March 2013). The data appear in an Interactions post on the Altmetric blog.

Open archive repositories have been assisting the researcher in getting the access to past research through archival repositories. Accesses to the open archive repositories are available freely on the Internet. The E-LIS open archive repository formulates LIS research more visible and accessible and increases its status and value. Here the researcher has tried to analyze an impact of E-LIS open archive digital repository of library and information science in the aspects of the annual growth of E-LIS publication index, primary subject coverage, types of documents indexing, country wise publication, and top downloads by country and highest downloaded articles, etc. The researcher also studied the top ten prolific authors, various file formats, internet browser and search engines used to access the E-LIS repository. This paper also discussed the author copyright policy, data preservation policy of repository. E-LIS is rapidly growing, and as on March 2018, there were over 20,000 full-text documents added from 124 countries around the world.The Researchers have undertaken the case study method to analyze the impact of E-LIS open access repository. The relevant data and statistics are collected from the E-LIS official portal. The researchers systematically analyzed E-LIS repository by year-wise publications, subject coverage, country contributions, prolific authors, coverage, document type, formats, language coverage, most downloaded articles, most accessed countries, etc. Analyzed data is based on the data retrieved as of 29th March 2018 from E-LIS official website, i.e., http://eprints.rclis.org.

This table includes repositories with at least 10 shared datasets on the site.

Usage data collected as part of PLOS' project on incentivising best practice for data sharing. The dataset contains usage data related to two experiments - the integration of the Dryad repository into a manuscript submission system and the introduction of the Accessible Data icon on PLOS articles which shared data in a repository.This dataset contains the following files:1) Dryad-usage.xlsxThis details the usage of the integration of the Dryad repository with the submission system of PLOS Pathogens during the study period (5th October 2021 to 4th October 2022). The data has been compiled from both Dryad systems and PLOS systems.2) Accessible-data-clicksThis details the interactions with the Accessible Data icon on PLOS article html pages during the study period (1st April 2022 (or date of publication for articles not published on this date) and the 31st of March 2023). The number of clicks on the icon is given alongside the number of article views. This data has been compiled from PLOS systems.3) Figshare-views-and-downloadsThis details the number of views and downloads received per month for datasets which are associated with a PLOS article featuring the Accessible Data icon. Data is given for the study period (April 2022 to March 2023) and for the preceding 12 months. This data has been compiled from Figshare systems and bot activity has been removed.AcknowledgementsOur thanks go to Dryad and Figshare for sharing this data with us and granting us permission to share it openly.

This data release contains the ten thousand neuron recordings used in Stringer, Pachitariu et al, 2018a. The code to make the figures in the paper will be available at https://github.com/MouseLand/stringer-pachitariu-et-al-2018aWe encourage data users to fork this repository, or create their own repository inside MouseLand, where we will also be adding our future data and analyses. "Watching" the repository might be a good idea, since any new information about the data, analyses of the data, or publications using it, will appear there. Some potential projects to do with this data:1) peer prediction: how well can you predict a neuron from the other 10,000? can you beat our score?2) face prediction: how well can you predict a neuron from the behavioral patterns on the face videos? 3) manifold discovery: can you find a nonlinear low-dimensional embedding? how low can it go? If you use these data in a paper, please cite the original research paper, as well as this dataset using the figshare doi.

This is a CSV file containing a listing of the top 60 articles published in the Journal of Digital Scholarship in the Humanities (JDSH, preciously LLC), as exported from the Altmetric Explorer tool on11 April 2017. The sheet has been manually annotated adding columns to indicate each article entry's corresponding License (column E) and Access Type (column F). License and Access Type data was crosschecked manually by accessing each article online individually. The file also contains data obtained from the Open Access Button API. The article DOIs as obtained from the Altmetric Explorer were run through the Open Access Button API on 15 May 2017 in order to discover if any of the published articles had open versions available. Any resulting links when available, were added to column O. Columns O and P also include additional information, when available, about the type of content available via the Open Access Button. Joe McArthur from the Open Access Button ran the first initial search for open surrogates of this dataset through the Open Access Button API. Ernesto Priego then manually crosschecked each entry and limited the final dataset to the top 60 articles (of 82). Please note that the Altmetric data for the JDSH is likely to have changed by now, though not too significantly. Altmetric scores have not been included in this file but the order of the entries correspond to the order in the data initially exported from the Altmetric Explorer (from most mentions to fiewer mentions, with a minimum of 1 mention). This dataset is part of the author and collaborator's ongoing research on open access and institutional repository uptake in the digital humanities. The data included in this file allows users to quickly quantify the number of JDSH articles published with open licenses, number of currently 'free', paywalled or open access articles. The data shared here also allows users to see which of the articles and/or their metadata (according to the Open Access Button API) have been deposited in institutional repositories. The data presented is the result of the specific methods employed to obtain the data. In this sense this data represents as much a testing of the technologies employed as of the actual articles' licensing and open availability. This means that data in columns L-P reflect the data available through the Open Access Button API at the moment of collection. It is perfectly possible that 'open surrogates' of the articles listed are available elsewhere through other methods. As indicated above data in columns E-F was obtained and added manually. Article DOI's were accessed manually from a computer browser outside/without access to university library networks, as the intention was to verify if any of the articles were available to the general public without university library network/subscription credentials.This deposit is part of a work in progress and is shared openly to document ongoing work and to encourage further discussion and analyses.

The report reviews existing standards, best practices, and governance requirements needed to establish and run trusted repositories that house health research data. The objectives of the report are to identify existing standards related to data repositories; to assess the standards that are currently in place in selected repositories that house health data; to conduct a gap analysis of governance standards used in existing repositories.Updated to version 3 on 11th May 2018:The first version of Appendix 1 had the word ‘confidential’ on the cover page which had been left in in error during the editing process. This has now been taken out. Updated to version 2 on 8th May 2018: The document was previously called ‘Development of International Standards for Online Repositories’ but has been changed to ‘Development of Standards for Online Repositories’ as it better reflects the document and its scope. The first version of the document had the word ‘confidential’ on the cover page which had been left in in error during the editing process. This has now been taken out. Finally, Appendix 1 has been added as it is referred to in the document but was not available.

The OpenDengue project aims to build and maintain a database of dengue case counts for every dengue-affected country worldwide since 1990 or earlier. We collate data from a range of publicly available sources including ministry of health websites, peer-reviewed publications and other disease databases.The complete details, methodologies, and findings associated with this dataset are available in our research paper (https://doi.org/10.1038/s41597-024-03120-7).The latest database is available on our OpenDengue website (https://opendengue.org/data.html). Past and current versions are also available in the Opendengue Github repository (https://github.com/OpenDengue/master-repo).File descriptorGlobal summaries of data: We provide three global summaries of the data in OpenDengue. We provide the best national estimate (National_extract.csv), the best temporal resolution (Temporal_extract.csv) and the best spatial resolution (Spatial_extract.csv). This allows users to customise their data extraction based on their research question. Each row in the data table contains a unique, non-overlapping location and time period with the associated dengue case data. The below codebook describes each variable.adm_0_name : administrative level 0/country nameadm_1_name : administrative level 1 nameadm_2_name : administrative level 2 namefull_name : full place nameISO_A0 : ISO country codeFAO_GAUL_code : Food and Agricultural Organization Global Administrative Unit Layer CodeRNE_iso_code : RNaturalEarth ISO codeIBGE_code : Brazilian Instituto Brasileiro de Geografia e Estatística (IBGE) codecalendar_start_date : the start date in calendar time with the format YYYY-mm-ddcalendar_end_date : the end date in calendar time with the format YYYY-mm-ddYear : yeardengue_total : the total dengue case count relating to the period and place (see dengue classification protocol and dengue double count protocol)case_definition_standardised : case definition after standardisationS_res : spatial resolutionT_res : temporal resolutionUUID : Universal Unique Identifier relating to the source file from which the data originates

To determine where data is shared and what data is no longer available, this study analyzed data shared by researchers at a single university. 2166 supplemental data links were harvested from the university’s institutional repository and web scraped using R. All links that failed to scrape or could not be tested algorithmically were tested for availability by hand. Trends in data availability by link type, age of publication, and data source were examined for patterns. Results show that researchers shared data in hundreds of places. About two-thirds of links to shared data were in the form of URLs and one-third were DOIs, with several FTP links and links directly to files. A surprising 13.4% of shared URL links pointed to a website homepage rather than a specific record on a website. After testing, 5.4% the 2166 supplemental data links were found to be no longer available. DOIs were the type of shared link that was least likely to disappear with a 1.7% loss, with URL loss at 5.9% averaged over time. Links from older publications were more likely to be unavailable, with a data disappearance rate estimated at 2.6% per year, as well as links to data hosted on journal websites. The results support best practice guidance to share data in a data repository using a permanent identifier.

An introduction to research data tools

During the transition to increased research from home, here is a 3-minute introduction into the most useful tools to store, synchronise, organise and share your data.

For a more detailed summary, see:

Research Data Management at La Trobe.

https://doi.org/10.26181/5e5c41c9f1dde

Transcript

With the increased relevance of working remotely, let's look at some tools you can use to organise, synchronise and share your data. All of these work without using the university VPN.

Storage

First on the list, CloudStor allows you to securely synchronise files and folders across a team. It's easiest to find via a web search and once you're on the Australian Academic and Research Network site, you can sign up to CloudStor using your university login. Once there, you can drag and drop any file or folder onto the web page. You start off with 1TB of storage, but you can request more from ICT if you need to. To synch these files and folders across any number of computers or other devices, you simply need the appropriate app. It then just appears like any other folder on your computer. You can securely share files and folders with a range of customisable options, and you can let other people send you files in a similar manner.

Notebook

So with your working storage sorted, what replaces the trusty paper lab notebook? LabArchives is a general purpose online research notebook. Again, just use your university login. You can create new project notebooks organised on an existing template, or create your own. These project notebooks are organised into folders which contain pages, and those pages are just like the page of a paper notebook where you can organise your thoughts, hypotheses, observations and experiments, and the conclusions that you draw from them. You can also embed files straight into the notebook, along with a number of other features.

Sharing

So you've put all this work into your research. What's the best way to share it? Well, the La Trobe open repository is based on FigShare, and once again, you can use your university login. When you create a new item, you just drag and drop in a file or folder and add some simple descriptive information so that people are able to find it. Items can include your publications, research data, publication slides or recordings, and more or less any other research output. Describe it as thoroughly as possible, because this makes it more searchable and therefore more visible. For supplementary data or an article postprint, link to the DOI of the published version. Choose a license for it to be released under. We recommend CC BY but there's a range to choose from. If required by a publisher you can apply an embargo. When you're done, you can either save a private draft, or share it publicly. Public items appear on the repository main page and are searchable and are also assigned a DOI. For items that aren't published elsewhere, like negative results or teaching material, this allws people to cite them and increases their visibility.

For all things research data, check out our data ready guide, and join us for the digital drop-ins, first Tuesday of every month.

The categories are sorted by the frequency of their occurrence determined in the question analysis. The asterisk denotes the categories with an agreement less than 0.4.

Top sources of science news stories (those above 2% of sample)

Ending COVID-19 pandemic requires a collaborative understanding of SARS-CoV-2 and COVID-19 mechanisms. Yet, the evolving nature of coronaviruses results in a continuous emergence of new variants of the virus. Central to this is the need for a continuous monitoring system able to detect potentially harmful variants of the virus in real-time. In this manuscript, we present the International Database of SARS-CoV-2 Variations (IDbSV), the result of ongoing efforts in curating, analyzing, and sharing comprehensive interpretation of SARS-CoV-2's genetic variations and variants. Through user-friendly interactive data visualizations, we aim to provide a novel surveillance tool to the scientific and public health communities. The database is regularly updated with new records through a 4-step workflow (1—Quality control of curated sequences, 2—Call of variations, 3—Functional annotation, and 4—Metadata association). To the best of our knowledge, IDbSV provides access to the largest repository of SARS-CoV-2 variations and the largest analysis of SARS-CoV-2 genomes with over 60 thousand annotated variations curated from the 1,808,613 genomes alongside their functional annotations, first known appearance, and associated genetic lineages, enabling a robust interpretation tool for SARS-CoV-2 variations to help understanding SARS-CoV-2 dynamics across the world.

Part 3 of 3 folders of building satellite images. This data item consists of top-down satellite building views extracted from Mapbox Satellite Imagery. Mapbox offers a comprehensive global raster tileset, which includes high-resolution satellite and aerial imagery.Images are sourced from various providers, including NASA, USGS, Maxar, and Nearmaps, as described in their documentation: https://docs.mapbox.com/help/glossary/mapbox-satellite/. The original tiles are obtained with Zoom level 19. The code to extract building specific top-down views are provided in the accompanying repository.

Top themes mentioned as the most “value-add” curation action, analyzed by repository type.

This paper presents a large-scale document-level comparison of two major bibliographic data sources: Scopus and Dimensions. The focus is on the differences in their coverage of documents at two levels of aggregation: by country and by institution. The main goal is to analyze whether Dimensions offers as good new opportunities for bibliometric analysis at the country and institutional levels as it does at the global level. Differences in the completeness and accuracy of citation links are also studied. The results allow a profile of Dimensions to be drawn in terms of its coverage by country and institution. Dimensions’ coverage is more than 25% greater than Scopus which is consistent with previous studies. However, the main finding of this study is the lack of affiliation data in a large fraction of Dimensions documents. We found that close to half of all documents in Dimensions are not associated with any country of affiliation while the proportion of documents without this data in Scopus is much lower. This situation mainly affects the possibilities that Dimensions can offer as instruments for carrying out bibliometric analyses at the country and institutional level. Both of these aspects are highly pragmatic considerations for information retrieval and the design of policies for the use of scientific databases in research evaluation.

Number of studies, (research sites) and [sources of data] included in the analyses, with best-fitting probability distributions.

This repository contains data from the Twitterstorm that occurred from June 11-13 2017 following a controversial tweet by scientist and public figure Katie Hinde (@Mammals_Suck on Twitter). This is a good dataset for practicing the analysis of text and/or social network data in R. 1. tweets_raw.Rds contains raw text data for all 33261 tweets mentioning @Mammals_Suck in the 36 hours following the original tweet. This file can be read into R using the 'readRDS' function.2. tweets_clean.csv contains clean data on all 4843 quote & reply tweets responding to the original tweet over a 36 hour period, including: tweet text, user name, tweet time, # of favorites, # of retweets, # of friends of the user, # of followers of the user, user self-description, user location, type (quote or reply).3. social_network.rds contains a social network for users in the twitterstorm an an 'igraph' object. Vertex names correspond to Twitter users, and edge weights are based on co-followers (i.e., the number of mutually followed accounts, which is a proxy for overlap in the interests of two users). Additional vertex attributes can be added to the graph using information about users from the 'tweets_clean.csv' file, such as the time they entered the twitterstorm, their geographic location, or the text content of their tweets. This file can be read into R using the 'readRDS' function. For more information, check out the blog posts written by myself and Katie Hinde. Mine focuses on data analysis, while hers focuses on her experience and understanding of the events. My blog post: https://rgriff23.github.io/2017/06/29/Katie-Hinde-Twitterstorm.html Katie Hinde's blog post: https://mammalssuck.blogspot.co.uk/2017/06/portrait-of-unexpected-twitter-storm.html The R code I used to compile and analyze this data can be found in this GitHub repository: https://github.com/rgriff23/Katie_Hinde_Twitter_storm_text_analysisNote that the data in the GitHub repo does not match the data included in this figshare repo exactly. This is because the data provided here has been reduced to information collected from Twitter: I eliminated data columns that were produced using subsequent analysis, such as tweet classifications based on sentiment analysis or social network analysis.