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773 results for “Data Science”
Data supplement for "Alignment of scanning lidars in offshore wind farms" - Wind Energy Science Journal
<p>These data are supplements for the calculations of the methods from the article "Alignment of scanning lidars in offshore wind farms".<br> The data was used to produce the results from the publication and is intended to be used here as sample data for illustrative purposes.</p>
GESIS - Leibniz Institute for the Social Sciences data access categories
<p>Replication code for extracting and analysing data access categories from the oai-pmh feed provided by the GESIS - Leibniz Institute for the Social Sciences DBK data catalogue. The code utilises the dc_oai-de feed to extract metadata about objects in the data catalogue, this is then edited to retain and summarise information on the four data access categories used by the archive. The oai-pmh metadata is available from GESIS under a CC0 licence.</p> <p>The .csv files extracted from the oai-pmh feed and edited to correct for missing records is also included for replication.</p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (https://frictionlessdata.io/specs/tabular-data-package/), holds the measurements of 35 known metabolites(all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in one Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and one organism part (annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable STATO terms. The measurements over these metabolites, which were made in 2 distinct experiments, were extracted from: a supplementary material table, available from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip and published alongside the Nature Genetics manuscript identified by the following doi: https://doi.org/10.1038/s41588-018-0110-3, published in June 2018 a supplementary material table available as a pdf from 'Biosynthesis of monoterpene scent compounds in roses' by Magnard et al, Science 03 Jul 2015 identified by the following doi: https://doi.org/10.1126/science.aab0696. This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR)and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.It is associated to the following project: https://github.com/proccaserra/rose2018ng-notebook with all the necessaryinformation, executable code and tutorials in the form of Jupyter notebooks.</p>
Data for "Breaking the Paywall: The role of Open Journal System as key Open Science infrastructure"
<h3><strong>Context</strong></h3> <p>This research was conducted within the NSF-SEEKCommons Project, a research initiative dedicated to supporting Open Science and Open Access in disciplinary research. The project has a special interest in understanding the role that critical infrastructure has in supporting open initiatives. The Open Journal System (OJS) serves as a long-standing fundamental piece for Open Access throughout the globe. Hence, it provides valuable information about experiences developing, deploying, and maintaining open technologies. </p> <h3><strong>Methods<br></strong></h3> <div> <div>We used mixed methods for our research, triangulating repository data, installation data, interviews, and documentary analysis. We collected repository data using a report generator (Kopp [2018] 2024) that uses repository metadata to present general statistics about a Git project. The resulting information was manually curated, disambiguated, and annotated to have a homogeneous set of developers with information about their institutional affiliation and country. </div> <div> </div> <div>Names are normalized based on the information in qualitative interviews and by browsing the full-extent commits in the GitHub repository. Other sources for this were the institutional materials (available in current and archived versions of the PKP website), meeting minutes, the user forum, and further project documentation available online. GitHub handles are homologated to their most comprehensive version. For institutional and country affiliation, we resorted to GitHub profiles, PKP documentation and forums, institutional domains available in emails, and researchers' ORCID IDs. </div> </div> <h3><strong>Available files</strong></h3> <ol> <li><strong>Information about the codebase</strong> (number of files, lines of code, and timestamp) organized by <strong>month, quarter, and semester. </strong><br>See file: OJS_GitStats_04-24.csv</li> <li>Information about the historical evolution of the codebase (number of files, lines of code, and timestamp), including <strong>a description of the top committers for each month</strong>. Commiters are described by including their institutional affiliation and country of origin. <br>See file: OJS_DevStats_Institution-Country_1.tsv</li> <li>Information about the <strong>historical evolution of the codebase </strong>focusing on <strong>top committers</strong>, along with their institution and country. This file is formatted to map the co-occurrence of developers and attributes by month between 2004-2024.<br>See file: OJS_DevStats_Institution-Country_2.tsv</li> <li>Selected fields to describe<strong> working and regularly maintained plugins for OJS as of October 2024.</strong> Includes name of the plugin, homepage, description, maintainer, and institutional affiliation. <br>See file: OJS_Plugins_2024_Processed.tsv</li> <li>Details of the aggregated <strong>information</strong> included in <strong>Table</strong> <strong>5</strong> of the article.<br>See file: OJS_Plugins_2024_Table5.tsv</li> <li><strong>Snapshot</strong> to XML information of the <strong>plugin gallery of OJS </strong>(October 21) retrieved from PKP website (Smecher 2024)<br>See file: OJS_Plugins_2024.csv</li> </ol> <h3>Funding</h3> <p><span>The SEEKCommons Project is funded by the U.S. National Science Foundation (NSF), grant #2226425</span></p>
COSN paper data (The Chinese Open Science Network (COSN): Building an Open Science community from scratch)
<p>This is the dataset for generating figure1 and figure 3 in the manuscript <em>The Chinese Open Science Network (COSN): Building an Open Science community from scratch </em>(Accepted by AMPPS). Preprint at: <a href="https://doi.org/10.31234/osf.io/ac9by">https://doi.org/10.31234/osf.io/ac9by</a>.</p> <p>All the data and codes are available in repo: <a href="https://github.com/OpenSci-CN/COSN_AMPPS_Paper">COSN_AMPPS_Paper</a> Accepted Version.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Data on a citation context analysis focusing on natural sciences and social sciences and humanities
<p>This dataset contains data on citation context analysis between natural sciences (NS) and social sciences and humanities (SSH). In particular, the data were created through manual coding of each citation between papers related to SDG7 (renewable energy) and SDG13 (climate change) and papers cited by them. This dataset consists of 9 files, associated with the article: Nishikawa, K. How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities. Scientometrics (2023). <a href="https://doi.org/10.1007/s11192-023-04664-y">https://doi.org/10.1007/s11192-023-04664-y</a></p> <p> </p> <p>The files are numbered as follows:</p> <ul> <li>00 – README</li> <li>01 – Data by citation pair for SDG7 (original)</li> <li>02 – Data by citation pair for SDG13 (original)</li> <li>03 – Data by mention location for SDG7 (original)</li> <li>04 – Data by mention location for SDG13 (original)</li> <li>05 – Data by citation pair for SDG7 (additional)</li> <li>06 – Data by citation pair for SDG13 (additional)</li> <li>07 – Data by mention location for SDG7 (additional)</li> <li>08 – Data by mention location for SDG13 (additional)</li> </ul> <p>See README for more information.</p>
Austrian Science Fund (FWF) Publication Cost Data 2014
<p>Following 2013 (http://dx.doi.org/10.6084/m9.figshare.988754), the Austrian Science Fund (FWF) makes its publication costs spent in 2014 (esp. for Open Access) publically available.</p> <p>The dataset includes payments for publications of authors funded by the Austrian Science Fund (FWF) via following programmes:</p> <p>"Peer-Reviewed Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/peer-reviewed-publications/</p> <p>"Stand-Alone Publications": https://www.fwf.ac.at/en/research-funding/fwf-programmes/stand-alone-publications/</p> <p>In addition to 2013, this dataset includes also costs for Open Access books and other venues.</p>
MARCSI - Inventory of Marine Citizen Science Initiatives and the FAIRness of the data they produce
<p>Inventory (data set) of Marine Citizen Science Intiatives collected and described in the publication entitled "Past and present marine citizen science around the globe: a cumulative inventory of initiatives and data produced" co-authored by Uta Wehn, Ane Bilbao, Luke Somerwill, Torsten Linders, Joan Maso, Stephen Parkinson, Christina Semasingha,<sup> </sup>Sasha Woods.</p>
Data of Survey on National Contributions to EOSC and Open Science 2023
<p>This is the data set of the annual survey on National Contributions to EOSC and Open Science 2023 for the EOSC Steering Board</p> <p>The annual survey on National Contributions to EOSC and Open Science was developed by the EOSC Future project and EOSC Steering Board to monitor policies, practices, and impacts related to EOSC and Open Science at national and institutional levels in Europe</p> <p>The annual survey for 2023 was published in the EOSC Open Science Observatory on 17 January 2024 and ran until 01 July 2024 whereby 32 European member states, associated countries, and other countries responded to the survey</p> <p>The data of the annual survey for 2023 is available and exploitable in the online dashboard of the EOSC Open Science Observatory developed by Technopolis Group and OpenAIRE in the EOSC Future project and continued in the EOSC Track project: [<a href="https://eoscobservatory.eosc-portal.eu">https://eoscobservatory.eosc-portal.eu</a>]</p> <p>Disclaimer 1: The annual survey on National Contributions to EOSC and Open Science is in an initial stage of implementation and will be improved in future iterations whereby the data should for now be taken as a best-effort attempt by participating countries</p> <p>Disclaimer 2: V1 of the data set included an error in the data set and has thus been restricted and replaced by an updated V2 of the data set</p>
Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
Supplementary data to Dating the timbers from the 'Sparrow-Hawk', a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports 103374
<p>This record gives access to all supplementary data that forms the background to the paper: Daly, A., Hocker, F. & Mires, C., 2022. Dating the timbers from the ‘Sparrow-Hawk’, a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports https://doi.org/10.1016/j.jasrep.2022.103374</p> <p>In 1626, a vessel making its way to Virginia was forced off course and damaged in a storm, which drove the ship onto the eastern shore of the Cape Cod peninsula, Massachusetts. Onboard were two English merchants and some servants and farmers, many of whom were Irish. In 1863, a storm exposed the weathered remains of a vessel at Old Ship Harbor. At the time, it was hailed as the same ship that had brought the Virginia-bound passengers to Plymouth in 1626. Recent wiggle-match C14 dating and dendrochronology suggests that this is indeed a ship from the early seventeenth century.</p>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in Asia
<p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In Asia, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in Asian countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in Asia' at <a href="https://doi.org/10.5281/zenodo.12566244">https://doi.org/10.5281/zenodo.12566244</a></p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Frictionless Tabular Data Package for GC-MS Rose scent profile data for Data published in Nature genetics, June, 2018 & Science, July 2015
<p>This dataset, in the form of a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holds the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. </p> <p>The data were extracted from:</p> <ul> <li>a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a> and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018</li> <li>a supplementary material table available as a pdf from "Biosynthesis of monoterpene scent compounds in roses" by Magnard et al, Science 03 Jul 2015 identified by the following doi: <a href="https://doi.org/10.1126/science.aab0696">https://doi.org/10.1126/science.aab0696</a></li> </ul> <p>This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science.</p> <p>It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.</p> <p> </p>
Data related to the article: Y. Norman et al., Science 365, eaax1030 (2019)
<p>This data set contains intracranial EEG data, analysis code and results associated with the manuscript, "<strong>Hippocampal Sharp-wave Ripples Linked to Visual Episodic Recollection in Humans</strong>". [DOI: 10.1126/science.aax1030]</p> <p>Data files (.mat) and associated scripts (.m) are divided into folders according to the subject of the analysis (e.g. ripple detection, ripple-triggered averages, multivariate pattern analysis etc.) and are all contained in the .zip file: “Norman_et_al_2019_data_and_code_zenodo.zip".</p> <p>The code is written in Matlab R2018b and run on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM.</p> <p>Matlab's Signal Processing Toolbox is required.</p> <p><strong>General notes:</strong></p> <p>1) The data does not contain identifying details about the patients, nor voice recordings.</p> <p>2) Before running the analyses, make sure you set the correct paths in the "startup_script.m" located in the main folder where the zip file was extracted.</p> <p>3) To run the code, the following open-source toolboxes are required:</p> <ul> <li><strong>EEGLAB</strong> (<a href="https://sccn.ucsd.edu/eeglab/download.php">https://sccn.ucsd.edu/eeglab/download.php</a>), version: "eeglab14_1_2b". <ul> <li>A. Delorme, S. Makeig, EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. <em>J. Neurosci. Methods</em>. <strong>134</strong>, 9–21 (2004).</li> </ul> </li> <li><strong>Mass Univariate ERP Toolbox</strong> (<a href="https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox">https://openwetware.org/wiki/Mass_Univariate_ERP_Toolbox</a>), version: "dmgroppe-Mass_Univariate_ERP_Toolbox-d1e60d4". <ul> <li>D. M. Groppe, T. P. Urbach, M. Kutas, Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review. <em>Psychophysiology</em>. <strong>48</strong>, 1711–1725 (2011).</li> </ul> </li> </ul> <p>*** Make sure you download the relevant toolboxes and save them in the "path_to_toolboxes" before running the analysis scripts (see "startup_script.m")</p> <p>4) Code developed by other authors (redistributed here as part of the analysis code):</p> <ul> <li>DRtoolbox (https://lvdmaaten.github.io/drtoolbox/), version: 0.8.1b. <ul> <li>L.J.P. van der Maaten, E.O. Postma, and H.J. van den Herik. <strong>Dimensionality Reduction: A Comparative Review</strong>. Tilburg University Technical Report, TiCC-TR 2009-005, 2009.</li> </ul> </li> <li>Scott Lowe / superbar (<a href="https://github.com/scottclowe/superbar">https://github.com/scottclowe/superbar</a>), version: 1.5.0.</li> <li>Oliver J. Woodford, Yair M. Altman / export_fig (<a href="https://github.com/altmany/export_fig">https://github.com/altmany/export_fig</a>).</li> </ul>
Raw Data for Mapping Repositories and their Institutional Open Science Policies in the Middle East and North Africa (MENA)
<div> <p>Persistent Identifiers (PIDs), particularly Digital Object Identifiers (DOIs), are crucial for establishing a robust and globally accessible research infrastructure. In the Middle East and North Africa (MENA) region, a diverse array of research outputs and resources are produced and published in repositories. However, a significant number of these repositories, and outputs remain undiscoverable in global registries and aggregators. <br><br>These three datasets provides comprehensive information on the adoption of repositories, Open Access mandates, and DOIs adoption in MENA countries. It includes detailed records from different registry sources and repository platforms.<br><br>You can read the full report titled 'Mapping Repositories and their Institutional Open Science Policies in MENA' at <a href="https://doi.org/10.5281/zenodo.11370031">https://doi.org/10.5281/zenodo.11370031</a></p> </div>
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (<a href="https://doi.org/10.5194/essd-13-4529-2021">https://doi.org/10.5194/essd-13-4529-2021</a>).</p> <p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution.<br> LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (<a href="https://doi.org/10.5194/hess-21-5293-2017">https://doi.org/10.5194/hess-21-5293-2017</a>), Chile (<a href="https://doi.org/10.5194/hess-22-5817-2018">https://doi.org/10.5194/hess-22-5817-2018</a>), Brazil (<a href="https://doi.org/10.5194/essd-12-2075-2020">https://doi.org/10.5194/essd-12-2075-2020</a>), Great Britain (<a href="https://doi.org/10.5194/essd-12-2459-2020">https://doi.org/10.5194/essd-12-2459-2020</a>) and Australia (<a href="https://doi.org/10.5194/essd-13-3847-2021">https://doi.org/10.5194/essd-13-3847-2021</a>), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.</p> <p>We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.</p> <p><strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>.</p> <p><strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (<a href="https://www.gkd.bayern.de/en/rivers/discharge/tables">https://www.gkd.bayern.de/en/rivers/discharge/tables</a>) and Baden-Württemberg (<a href="https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer">https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer</a>). Runoff data from the Czech Republic may not be used to set up operational warning systems (<a href="https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf">https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf</a>).</p> <p><strong>License: </strong>This work is licensed with CC BY-SA 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Additional references: </strong>We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying <a href="https://doi.org/10.5194/essd-13-4529-2021">paper</a>.</p> <p><strong>Supplements: </strong>We have created additional files after publication (therefore non peer-reviewed):<br> 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge.<br> 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes.<br> If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at</p>
Si data files for Galaxy materials science tutorials
<p>This is a training dataset for use in Galaxy materials science tutorials. These files can be used to demonstrate the AIRSS (Ab-Initio Random Structure Searching) method for finding muon stopping sites, using the UEP (Unperturbed Electrostatic Potential) technique for the optimisation stage of that method.</p> <p>The files included are:</p> <ul> <li><strong>Si.cell:</strong> structure file containing atom locations</li> <li><strong>Si.den_fmt:</strong> electron density data, generated with CASTEP</li> <li><strong>Si.castep:</strong> CASTEP log file for the electron density calculation</li> <li><strong>Si-muairss-uep.yaml:</strong> configuration file for the AIRSS / UEP workflow</li> </ul>
Data from "PathOS - D1.2 Scoping Review of Open Science Impact"
<p>This dataset contains the data from the Scoping Review of Open Science Impact. Included are all record for which we assessed the full-text.</p> <p>The columns are as follows:</p> <ul> <li>id: Internal identifier</li> <li>Several metadata columns from Scopus/Web of Science: authors, year, title, abstract, type, DOI</li> <li>OS type: type of Open Science (e.g., Open Access, Citizen Science)</li> <li>inclusion_status: included, duplicate, out of scope, non-english</li> <li>justification: reasons for decision on inclusion_status</li> <li>Several columns with data extracted by the authors: Study details and design, Types of data sources, Study aims, Relevance to which aspect of impact, Key findings, Coverage/Context, Confidence assessment</li> </ul> <p>A complete description of the methods and detailed instructions for coders for extracting data from reports is contained in section 2 of the deliverable report which is available at <a href="https://doi.org/10.5281/zenodo.7883699">https://doi.org/10.5281/zenodo.7883699</a>.</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.