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8,537 results for “Openness”
Anonymised Dataset Survey of Open Repositories in Ireland
<p>A dataset of survey results of the National Open Research Forum (NORF) Survey of Open Repositories in Ireland. Conducted from April 24 to May 30, 2023, presents a comprehensive overview of the status of open repositories in Ireland. The survey involved respondents from educational, governmental, and research institutions aimed to assess various aspects of repository provision and management across key performance indicators.</p>
SCOPING REVIEW OF EMPIRICAL LITERATURE ON EVALUATION OF EDITORIAL POLICIES SUPPORTING OPEN SCIENCE PRACTICES IN SCHOLARLY JOURNALS
<p>Excel file containing the description of methods & materials used in the scoping review, the whole dataset of included studies, and respective descriptive metadata.</p>
FIGURE 4 in Global access to nomenclatural botanical resources: Evaluating open access availability
FIGURE 4 (a) Proportion of International Plant Names Index (IPNI) nomenclatural act records that are non-discoverable and (b) their ratio of open to closed access by World Geographic Scheme for Recording Plant Distributions (WGSRPD) level 2.
FIGURE 3 Bibliographic works containing vascular plant nomenclatural events published between 2019 and 2021 in Global access to nomenclatural botanical resources: Evaluating open access availability
FIGURE 3 Bibliographic works containing vascular plant nomenclatural events published between 2019 and 2021 and their use of open access (OA), coverage of top 80% of the International Plant Names Index (IPNI) dataset (journal abbreviations follow IPNI, asterisk indicates that the title is included in the Directory of Open Access Journals).
FIGURE 2 in Global access to nomenclatural botanical resources: Evaluating open access availability
FIGURE 2 Open access (a) takeup and (b) status for nomenclatural acts recorded in the International Plant Names Index per year of study (2012–2021).
FIGURE 1 in Global access to nomenclatural botanical resources: Evaluating open access availability
FIGURE 1 Flowchart depicting category assignment for (a) takeup of open access and (b) open access status.
A Dataset of Atoms of Confusion in the Android Open Source Project
<p>This is a dataset cataloging the presence of Atoms of Confusion in the Android Open Source Project, alongside metrics<br>of object-oriented software development.</p> <p> </p> <h3>Schema</h3> <h4>ac_reports</h4> <p><strong>id </strong>INTEGER The primary key <br>project_name VARCHAR Name of the project/repository in the AOSP<br>line INTEGER NOT NULL Line where the AC was found<br>snippet VARCHAR NOT NULL The excerpt of code that represents the Atom of Confusion<br>class_name VARCHAR Name of the class where the AC was found<br>ac VARCHAR NOT NULL Type of Atom of Confusion<br>path VARCHAR Path to the file in the AOSP superproject<br>"commit" VARCHAR SHA of the commit of the file in the repository<br>loc VARCHAR Number of lines of code in the file</p> <h4>files</h4> <p>path VARCHAR The path of the file</p> <p>loc INTEGER Lines of code</p> <p>project_name VARCHAR Name of the project/repository in the AOSP</p> <h4>projects</h4> <p>name VARCHAR The name of the project/repository in the AOSP</p> <p> </p> <h4>classes</h4> <p>id INTEGER The primary key </p> <p>file_path VARCHAR Path to the file in the AOSP superproject</p> <p>name VARCHAR Name of the class</p> <p>type VARCHAR The type of class (e.g. interface, anonymous...)</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>dit INTEGER From CK: Depth Inheritance Tree</p> <p>noc INTEGER From CK: Number of Children</p> <p>rfc INTEGER From CK: Response for Class</p> <p>lcom INTEGER From CK: Lack of Cohesion of Methods</p> <p>lcom_normalized DOUBLE From CK: Refers to the LCOM* metric that computes Lack of Cohesion of Methods between 0 and 1.</p> <p>tcc DOUBLE From CK: Tight Class Cohesion</p> <p>lcc DOUBLE From CK: Loose Class Cohesion</p> <p>"totalMethodsQty" INTEGER From CK: Number of methods (constructors are included)</p> <p>"staticMethodsQty" INTEGER From CK: Number of static methods</p> <p>"publicMethodsQty" INTEGER From CK: Number of public methods</p> <p>"privateMethodsQty" INTEGER From CK: Number of private methods</p> <p>"protectedMethodsQty" INTEGER From CK: Number of protected methods</p> <p>"defaultMethodsQty" INTEGER From CK: Number of default methods</p> <p>"visibleMethodsQty" INTEGER From CK: Number of visible methods</p> <p>"abstractMethodsQty" INTEGER From CK: Number of abstract methods</p> <p>"finalMethodsQty" INTEGER From CK: Number of final methods</p> <p>"synchronizedMethodsQty" INTEGER From CK: Number of synchronized methods</p> <p>"totalFieldsQty" INTEGER From CK: Number of fields</p> <p>"staticFieldsQty" INTEGER From CK: Number of static fields</p> <p>"publicFieldsQty" INTEGER From CK: Number of public fields</p> <p>"privateFieldsQty" INTEGER From CK: Number of private fields</p> <p>"protectedFieldsQty" INTEGER From CK: Number of protected fields</p> <p>"defaultFieldsQty" INTEGER From CK: Number of default fields</p> <p>"finalFieldsQty" INTEGER From CK: Number of final fields</p> <p>"synchronizedFieldsQty" INTEGER From CK: Number of synchronized fields</p> <p>nosi INTEGER From CK: Number of static invocations</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p> </p> <h4>methods</h4> <p>id_ INTEGER NOT NULL The primary key</p> <p>file_path VARCHAR NOT NULL Path to the file in the AOSP superproject</p> <p>class_id INTEGER NOT NULL ID of the class where this method is</p> <p>name VARCHAR NOT NULL The name of the method</p> <p>constructor BOOLEAN NOT NULL From CK: True if method is a constructor</p> <p>line INTEGER From CK: Line where this method is located</p> <p>cbo INTEGER From CK: Coupling between objects, number of dependencies a class has.</p> <p>"cboModified" INTEGER From CK: Coupling between objects, number of dependencies a class has. (References from other classes to this class + references from this class to other classes)</p> <p>fanin INTEGER From CK: number of classes that reference this particular class.</p> <p>fanout INTEGER From CK: the number of classes referenced by this class</p> <p>wmc INTEGER From CK: Weight Method Class | McCabe's complexity | Cyclomatic Complexity</p> <p>rfc INTEGER From CK: Response for Class</p> <p>loc INTEGER From CK: Lines of code</p> <p>"returnQty" INTEGER From CK: Number of return statements</p> <p>"variablesQty" INTEGER From CK: Number of variables</p> <p>"parametersQty" INTEGER From CK: Number of parameters</p> <p>"methodsInvokedQty" INTEGER From CK: all methods invoked</p> <p>"methodsInvokedLocalQty" INTEGER From CK: methods invoked locally</p> <p>"methodsInvokedIndirectLocalQty" INTEGER From CK: number of indirect local invocations</p> <p>"loopQty" INTEGER From CK: Number of repetition structures (while, for)</p> <p>"comparisonsQty" INTEGER From CK: Number of comparisons, e.g. ==, !=</p> <p>"tryCatchQty" INTEGER From CK: Number of try...catch statements</p> <p>"parenthesizedExpsQty" INTEGER From CK: Number of expressions inside parenthesis.</p> <p>"stringLiteralsQty" INTEGER From CK: Number of string literals. Repeated strings are counted as well.</p> <p>"numbersQty" INTEGER From CK: Number of numbers, e.g. ints, floats, doubles</p> <p>"assignmentsQty" INTEGER From CK: Number of assignments, usage of the = operator.</p> <p>"mathOperationsQty" INTEGER From CK: Number of math operations.</p> <p>"maxNestedBlocksQty" INTEGER From CK: Number of the highest nested block in the class.</p> <p>"anonymousClassesQty" INTEGER From CK: Number of anonymous classes</p> <p>"innerClassesQty" INTEGER From CK: Number of inner classes</p> <p>"lambdasQty" INTEGER From CK: Number of lambdas</p> <p>"uniqueWordsQty" INTEGER From CK: Number of unique words in the source file</p> <p>modifiers INTEGER From CK: Number of modifiers in class/methods (usage of "public", "static", "abstract"...)</p> <p>"logStatementsQty" INTEGER, From CK: Number of log statements.</p> <p>"hasJavaDoc" BOOLEAN From CK: True if method has a JavaDoc</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon
<p>List of papers used in literature review for "A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon"</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control
<p>This dataset contains the data and model used in the paper</p> <blockquote> <p>Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." <a href="https://arxiv.org/abs/2311.04780"><em>arXiv preprint arXiv:2311.04780</em></a> (2023).</p> </blockquote> <p>If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.</p> <p>These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on <a href="https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc">GitHub.</a></p> <p>Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the <a href="https://arxiv.org/abs/2311.04780">paper</a>.</p> <p>The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the<a href="https://www.synapse.org/#!Synapse:syn25649159/wiki/610007"> Fetal Tissue Annotation Challenge</a> (FeTA).</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p> <p>[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." <em>Nature methods</em> 18.2 (2021): 203-211.</p>
Open dataset of 20 interviews with senior service designers and managers for the Empathy Business research project
<p>The aim of the Empathy Business research project (2023-2024) led by the University of Lapland focused on how to digitalize services and business prototyping through creativity. The research named challenges as well as design methods for developing digital tools for meeting the future needs of service design and business development.<br><br>In total, 20 interviews among senior service designers and managers located in Europe, Latin America and Asia were conducted during spring 2023. The interviews provide perspectives related to the future of service design as a practise, the skills required and further issues of relevance for professionals in the field. Based on affinity diagramming eight main clusters were named: Sustainability, Business compatibility, New tools, Designer’s skills, Art-based methods, People in the centre, Online workshops, and Physical workshops.<br><br>This data set includes an anonymized list of the interviewees, affinity diagram post-it notes of the interviews, short descriptions of the main clusters and an internet link to the online affinity diagram on Miro board.<br><br>The materials provided initial insights for developing Proof-of-Concepts for digitized interfaces, such as suitable plugins, 3D-based photorealistic solutions, or an application with the potential to be used in service design and service prototyping contexts as well as in other development processes within and across organizations.</p>
REACH_OpenCall_ Applicants_Open_Database
<p>This anonymised database covers Open Calls 1, 2 and 3 of REACH Incubator. </p>
The Editors-in-Chief of SOIL ORGANISMS: Prof. Dr. Willi Xylander (Görlitz) and Prof. Dr. Nico Eisenhauer (Leipzig). in SOIL ORGANISMS - an international open access journal on the taxonomic and functional biodiversity in the soil
The Editors-in-Chief of SOIL ORGANISMS: Prof. Dr. Willi Xylander (Görlitz) and Prof. Dr. Nico Eisenhauer (Leipzig).
Scripts, Data, and Figures for "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems"
<div> <p>This archive contains the scripts required to run all calculations presented in "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems," the figure generation scripts and attendant processed data, and a copy of MesoHOPS version 1.4.0, as used in the paper.</p> </div>
Asteroseismology of the young open cluster NGC 2516 I: Photometric and spectroscopic observations
<p>A column-to-column explaination is here:</p> <p>gaia_dr3_source_id: Gaia DR3 source ID</p> <p>TIC: TIC number</p> <p>gaia_RA: RA by Gaia</p> <p>gaia_DEC: DEC by Gaia</p> <p>gaia_G_apparent_mag: Gaia G band magnitude</p> <p>gaia_G_apparent_mag_err: Gaia G band magnitude uncertainty</p> <p>gaia_G_absolute_mag: Gaia G band absolute magnitude, without the correction of extinction</p> <p>gaia_G_absolute_mag_err: uncertainty of gaia_G_absolute_mag</p> <p>log_Luminosity: log of luminosity, calculated by Gaia effective temperature, with the bolometric correction and extinction correction. Use with caution.</p> <p>log_Luminosity_err: uncertainty of log_Luminosity</p> <p>Gaia_Teff: effective temperature provided by Gaia. Use with caution.</p> <p>Gaia_Teff_err: uncertainty of Gaia_Teff. Use with caution. A uncertainty value of '100' means the temperature is absent by Gaia, so we use the temperature from the TIC input catalog.</p> <p>BP-RP: Gaia colour index.</p> <p>BP-RP_err: uncertainty of BP-RP</p> <p>Teff_by_spectra: effective temperature by FEROS spectra, better than Gaia_Teff, only available for nine stars. "9999" means no data available.</p> <p>Teff_by_spectra_err: uncertainty of Teff_by_spectra. "9999" means no data available.</p> <p>log_L_by_Teff_spectra: log of luminosity calculated by Teff_by_spectra, with the bolometric correction and extinction correction, better than log_Luminosity.</p> <p>log_L_by_Teff_spectra_err: uncertainty of log_L_by_Teff_spectra</p> <p>spectra_logg: log g by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_logg_err: uncertainty of spectra_logg</p> <p>spectra_vsini: projected equatorial velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_vsini_err: uncertainty of spectra_vsini</p> <p>spectra_matellicity: matellicity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_matellicity_err: uncertainty of spectra_matellicity</p> <p>spectra_radial_velocity: radial velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_radial_velocity_err: uncertainty of spectra_radial_velocity</p> <p>spectra_microturbulent: microturbulent velocity by FEROS spectra, only available for nine stars. "9999" means no data available.</p> <p>spectra_microturbulent_err: uncertainty of spectra_microturbulent</p> <p>spectra_SNR: signal-to-noise ratio of the FEROS spectra</p> <p>Pi0: asymptotic spacing of g modes, measured by g modes, only available for 11 stars. '9999' means no data available.</p> <p>Pi0_err: uncertainty of Pi0</p> <p>core_rotation_g_mode: near-core rotation rate in unit of days^{-1}, measured by g modes, only available for 11 stars. '9999' means no data available.</p> <p>surface_modulation_period: surface rotation period measured by surface modulations. '9999' means no data available.</p> <p>surface_modulation_period_err: uncertainty of surface_modulation_period</p> <p> </p>
Thermochemical Data for Furan-based Monomer Candidates for Frontal Ring-Opening Metathesis Polymerization (FROMP)
<p>This dataset includes 471 furan-based monomer candidates for frontal ring-opening metathesis polymerization (FROMP) and relevant thermochemistry as calculated with density functional theory (DFT). The monomer candidates were combinatorically enumerated using Diels-Alder reactions of furan derivatives as dienes and four types of dienophiles (alkenes, alkynes, allenes, and benzynes). Common substituents were enumerated for the dienophile classes, and methyl substitution on the diene was explored. We used the SMILES arbitrary target specification (SMARTS) language to produce monomers and ring-opened structures from diene and dienophile precursor SMILES, and we studied the ring-opening reaction using a homodesmotic equation with ethene. RDKit conformers were initially generated from SMILES, then optimized with GFN2-xTB. The two conformers lowest in energy were then optimized with DFT using the wb97x-D3 functional, def2-TZVP basis set, and def2/J auxiliary basis set. Gibbs free energy corrections were obtained through frequency calculations. Structures with imaginary frequencies below -50 cm^{-1} were excluded from this work, and smaller imaginary modes were flipped to be positive for free energy calculations. Modes below 50 cm^{-1} were treated with the modified rigid rotor approximation, and all thermochemical values were calculated at T=200C. The CSV file contains the monomer SMILES, the free energy of reaction for Diels-Alder addition (G_DA_200), and the enthalpy of the ring-opening reaction (H_RO_200). All energies are given in kcal/mol. An interactive HTML is also included to visualize the monomers in this dataset.</p>
Open dataset of scholars on Twitter (X)
<p>This is a version 2 dataset of paired OpenAlex author IDs (<a href="https://docs.openalex.org/about-the-data/author">https://docs.openalex.org/about-the-data/author</a>) and Twitter (now X) user IDs<br><br></p> <p><strong>Major update in this version</strong></p> <p>Following the significant update to OpenAlex's author identification system, the scholars on Twitter dataset, which previously linked Twitter IDs to OpenAlex author IDs, immediately became outdated. This called for a new approach to re-establish these links, as the absence of new Twitter data made it impossible to replicate the original method of matching Twitter profiles with scholarly authors. To navigate this challenge, a bridge was constructed between the June 2022 snapshot of the OpenAlex database—used in the original matching process—and the most recent snapshot from February 2024. This bridge utilized OpenAlex works IDs and DOIs to match authors in both datasets by their shared publications and identical primary names. When a connection was established between two authors with the same name, the new OpenAlex author ID was assigned to the corresponding Twitter ID. When direct matches based on primary names were not found, an attempt was made to establish connections by matching the names from June 2022 with any corresponding alternative names found in the 2024 dataset. This method ensured continuity of identity through the system update, adapting the strategy to link profiles across the temporal divide created by the database's overhaul.</p> <p>Our efficient method for re-establishing links between author IDs and Twitter profiles has been notably successful, managing to rematch 432,417 (88%) OpenAlex author IDs. This effort successfully restored connections for 388,968 unique Twitter users, which represents 92% of the original dataset. Of these, 375,316 were matched using their primary names, and 57,101 through alternative names. The simplicity and quick execution of this approach led to exceptionally favourable results, with a minimal loss of only 8% of the original Twitter-linked scholarly accounts.</p> <p>The dataset includes <strong> 432,417 unique author_ids</strong> and <strong>388,968 unique tweeter_ids</strong> forming <strong>462,427 unique author-tweeter pairs</strong>.</p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li><strong>authors_tweeters_2024_02.csv</strong> is the actual dataset of author IDs paired with tweeter IDs. The "alternative" column indicates if the match was made with the primary name (0) or an alternate name (1).</li> <li><strong>mapping_tweeters_2022_2024.csv</strong> contains the relationship made between the 2022 author IDs and the 2024 author IDs, including the names.</li> </ul> <p> </p> <p><strong>How to cite</strong></p> <p>When using the dataset, please cite the following article providing details about the matching process:</p> <div> <div>Mongeon, P., Bowman, T. D., & Costas, R. (2023). An open data set of scholars on Twitter. <em>Quantitative Science Studies</em>, 1–11. <div><a href="https://doi.org/10.1162/qss_a_00250">https://doi.org/10.1162/qss_a_00250</a></div> </div> </div>
Opening minds, opening doors: measuring open access friendliness of the top five pharmacy institutes (NIRF- 2023)
Open the record for dataset details and reuse information.
Einführung eines Informationsbudget im Rahmen des DFG-geförderten Projekts "Open Access-Publikationskosten"
<p>Hintergrund:<br>● Projektergebnisse sollen zum Zweck der<br>wissenschaftsadäquaten Kommunikation im Open Access<br>veröffentlicht werden<br>● sie sollen “entweder direkt in qualitätsgesicherten bzw. fachlich anerkannten<br>Open-Access-Zeitschriften od. auf Open-Access-Plattformen (...)<br>od. zusätzlich zur Verlagspublikation (...) in disziplinspezifische<br>od. institutionelle elektronische Archive (Repositorien)<br>eingestellt werden.”<br>https://www.dfg.de/resource/blob/323900/27c0c6db263ec8566d19d0b3719310ad/dfg-2-00-de-v0124-data.pdf</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.