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5,061 results for “access”
Considerable gaps in our global knowledge of potential groundwater accessibility
<p>This data contains the model ensemble mean of water table depth in meter of four global steady-state groundwater models.<br>The spatial resolution is 5 arc-minutes (~9 by 9 km at the Equator). Models with higher resolution were aggregated to this resolution using the mean.<br>The Models do not contain any anthropogenic influences.<br>The uncertainty in water table depth (m) is based on the range (Max - Min) of the ensemble.</p><p>Robert Reinecke 2024</p>
National Open Access Monitor, Draft Report: Stakeholder Feedback: Response Dataset
<p>This dataset contains the response data from the 'National Open Access Monitor, Draft Report: Stakeholder Feedback' Form which was open from 16th to 30th November 2023 under the National Open Access Monitor Project. A PDF reference copy of the Feedback Form is available here: <a href="https://zenodo.org/doi/10.5281/zenodo.10141988">https://zenodo.org/doi/10.5281/zenodo.10141988</a></p><p>The purpose of the form was to capture stakeholder feedback on the National Open Access Monitor, Ireland Draft Report, for actioning by OpenAIRE in the final National Open Access Monitor Report to be delivered in January 2024. The draft is an interim report, and includes reference to the initial feedback from IReL and the National Open Access Monitor Project Advisory Group.</p><p><strong>To note: </strong></p><ul><li>Responses have been pseudonymised to the level of stakeholder-group e.g. Contributor I, Research Performing Organisation I, where requested by the participant in the participant consent form: <a href="https://doi.org/10.5281/zenodo.7589770">https://doi.org/10.5281/zenodo.7589770</a></li><li>This is the original raw data file, in csv format, as downloaded from the Online Surveys platform and subsequently pseudonymised.</li></ul><p>-----------------------</p><p>The context for the feedback form is detailed in the National Open Access Monitor Project Plan: <a href="https://doi.org/10.5281/zenodo.7331431">https://doi.org/10.5281/zenodo.7331431</a>, the National Open Access Monitor Advisory Group Meeting Minutes, 27th October 2023: <a href="https://zenodo.org/doi/10.5281/zenodo.10105023 ">https://zenodo.org/doi/10.5281/zenodo.10105023 </a>and the OpenAIRE National Open Access Monitor Ireland, Draft Report: <a href="https://zenodo.org/doi/10.5281/zenodo.10136295">https://zenodo.org/doi/10.5281/zenodo.10136295</a></p><p>This project is managed by <a href="http://www.irel.ie/">IReL </a>and has received funding from Ireland's National Open Research Forum under the NORF Open Research Fund. <a href="https://norf.ie/funding/">https://norf.ie/funding/ </a><a href="https://norf.ie/orf-projects-announcement/">https://norf.ie/orf-projects-announcement/</a></p>
Video 4 - Open Science: equitable access for everyone.
<p>An interview about the necessity of funding Open Science platforms as a means of achieving equitable science with Iryna Kuchma, Open Access Programme Manager for EIFL; Ana María Cetto, Professor of Physics at Universidad Nacional Autónoma de México; Yensi Flores, Postdoctoral researcher at the Cancer Research Centre, University College Cork; and <span>Bregt Saenen, Senior Policy Officer for Open Science at Science Europe</span>.<br><br></p> <p><span>Science is a global enterprise targeting global problems – limiting access to science defeats this purpose. Open Science platforms are necessary for researchers from all countries and organizations to be able to participate in the global scientific effort. Funding bodies can support Open Science platforms as a way of ensuring equitable and trustworthy science. </span></p> <p> </p>
ACCESS-AM2 Southern Ocean cloud and radiation data and code for SHAP analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) and SHAP analysis code and data used for the study described in Fiddes et al. (2024) '<em>A machine learning approach for evaluating Southern Ocean cloud-radiative biases over the Southern Ocean in a global atmosphere model</em>' accepted in Geoscientific Model Development</p> <p>Included files: </p> <p>- code.zip, inc: </p> <p> - pre-process_modis.ipynb: process the modis data, described in Fiddes et al. 2022 (https://doi.org/10.5194/acp-22-14603-2022)<br> - pre-process.ipynb: organises model and modis data for analysis. Produces the files: COSP_vars_MODIS_2015-2019.nc, COSP_vars_cg207_2015-2019.nc and COSP_vars_bx400_2015-2019.nc<br> - run_XGBoost+SHAP_control.ipynb: run the XGBoost model and SHAP analysis for the control run (bx400). Produces the files: SHAP_values_SWCRE_2015-2019_bx4002.nc, XGBoost_predicted_SWCRE_2015-2019_bx4002.nc, SHAP_interactions_bx400.nc<br> - run_XGBoost+SHAP_ice.ipynb: run the XGBoost model and SHAP analysis for the ice experiment run (cg207). Produces the files: SHAP_values_SWCRE_2015-2019_cg2072.nc, XGBoost_predicted_SWCRE_2015-2019_cg2072.nc<br> - analysis+plots_ML.ipynb: plots and stats presented in paper </p> <p>- COSP_vars_MODIS_2015-2019.nc</p> <p>- COSP_vars_cg207_2015-2019.nc</p> <p>- COSP_vars_bx400_2015-2019.nc</p> <p>- SHAP_values_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_values_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_cg2072.nc</p> <p>- XGBoost_predicted_SWCRE_2015-2019_bx4002.nc</p> <p>- SHAP_interaction_bx400.nc</p> <p>The cloud types used in this work can be found at https://doi.org/10.5281/zenodo.6004061 </p>
Data for publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020"
<p>Data to reproduce figures for the publication "Benefits of open access to researchers from lower-income countries: A global analysis of reference patterns in 1980–2020" (DOI: 10.1177/01655515241245952). Each file contains the data underlying the figure corresponding to the file name.</p>
Patristisches Textarchiv. Ein Open Access-Archiv antiker christlicher Texte
The "Patristic Text Archive" offers anyone interested a collection of texts and translations of Christian texts from antiquity (i.e. "Patristic" is conceived in a very broad sense).
Dataset for Earth Sciences at Freie Universität Berlin: Open Access, Licenses and Persistent Identifiers Monitoring
<p>In <em>Version 4</em>, <strong>publishers </strong>and <strong>journals</strong> names has been extended.</p> <p>In <em>Version 3</em>, new entries have been added for both <strong>journal </strong>and <strong>non-journal article outputs</strong>, specifically including data from the year <strong>2023</strong>. Minor adjustments were also made to URLs and open access (OA) statuses.</p> <p><em>Note</em>: Data for journal and non-journal article outputs from the year 2023 were unavailable at the time of preparing the <strong>short paper</strong> presenting the results, findable under <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">10.5281/zenodo.14170751</a> [1]).</p> <p><br>Started in 2021, Berlin University Alliance (BUA) Open Science Dashboards, followed by the BUA Open Science Magnifiers projects, seek to investigate Open Science (OS) practices across different research domains and communities. A primary focus of these initiatives lies in the development of OS indicators, tailored to discipline specific ones, alongside their visualisation for monitoring.</p> <p>Collaborating closely with the Department of Earth Sciences at Freie Universität Berlin (FU), one of the project's key objectives is the implementation of an Open Science Dashboard for Earth Sciences FU. The visualisation of the first OS metrics is already available under <a href="https://quest-open-earthsciences.charite.de/">https://quest-open-earthsciences.charite.de/</a>.</p> <p>The datasets utilized include the outputs from the Department of Earth Sciences at FU, i.a. on Open Access (OA) categorisations and statuses, persistent identifiers (PIDs) and Open Licences (Creative Commons) availability, published between 2016-2023. These datasets consist of (i) <strong>"journal_articles_v3.csv"</strong> and (ii) <strong>"non_journal_articles_outputs_v3.csv"</strong>, the latter including “book”, “book chapter”, “conference paper”, “conference abstract”, and “other research outputs” (e.g. book reviews, project reports, book chapters in school books, or electronic supplementary material).</p> <p>Data for the dashboard was obtained from the FU university bibliography (<a href="https://frub-berlin.primo.exlibrisgroup.com/">https://frub-berlin.primo.exlibrisgroup.com/</a>), but coverage of PID information was incomplete, OA category information was incomplete and often erroneous, and copyright/open licence information was missing in this data set. Therefore, the data set was <strong>enriched with manually researched information</strong>. Data enrichment was different for journal articles and for non-journal-article publications. For <strong><em>journal articles</em></strong>, <em>copyright/open licence</em> information was added, and <em>open access category</em> information was checked and added or corrected. For <strong><em>non-journal-article outputs</em></strong>, missing <em>PIDs</em> were added and <em>open access category</em> information was checked and added or corrected. </p> <p>The "<em>data_dictionary_earth_sciences_v3.csv"</em> table documents all variables of each data file containing here.</p> <p>Both for the dashboard, and in our following publications, we categorized <strong>"bronze"</strong> OA outputs as closed access. Although such publications are openly available on the publisher's websites, they lack licence information and thus cannot be openly reused, and presumably even change its openness status at any time. Following the methodology of Charité Dashboard on Responsible Research (<a href="https://quest-dashboard.charite.de/#tabStart">https://quest-dashboard.charite.de/#tabStart</a>) we only include "gold", "hybrid" and "green" OA as true OA. Further details about the enrichment process conducted on these datasets can be found under <a href="https://doi.org/10.5281/zenodo.1099821" target="_blank" rel="noopener">10.5281/zenodo.1099821</a>9 [2]</p> <p> </p> <p>[1] Duine, M., Iarkaeva, A., & Hübner, A. (2024, November 15). Initiating discipline-specific Open Science Monitoring with the Open Science Dashboard for Earth Sciences. 28th International Conference on Science, Technology and Innovation Indicators (STI2024), Berlin, Germany. <a href="https://doi.org/10.5281/zenodo.14170751" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14170751</a><br>[2] Duine, M., Hübner, A., & Iarkaeva, A. (2024). Enrichment of university bibliography data for open science monitoring. Zenodo. <a href="https://doi.org/10.5281/zenodo.10998219">https://doi.org/10.5281/zenodo.10998219</a></p>
FASTA file containing the MYB encoding gene An2-like and Ant1 coding sequences corresponding to wild and cultivated tomato accessions
<p>The coding sequence (CDS) of the MYB encoding genes <em>Ant1</em> and <em>An2-like</em>. Sequences were retrieved from regions corresponding to the<em> Aft</em> locus from <em>Solanum galapagense </em>accession LA1141, <em>S. lycopersicum</em> variety OH8245, and 84 tomato accessions published as part of The 100 Tomato Genome Sequencing Consortium (The 100 Tomato Genome Sequencing Consortium et al., 2014). Sequences were compared to available CDS available from the Sol genomics network (SGN) and the National Center for Biotechnology Information. The CDS was retrieved from <em>S. lycopersicum</em> variety Indigo Rose [MN433087 (Yan et al., 2020)], <em>S. lycopersicum</em> accession LA1996 [MN242011.1, EF433417.1( Sapir et al., 2008; Colanero et al., 2020)], and <em>S. chilense </em>accession LA1930 [MN242012.1 (Colanero et al., 2020)], The orthologous CDS corresponding to the <em>Aft </em>MYB encoding genes from <em>Solanum tuberosum</em> L. Group Phureja clone DM1-3 genome (PGSC DM v4.03 Pseudomolecules) was retrieved from the Potato Genome Sequence Consortium (PGSC: Potato Genome Sequencing Consortium et al., 2011), and the Capsicum annum cv. CM334 genome was retrieved from <em>Capsicum annuum </em>cv CM334 genome chromosome release 1.55 (Hulse-Kemp et al. 2018). These CDS were obtained using the Basic Local Alignment Search Tool (BLAST) tool available from the Sol Genomics Network (SGN) (available at https://solgenomics.net/tools/blast/). Comparison of syntenic chromosomal regions using known positions of tomato, potato, and pepper markers with comparative map viewer from SGN: (available at https://solgenomics.net/cview) on chromosome 10, was used as a quality check for S.<em> tuberosom</em> and <em>C. annuum.</em> Orthologous CDS corresponding to <em>Salvia miltiorrhiza, Arabidopsis thaliana</em>, [NM_105308.2, NM_105310.4 (Teng et al., 2005, Cominelli et al., 2008; Beradini et al., 2015)] were chosen based on tomato <em>Aft</em> sequence homology and gene annotations of positive R2R3 MYB regulation of anthocyanin. The CDS corresponding to the <em>Aft</em> genes were retrieved from the CDS reference genomes available from the Sol Genomics Network SGN: Tomato Genome CDS (ITAG release 4.0), Potato PGSC DM v3.4 CDS sequences, <em>Capsicum annuum </em>cv CM334 Genome CDS (release 1.55), or from the National Center for Biotechnology Information (NCBI: https://www.ncbi.nlm.nih.gov) reference sequences (RefSeq) section of the Genbank records. When accessed from Genank records, the CDS sequence was extracted from the “features” section and exported as a FASTA file.</p>
Fachinformationsdienst (FID) BAUdigital: Ergebnisse der Umfrage zu Open Access und Forschungsdaten 2021
<p>Die im Rahmen des Fachinformationsdienstes BAUdigital durchgeführte Umfrage (Laufzeit: 08. März - 30. April 2021) hatte das Ziel, die Publikationskulturen der adressierten Disziplinen zu ermitteln und bedarfsorientiert zu erheben, welche Dienstleistungen und Informationsangebote für die Fachcommunity wichtig sind. Die Umfrage richtete sich daher vorrangig an Forschende der Fachdisziplinen Bauingenieurwesen, Architektur und Urbanistik, aber auch an Fachvertreter*innen aus Lehre und Wirtschaft. </p> <p>Für die Erhebung der Daten wurde LimeSurvey verwendet, die Beantwortung des Fragebogens dauerte durchschnittlich 15 Minuten und erfolgte anonym.</p> <p>Der DFG-geförderte Fachinformationsdienst BAUdigital ist ein Verbundprojekt der Universitätsbibliothek Braunschweig, der Universitäts- und Landesbibliothek Darmstadt, der TIB – Leibniz-Informationszentrum Technik und Naturwissenschaften und dem Fraunhofer-Informationszentrum Raum und Bau.</p>
Sample accession list for "Malaria protection due to sickle haemoglobin depends on parasite genotype"
<p>This dataset contains a list of sample accessions and associated metadata for <em>P.falciparum</em><br> DNA samples sequenced for the analysis presented in the paper:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The data contains: i. a single tab-delimited text file containing accessions and sequence read quality control-related information related to the processing described in [1], and ii. a README file describing the contents of the data in markdown and HTML format. Please see the enclosed README file for full details.</p> <p>A full list of datasets which have been released with this manuscript can be found on the <a href="https://www.malariagen.net/resource/32">MalariaGEN website</a>.</p> <p> </p>
UK HESA 2020 Academic women: Changing the Academic Gender Narrative through Open Access
<p>This Zenodo entry includes the full data files (.csv and .xlsx) for Figure 6: 'Percentages of women academic staff (headcount) in a subset of 165 United Kingdom universities by grouping, 2020', included in the manuscript "Changing the Academic Gender Narrative through Open Access", authored by members of the Curtin Open Knowledge Initiative (COKI). The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).</p>
Data from: The interaction of ice and law in Arctic marine accessibility
<p>Sea ice levies an impost on maritime navigability in the Arctic. But ice cover diminution due to anthropogenic climate change is generating expectations for improved accessibility in coming decades. Projections of sea ice cover retreating preferentially from the eastern Arctic suggest key provisions of international law of the sea will require revision. Specifically, protections against marine pollution in ice covered seas enshrined in Article 234 of the United Nations Convention on the Law of the Sea have been used in recent decades to extend jurisdictional competence over the Northern Sea Route only loosely associated with environmental outcomes. Projections show that plausible open water routes through international waters may be accessible by mid-century under all but the most aggressive of emissions control scenarios. While inter- and intra-annual variability places the economic viability of these routes in question for some time, the inevitability of a seasonally ice-free Arctic will be attended by a reduction of regulatory friction and a recalibration of associated legal frameworks.</p>
Harmonized Cultural Access & Participation Dataset for Music
<p>Changes since the last version: in the .csv export there was a naming problem.</p> <p>- `visit_concert`: This is a standard CAP variables about visiting frequencies, in numeric form. <br> - `fct_visit_concert`: This is a standard CAP variables about visiting frequencies, in categorical form. <br> - `is_visit_concert`: binary variable, 0 if the person had not visited concerts in the previous 12 months.<br> - `artistic_activity_played_music`: A variable of the frequency of playing music as an amateur or professional practice, in some surveys we have only a binary variable (played in the last 12 months or not) in other we have frequencies. We will convert this into a binary variable. <br> - `fct_artistic_activity_played_music`: The `artistic_activity_played_music` in categorical representation.<br> - `artistic_activity_sung`: A variable of the frequency of singing as an amateur or professional practice, like played_muisc. Because of the liturgical use of singing, and the differences of religious practices among countries and gender, this is a significantly different variable from played_music.<br> - `fct_artistic_activity_sung`: The `artistic_activity_sung` variable in categorical representation.<br> - `age_exact`: The respondent’s age as an integer number. <br> - `country_code`: an ISO country code<br> - `geo`: an ISO code that separates Germany to the former East and West Germany, and the United Kingdom to Great Britain and Northern Ireland, and Cyprus to Cyprus and the Turiksh Cypriot community.[we may leave Turkish Cyprus out for practical reasons.]<br> - `age_education`: This is a harmonized education proxy. Because we work with the data of more than 30 countries, education levels are difficult to harmonize, and we use the Eurobarometer standard proxy, age of leaving education. It is a specially coded variable, and we will re-code them into two variables, `age_education` and `is_student`. <br> - `is_student`: is a dummy variable for the special coding in age_education for “still studying”, i.e. the person does not have yet a school leaving age. It would be tempting to impute `age` in this case to `age_education`, but we will show why this is not a good strategy.<br> - `w`, `w1`: Post-stratification weights for the 15+ years old population of each country. Use `w1` for averages of `geo` entities treating Northern Ireland, Great Britain, the United Kingdom, the former GDR, the former West Germany, and Germany as geographical areas. Use `w` when treating the United Kingdom and Germany as one territory.<br> - `wex`: Projected weight variable. For weighted average values, use `w`, `w1`, for projections on the population size, i.e., use with sums, use `wex`.<br> - `id`: The identifier of the original survey.<br> - `rowid``: A new unique identifier that is unique in all harmonized surveys, i.e., remains unique in the harmonized dataset.</p>
Modular control of human movement during running: an open access data set
<p>The human body is an outstandingly complex machine including around 1000 muscles and joints acting synergistically. Yet, the coordination of the enormous amount of degrees of freedom needed for movement is mastered by our one brain and spinal cord. The idea that some synergistic neural components of movement exist was already suggested at the beginning of the XX century. Since then, it has been widely accepted that the central nervous system might simplify the production of movement by avoiding the control of each muscle individually. Instead, it might be controlling muscles in common patterns that have been called muscle synergies. Only with the advent of modern computational methods and hardware it has been possible to numerically extract synergies from electromyography (EMG) signals. However, typical experimental setups do not include a big number of individuals, with common sample sizes of five to 20 participants. With this study, we make publicly available a set of EMG activities recorded during treadmill running from the right lower limb of 135 healthy and young adults (78 males, 57 females). Moreover, we include in this open access data set the code used to extract synergies from EMG data using non-negative matrix factorization and the relative outcomes. Muscle synergies, containing the time-invariant muscle weightings (motor modules) and the time-dependent activation coefficients (motor primitives), were extracted from 13 ipsilateral EMG activities using non-negative matrix factorization. Four synergies were enough to describe as many gait cycle phases during running: weight acceptance, propulsion, early swing and late swing. We foresee many possible applications of our data, that we can summarize in three key points. First, it can be a prime source for broadening the representation of human motor control due to the big sample size. Second, it could serve as a benchmark for scientists from multiple disciplines such as musculoskeletal modelling, robotics, clinical neuroscience, sport science, etc. Third, the data set could be used both to train students or to support established scientists in the perfection of current muscle synergies extraction methods.</p> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus.</p> <p>The file "dataset.rar" contains data in older format, not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a>.</p>
Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series
<p>This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. 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 264 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.</p>
bulk-tumour-api: a programmatically accessible dataset of pre-processed bulk tumour sequencing data
<p><strong>This repository, including the API, are currently under development.</strong></p> <p><strong>bulk-tumour-api</strong>: A programmatically accessible dataset of pre-processed bulk tumour sequencing data. The python API can be found at https://github.com/tomouellette/bulk-tumour-api. All data stored in this repository have been collected from open access online sources. Original references and sources are provided in database.tsv (for empirical patient data) and synthetic.tsv (for simulated data).</p> <p><strong>A note on datasets: </strong></p> <ul> <li>All <em>empirical patient sequencing </em>samples have been processed into pseudo-VCF files which at minimum contain the following columns: sample identifier (sample), patient identifier (patient), chromosome (chr), position (pos), variant allele frequency (VAF), alternate read counts (t_alt_count), depth (DP), and total copy number (total_cn). However, if more data is required, unprocessed data including copy number segments or gene-level calls, clinical, and/or biopsy level information can be found in the /raw/. </li> <li>All <em>synthetic datasets </em>have also been processed in pseudo-VCF files. In some cases, all ground truth information (e.g. subclone frequency) is contained within the pseudo-VCF. In other cases, additional meta/ground-truth information are in separate files; any simulated sample with a column marked has_meta = True will have multiple files that will be downloaded together.</li> </ul>
Data and codes: Changing the Academic Gender Narrative through Open Access
<p>This Zenodo entry includes data and R codes used to generate the figures included in the manuscript "Changing the Academic Gender Narrative through Open Access", authored by members of the Curtin Open Knowledge Initiative (COKI). These include data that are either publicly available or derived through the COKI data infrastructure.</p> <p>The R file includes codes used to generate Figures 1, 2, 3, 4, 1A, 2A and 3A. It uses data contained in the files "au_data_all.csv", "au_groupings.csv", "uk_data_all.csv" and "uk_groupings.csv".</p> <p>This entry also includes the full data files (.csv and .xlsx) for Figures 5 and 6 included in the manuscript:</p> <ul> <li>Figure 5: ‘Percentages of women academic staff (headcount) compared to the total number of academics in the institution for 43 Australian universities by grouping, 2020’. The analysis is of publicly available data sourced from the Australian Department of Education, Skills and Employment.</li> <li>Figure 6: ‘Percentages of women academic staff (headcount) compared to the total number of academics in the institution for a subset of 165 United Kingdom higher education institutions by grouping, 2020’. The analysis is of publicly available data sourced from the United Kingdom Higher Education Statistics Agency (HESA).</li> </ul>
Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore
<p>Chromatin accessibility data for the CRISPRai prediction algorithm implemented in crisprScore; see https://github.com/crisprVerse/crisprScore for more detail.</p> <p> </p>
Data from "Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography"
<p>Data presented in the Science Advances manuscript "<em>Fast acquisition of propagating waves in humans with low-field MRI: towards accessible MR elastography</em>" by Yushchenko M., Sarracanie M., Salameh N.</p> <p>See further details in <em>Description.txt.</em></p> <p>The 3D wave datasets acquired in humans at 0.1 T can be used for elastogram reconstruction with appropriate methods.<br> <br> </p>
Monitoring open access publishing of NWO funded research (2015-2021) data set
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (<a href="https://doi.org/10.5281/zenodo.7041897">https://doi.org/10.5281/zenodo.7041897</a>)</p> <p>The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in <a href="https://zenodo.org/record/4446042">2020</a> and <a href="https://zenodo.org/record/5056043">2021</a>, covering publications from the period 2015–2018 and 2015-2020, respectively.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.