Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
8,537
datasets available to search
ShareScore release 0.9.0
Dataset results
8,537 results for “Openness”
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
A Large-scale Dataset of (Open Source) License Text Variants
<p>We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive—the largest publicly available archive of FOSS source code with accompanying development history—all versions of files whose names are commonly used to convey licensing terms to software users and developers.<br> The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing.<br> Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared.<br> The dataset is released as open data as an archive file containing all deduplicated license blobs, plus several portable CSV files for metadata, referencing blobs via cryptographic checksums.</p> <p>For more details see the included README file and companion paper:</p> <ul> <li>Stefano Zacchiroli. <a href="https://doi.org/10.1145/3524842.3528491"><em>A Large-scale Dataset of (Open Source) License Text Variants</em></a>. In proceedings of the <a href="https://conf.researchr.org/home/msr-2022">2022 Mining Software Repositories Conference (MSR 2022)</a>. 23-24 May 2022 Pittsburgh, Pennsylvania, United States. ACM 2022.</li> </ul> <p>If you use this dataset for research purposes, please acknowledge its use by citing the above paper.</p> <ul> </ul>
Survey on Open Science Practices in Functional Neuroimaging. Dataset and Materials
<p>Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. A PubMed search with the search terms ("fMRI" OR "functional magnetic resonance imaging" OR "functional Magnetic Resonance Imaging") was done to collect email addresses from corresponding authors of scientific articles published between 2010/01/01 and 2020/08/28. An email was sent to 14,690 addresses on 2020/01/12 with an invitation to participate, including a personalized link to the survey. If the recipients did not click the link or did not complete the survey after 14 days, they received a single reminder email. The questionnaire was composed of five building blocks. The Blocks 1-3 focused on three areas of open science practices: data structure, preregistration and data sharing. The fourth block asked about technical expertise with software and the fifth part assessed sociodemographic data.</p>
Materials for Design Open Repository. High Entropy Alloys
<p>The current dataset is composed of a collection of High Entropy Alloys (HEAs). It contains the alloy composition, the number of chemical elements (No), the phase in a simple form (S_Phase), where 4 classes of phases were considered, namely amorphous (AM), intermetallic (IM), solid solution (SS), and solid solution + intermetallic (SS+IM). It contains also a second phase column (Phase), where we added the type of phase present in alloys with SS and repeated the S_Phase entry for the other cases. We have calculated 13 design parameters (see their definition below) used to design HEAs, known as the parametric approach. Finally, a set of columns containing the chemical elements and their corresponding fraction in the alloy is included. This dataset was developed in the framework of the European project ACHIEF for the discovery of novel materials to be used in industrial processes.</p> <ol> <li>Mean atomic radius <em>a</em> (Å) <ul> <li><span class="math-tex">\(a = \displaystyle\sum_{i=1}^{n} c_i r_i\)</span></li> </ul> </li> <li>Atomic size difference δ <ul> <li><span class="math-tex">\(\delta = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{r_i}{a} \bigg)^2}\)</span></li> </ul> </li> <li>Average melting temperature <em>T<sub>m</sub></em> (K) <ul> <li><span class="math-tex">\(T_m = \displaystyle\sum_{i=1}^{n} c_i T_{mi}\)</span></li> </ul> </li> <li>Average melting temperature standard deviation (K) <ul> <li><span class="math-tex">\(\sigma_{T_m} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{T_{mi}}{T_m} \bigg)^2}\)</span></li> </ul> </li> <li>Mixing enthalpy Δ<em>H<sub>mix</sub></em> (kJ/mol) <ul> <li><span class="math-tex">\(\Delta H_{mix} = 4 \displaystyle\sum_{i \neq j} c_i c_j H_{ij}\)</span></li> </ul> </li> <li>Mixing enthalpy standard deviation (kJ/mol) <ul> <li><span class="math-tex">\(\sigma_{\Delta H_{mix}} = \sqrt{\displaystyle\sum_{i \neq j} c_i c_j (H_{ij} - \Delta H_{mix})^2}\)</span></li> </ul> </li> <li>Ideal mixing entropy <em>S<sub>id</sub></em> (<em>R</em>)<strong>*</strong> <ul> <li><span class="math-tex">\(S_{id} = \Delta S_{mix} = -R \displaystyle\sum_{i=1}^{n} c_i \ln c_i\)</span></li> </ul> </li> <li>Electronegativity <em>χ</em> <ul> <li><span class="math-tex">\(\chi = \displaystyle\sum_{i=1}^{n} c_i \chi_i\)</span></li> </ul> </li> <li>Electronegativity difference in a multi-component alloy system <ul> <li><span class="math-tex">\(\Delta\chi = \displaystyle\sqrt{\sum_{i=1}^{n} c_i(\chi_i - \chi)^2}\)</span></li> </ul> </li> <li>Valence electron concentration <em>VEC</em> <ul> <li><span class="math-tex">\(VEC = \displaystyle\sum_{i=1}^{n} c_i \cdot VEC_i\)</span></li> </ul> </li> <li>Valence electron concentration standard deviation <ul> <li><span class="math-tex">\(\sigma_{VEC} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (VEC_i - VEC)^2}\)</span></li> </ul> </li> <li>Mean bulk modulus <em>K </em>(GPa) <ul> <li><span class="math-tex">\(K = \displaystyle\sum_{i=1}^{n} c_i K_i\)</span></li> </ul> </li> <li>Bulk modulus standard deviation (GPa) <ul> <li><span class="math-tex">\(\sigma_{K} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (K_i - K)^2}\)</span></li> </ul> </li> <li>Young's modulus <em>E</em> (GPa) <ul> <li><span class="math-tex">\(E = \displaystyle\sum_{i=1}^{n} c_i E_i\)</span></li> </ul> </li> <li>Shear modulus <em>G</em> (GPa) <ul> <li><span class="math-tex">\(G = \displaystyle\sum_{i=1}^{n} c_i G_i\)</span></li> </ul> </li> </ol> <p>where <em>n</em> is the number of components in the alloy system, <em>c<sub>i</sub></em> is the stoichiometric ratio, <em>r<sub>i</sub></em> is the atomic radius, <em>T<sub>mi</sub></em> is the melting temperature, <em>χ<sub>i</sub></em> is the Pauli electronegativity, <em>VEC<sub>i</sub></em> is the valence electron concentration, and <em>K<sub>i</sub></em> is the bulk modulus, <em>E<sub>i</sub></em> is the Young's modulus, and <em>G<sub>i</sub></em> is shear modulus for the <em>i</em>-th component of the alloy. <em>H<sub>ij</sub></em> is the binary mixing enthalpy in the liquid phase, and <em>R</em> is the gas constant.</p> <p><strong>*Note:</strong> the ideal mixing entropy <em>S<sub>id</sub></em> units in the first version of the dataset appear as kJ/mol, but they should be written in terms of the gas constant <em>R</em>, e.g., the compound Ag<sub>2</sub>Al has <em>S<sub>id</sub></em> = 0.636 <em>R</em>, where <em>R</em> = 8.314 J · K<sup>−1</sup> · mol<sup>−1</sup>. The second version the <em>S<sub>id</sub></em> units are corrected and two new features are included.</p>
Experimental Results of the REWIRE FED4FIRE+ Open Call (OC) 9 Project
<p>This repository contains the detailed experimental results of the <strong>REWIRE <em>"Experimenting with SDN-based Adaptable Non-IP Protocol Stacks in Smart-City Environments"</em></strong> project.</p> <p>This work has received funding from the EU's Horizon 2020 research and innovation programme through the 9th open call scheme of the FED4FIRE+ (grant agr. no 732638)</p>
5D-NP-MATER_MDO - Open Dataset for "Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing Based on Cyclic Ketene Acetals"
<p>This is the open dataset for the paper: "Marco Carlotti*, Omar Tricinci, Virgilio Mattoli*, Novel, High-Resolution, Subtractive Photoresist Formulations for 3D Direct Laser Writing based on Cyclic Ketene Acetals, Advanced Materials Technologies, On line (2022) [DOI: 10.1002/admt.202101590] "</p> <p>This include the Supplementary Information file ("SI.pdf") , all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata.</p> <p> </p>
Dataset: maturity of transparency of open data ecosystems in 22 smart cities
<p>This dataset contains data collected during a study "<a href="https://www.sciencedirect.com/science/article/pii/S2210670722002281?casa_token=8xHhtKug0xEAAAAA:POKIQswXhPdbwqgi5A8q98xitcUju_VS8T7oSP6YujXdABZlc5bNn4vEHzHGoxoW16mT6hA-HZ4#!">Transparency of open data ecosystems in smart cities: Definition and assessment of the maturity of transparency in 22 smart cities</a>" (Sustainable Cities and Society (SCS), vol.82, 103906) conducted by Martin Lnenicka (University of Pardubice), Anastasija Nikiforova (University of Tartu), Mariusz Luterek (University of Warsaw), Otmane Azeroual (German Centre for Higher Education Research and Science Studies), Dandison Ukpabi (University of Jyväskylä), Visvaldis Valtenbergs (University of Latvia), Renata Machova (University of Pardubice).</p> <p>This study inspects smart cities’ data portals and assesses their compliance with transparency requirements for open (government) data by means of the expert assessment of 34 portals representing 22 smart cities, with 36 features.</p> <p>It being made public both to act as supplementary data for the paper and in order for other researchers to use these data in their own work potentially contributing to the improvement of current data ecosystems and build sustainable, transparent, citizen-centered, and socially resilient open data-driven smart cities.</p> <p>***Purpose of the expert assessment***<br> The data in this dataset were collected in the result of the applying the developed benchmarking framework for assessing the compliance of open (government) data portals with the principles of transparency-by-design proposed by Lněnička and Nikiforova (2021)* to 34 portals that can be considered to be part of open data ecosystems in smart cities, thereby carrying out their assessment by experts in 36 features context, which allows to rank them and discuss their maturity levels and (4) based on the results of the assessment, defining the components and unique models that form the open data ecosystem in the smart city context.</p> <p>***Methodology***<br> Sample selection: the capitals of the Member States of the European Union and countries of the European Economic Area were selected to ensure a more coherent political and legal framework. They were mapped/cross-referenced with their rank in 5 smart city rankings: IESE Cities in Motion Index, Top 50 smart city governments (SCG), IMD smart city index (SCI), global cities index (GCI), and sustainable cities index (SCI). A purposive sampling method and systematic search for portals was then carried out to identify relevant websites for each city using two complementary techniques: browsing and searching.<br> To evaluate the transparency maturity of data ecosystems in smart cities, we have used the transparency-by-design framework (<a href="https://www.sciencedirect.com/science/article/pii/S0736585321000447?casa_token=7K8YGcYWbQcAAAAA:_HnV50rvxwQmDYyTjLYCmUkhDM2Qpsu8TPPBgOxajkV6ammJ1BBwgtQEnMMZdVk5ONxrGNY8hOw">Lněnička & Nikiforova, 2021</a>)*.<br> The benchmarking supposes the collection of quantitative data, which makes this task an acceptability task. A six-point Likert scale was applied for evaluating the portals. Each sub-dimension was supplied with its description to ensure the common understanding, a drop-down list to select the level at which the respondent (dis)agree, and a comment to be provided, which has not been mandatory. This formed a protocol to be fulfilled on every portal. Each sub-dimension/feature was assessed using a six-point Likert scale, where strong agreement is assessed with 6 points, while strong disagreement is represented by 1 point.<br> Each website (portal) was evaluated by experts, where a person is considered to be an expert if a person works with open (government) data and data portals daily, i.e., it is the key part of their job, which can be public officials, researchers, and independent organizations. In other words, compliance with the expert profile according to the International Certification of Digital Literacy (ICDL) and its derivation proposed in <a href="https://www.emerald.com/insight/content/doi/10.1108/OIR-05-2020-0204/full/html?casa_token=6Yd7zSiQMg0AAAAA:yT8d_thrh84stDSVbax8eXLm5vP9LkrwZZFMzC_vql9vZNoQP_iYBHCZ0NOndkvusIx9TZAvJLWBp6lqe9bymm-xHaZ93k2mYfoHXKdVq52A0a7MlwGa">Lněnička et al. (2021)</a>* is expected to be met.<br> When all individual protocols were collected, mean values and standard deviations (SD) were calculated, and if statistical contradictions/inconsistencies were found, reassessment took place to ensure individual consistency and interrater reliability among experts’ answers.<br> *<a href="https://www.sciencedirect.com/science/article/pii/S0736585321000447?casa_token=7K8YGcYWbQcAAAAA:_HnV50rvxwQmDYyTjLYCmUkhDM2Qpsu8TPPBgOxajkV6ammJ1BBwgtQEnMMZdVk5ONxrGNY8hOw">Lnenicka, M., & Nikiforova, A. (2021). Transparency-by-design: What is the role of open data portals?. Telematics and Informatics, 61, 101605</a><br> *<a href="https://www.emerald.com/insight/content/doi/10.1108/OIR-05-2020-0204/full/html?casa_token=6Yd7zSiQMg0AAAAA:yT8d_thrh84stDSVbax8eXLm5vP9LkrwZZFMzC_vql9vZNoQP_iYBHCZ0NOndkvusIx9TZAvJLWBp6lqe9bymm-xHaZ93k2mYfoHXKdVq52A0a7MlwGa">Lněnička, M., Machova, R., Volejníková, J., Linhartová, V., Knezackova, R., & Hub, M. (2021). Enhancing transparency through open government data: the case of data portals and their features and capabilities. Online Information Review.</a></p> <p>***Test procedure***<br> (1) perform an assessment of each dimension using sub-dimensions, mapping out the achievement of each indicator<br> (2) all sub-dimensions in one dimension are aggregated, and then the average value is calculated based on the number of sub-dimensions – the resulting average stands for a dimension value - eight values per portal<br> (3) the average value from all dimensions are calculated and then mapped to the maturity level – this value of each portal is also used to rank the portals.</p> <p>***Description of the data in this data set***<br> Sheet#1 "comparison_overall" provides results by portal<br> Sheet#2 "comparison_category" provides results by portal and category<br> Sheet#3 "category_subcategory" provides list of categories and its elements<br> </p> <p>***Format of the file***<br> .xls</p> <p>***Licenses or restrictions***<br> CC-BY</p> <p>For more info, see README.txt</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>
zbMATH Open Mathematics Subject Classification Dataset (springer)
<p>zbMATH Open Mathematics Subject Classification Dataset consisting of the following fields</p> <p><em>doi</em>: digital object identifier</p> <p><em>msc</em>: primary MSC of the article</p> <p><em>zbl_id</em>: zbMATH Open identier</p>
OmniFold Weights | CMS 2011A Open Data | Jet Primary Dataset | pT 375-700 GeV
<p>Unfolding weights corresponding to a selection of jets from the <a href="https://doi.org/10.5281/zenodo.3340205">Jet Primary Dataset of the CMS 2011A Open Data in MOD HDF5 format</a> and associated simulated datasets. The unfolding is performed in a high-dimensional manner using the <a href="https://arxiv.org/abs/1911.09107">OmniFold</a> method, which can unfold all observables simultaneously. <a href="https://arxiv.org/abs/1810.05165">Particle Flow Networks</a> are used in Step 1 and Step 2 of the OmniFold method to process the full phase space information. The datasets and neural networks were accessed/built via the <a href="https://energyflow.network/">EnergyFlow Python package</a>. An upcoming version of the package will contain an example/demo demonstrating how to use these weights.</p> <p>The phase space selections for the data, sim, and gen datasets (using the terminology of the OmniFold paper) are:</p> <ul> <li>data: <span class="math-tex">\(p_T^{\rm jet}\in [375, 700]\)</span> GeV, <span class="math-tex">\(|\eta^{\rm jet}|<2.4\)</span>, jet quality <span class="math-tex">\(\ge\)</span> 2</li> <li>sim: <span class="math-tex">\(p_{T,\text{corr}}^{\rm jet} \in [375, 700]\)</span> GeV, <span class="math-tex">\(|\eta^{\rm jet}| < 2.4\)</span>, gen jet matched ('gen_jet_pts != -1' in EnergyFlow), jets from the <a href="https://doi.org/10.5281/zenodo.3341500">170</a> and <a href="https://doi.org/10.5281/zenodo.3341772">1800</a> MC datasets are excluded</li> <li>gen: Matched to sim jet</li> </ul> <p>The omnifold_weights.npz file contains two arrays, 'wssim' corresopnding to the Step 1 weights <span class="math-tex">\(\omega_n\)</span>, and 'wsgen' corresponding to the Step 2 weights <span class="math-tex">\(\nu_n\)</span>, for iteration <span class="math-tex">\(n\)</span>. The shape of each of these arrays is (6, 16489054), with the first axis being the iteration axis and the second axis being the event axis. There are 5 iterations, but 6 sets of weights in each array, with the 0th entry being the starting weights.</p>
CMS Open Data 2012 datasets for dimuon exercises
<p>These datasets are a subset of the CMS Open data with 2021 data-taking conditions for education purposes.</p> <p>In this version, the data and simulation files are compressed into one big file for easy access. They are stored in two different formats (CSV and PKL) with the same content, therefore just use one of them.</p> <p>Once unzipped:</p> <p>- Data files, starting with output_data_CMS_Run2012B, correspond to 4429.37 /pb of data collected by the CMS Experiment. They are a subset of the dataset on reference [1].</p> <p>- Simulation files, starting with output_sim_CMS_MonteCarlo2012, are a subset of the dataset referenced on [2]. The number of generated events in this case is 30458871, and the cross section is 3503.71.</p> <p>All the files were processed with a modified version of the AOD2NanoAODOutreachTool [3]. The small modifications are related to the number of triggers stored, and some objects like taus were removed.</p> <p> </p> <p>--------------------------------------------------------</p> <p>[1] CMS collaboration (2017). DoubleMuParked primary dataset in AOD format from Run of 2012 (/DoubleMuParked/Run2012B-22Jan2013-v1/AOD). CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.YLIC.86ZZ">10.7483/OPENDATA.CMS.YLIC.86ZZ</a></p> <p>[2] Wunsch, Stefan; (2019). DYJetsToLL dataset in reduced NanoAOD format for education and outreach. CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.SRRA.2GON">10.7483/OPENDATA.CMS.SRRA.2GON</a></p> <p>[3] https://github.com/cms-opendata-analyses/AOD2NanoAODOutreachTool</p>
Stack Exchange Open Source site questions categorization
<p>This dataset contains the posts of Open Source Stack Exchange site, collected at the end of 2020, along with the categorization of the posts. For each post a category, and potentially a second one is indicated, along with the cluster (generic group) each category belongs to. The coding task of assigning each question to a category was performed by two independent coders for each question (the categorization of each coder is also provided in the dataset). The dataset contains also (in a separate file) a dictionary of the most correlated unigrams and bigrams per category.</p>
Evaluation Set - Contributions Similarity in the Open Research Knowledge Graph
<p>This evaluation set has been created for evaluating a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts structured ORKG contribution as input and recommends existing contributions in the ORKG semantically relevant to the given one.</p> <p> </p> <p>The evaluation set is manually annotated based on the <a href="https://www.orkg.org/orkg/featured-comparisons">featured comparisons</a> in the ORKG. In the course of this, it has been distinguished between homogeneous (those who are dissimilar in 2-3 properties) and heterogeneous (otherwise) instances. Multiple annotations have been obtained for the former and exactly one for the latter.</p> <p> </p> <p>It has been also distinguished between "with_response" and "without_response" instances (50 instances for each). The former are those contributions for them the initial version of the contributions similarity service has found similarities and the latter are the opposite case.</p> <p> </p> <p>This evaluation set has been created and applied on a modified version of the contributions similarity service in the context of <a href="https://doi.org/10.15488/11834">this master's thesis</a>. The modified version of the service has simplified the document representation of contributions that are stored in an ElasticSearch index by omitting redundant terms.</p> <p>The evaluation set has the following schema:</p> <pre><code class="language-json">{ "with_response": [ { "contribution_id": "some_id", "comparison_id": "some_id", "comparison_label": "some_label", "contribution_label": "some_label", "paper": "some_id", "research_field": "some_id", "research_problems": [ "some_id" ], "annotations": [ "some_id of a similar contribution", ... ] }, ... ], "without_response": [ ... ] }</code></pre> <p> </p>
Dependency Networks of Open Source Libraries Available Through CocoaPods, Carthage and Swift PM
<p>Third party libraries are used to integrate existing solutions for common problems and help speed up development. The use of third party libraries, however, can carry risks, for example through vulnerabilities in these libraries. Studying the dependency networks of package managers lets us better understand and mitigate these risks. So far, the dependency networks of the three most important package managers of the Apple ecosystem, CocoaPods, Carthage and Swift PM, have not been studied. We analysed the dependencies for all publicly available open source libraries up to December 2021 and compiled a dataset containing the dependency networks of all three package managers. The dependency networks can be used to analyse how vulnerabilities are propagated through transitive dependencies. In order to ease the tracing of vulnerable libraries we also queried the NVD database and included publicly reported vulnerabilities for these libraries in the 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>
PALEODEM/ Supplementary materials of the manuscript "Unraveling Early Holocene occupation patterns at El Arenal de la Virgen (Alicante, Spain) open-air site: an integrated palimpsest analysis"
<p>This repository hosts the R code scripts and datasets that allow reproducibility and replicability of the intra-site spatial analyses implemented in the paper:</p> <p>Rabuñal, J.R., Gómez-Puche, M., Polo-Díaz, A., Fernández-López de Pablo, J., 2022. Unraveling Early Holocene occupation histories at open-air sites through integrated chronological, archaeostratigraphical, lithic refitting and spatial analyses: the Arenal de la Virgen (Villena, Alicante) study case. SocArXiv.</p> <p>Contents:</p> <p>AV_Spatial_database.xlsx: main dataset for the intra-site spatial analysis.</p> <p>AV_Lcross_database.xlsx: dataset for the implementation of the cross-type L function.</p> <p>AV_2clusters.rds: dataset for the calculation of the artifact metrics.</p> <p>AV_MovingWindow_Results.csv: dataset with the results of the calculation of the Burnt Microdebris Index and its spatial autocorrelation analysis.</p> <p>AV_DBSCAN_Separation.R: R file containing the code used for the separation of the lithic spatial distribution using the DBSCAN automated density-based clustering algorithm.</p> <p>AV_Spatial_analysis.R: R file containing the code used for the intra-site spatial analysis.</p> <p>AV_MWA_Moran.R: R file containing the code for implementing the calculation and spatial autocorrelation analysis of the Burnt Microdebris Index.</p>
Basic data visualisations for Figshare State of Open Data 2021 survey
<p>R markdown files for:</p> <ul> <li>Downloading and cleaning data from the State of Open Data survey 2021</li> <li>Basic visualisations of responses to questions in the State of Open Data survey 2021</li> <li>HTML file of those visualisations.</li> </ul> <p>Free text fields are included in the markdown but have been turned off for knitting and in the HTML file.</p>
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>
Video: Introducing the Open Book Collective (OBC)
<p><em>This video is a Deliverable (D2.11) of the COPIM project.</em></p> <p>Open Book Collective is a non-profit that connects academics, librarians, publishers, and service providers to collectively sustain the infrastructures, relationships, and organizations vital for the success of open access book publishing.</p> <p>Through the Open Book Collective platform, publishers and publishing service providers can offer research institutions the option to financially support their work through library membership programs. Librarians can access the platform to explore and assess different initiatives, support Open Access collections, and manage their subscriptions.</p> <p>OBC’s legal and governance structure ensures it can’t be co-opted for profit, and that stakeholders have a meaningful say in its future. We provide financial support to new open access initiatives and connect book publishers to sustainable revenue streams.</p> <p>Open Book Collective is helping build a world where open access books are produced and distributed collaboratively and anti-competitively, without technical or economic barriers.</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.