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1,081 results for “publication data”
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2008 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Incentive mechanisms and the provision of public goods: Field experiment data for testing alternative economic frameworks to supply ecosystem restoration on Virginia's Eastern Shore: 2009 data.
This dataset consists of participant responses in one of two economic experiments conducted on Virginia's Eastern Shore during 2008 and 2009 by Elizabeth C. Smith used to gauge resident preferences and willingness-to-pay for ecosystem restoration activities. This dataset was designed to be used to examine a practical method to implement an individualized pricing approach to public good provision, grounded in Lindahl's marginal benefit theory. The study's focus was on ecosystem valuation and market approaches that have potential to provide public goods, examining the potential to generate revenues for public goods from consumers. While willingness-to-pay measurement techniques have been used to assess preferences for many environmental goods, this research goes a step further to explore real money auctions that generate revenues sufficient to pay for restoration activities. The data from the field experiments conducted in coastal Virginia were used, along with laboratory experiment data, to evaluate the performance of auction mechanisms in generating revenues relative to potential (Hicksian) willingness to pay for marginal increments in public goods. The field execution of this experiment involved residents of Virginia's Eastern Shore and local public goods. This application involved half-acre increments of ecosystem restoration for sea grass habitat in coastal lagoons, plantings for migratory bird habitat, and, in some auctions, clam-based increments of water quality services, defined as delaying the harvest of clams for six months beyond normal harvest by an existing aquaculture firm. To perform these tasks, participants were provided a budget, between $90 and $150. The auctioneer described for participants the ecosystem services that may result from additional ecosystem restoration associated with each activity. The actual levels of ecosystem restoration provided were based on aggregate offers reaching a pre-determined (but unknown to the participants) provision p
Supplementary Data to journal publication on 'The Foundations of the Patagonian Icefields'
<p>Partitioning and comparison of ice discharge estimates from the the Patagonian Icefields comprising associated uncertainties. For further details please refer to the notes in the individual files and/or consult the associated publication entitled 'The Foundations of the Patagonian Icefields' published in Communications Earth & Environment.</p>
Data associated with the following publication: Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel
<p>This data set contains the supporting data associated with the following publication:</p> <p>Morgenthaler, T., Loebach, J., Lynch, H., Pentland, D., Kottorp, A., & Schulze, C. (in press). Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel. Children, Youth and Environments. [DOI was not yet available when the data set was published]</p> <p>The data set includes the following files:</p> <ul> <li>read me file [contains all relevant information to understand and reuse this data set] </li> <li>13 additional files [for description, see read me file]</li> </ul> <p>For more information, please contact the lead researcher, Thomas Morgenthaler (tom.morgenthaler@gmail.com or 121101888@umail.ucc.ie)</p> <p> </p>
Code and data set for data analysis published as manuscript "Bacttle: a microbiology educational board game for lay public and schools"
<p>Code that processed raw data and plots the figures of the manuscript "Bacttle: a microbiology educational board game for lay public and schools"</p> <p>Below is a table with the original survey questions. The ID corresponds to the column displayed on the data set. When letters are followed by a number (1 or 2), it means that the question was answered before playing the game (1) and after playing the game (2).</p> <table> <tbody> <tr> <td> <p><em>ID<sup>1</sup></em></p> </td> <td> <p><em>Question text</em></p> </td> <td> <p><em>Possible answers<sup>2</sup></em></p> </td> </tr> <tr> <td> <p><em>A</em></p> </td> <td> <p>How old are you?</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>B</em></p> </td> <td> <p>Do you know what a bacterium is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>C</em></p> </td> <td> <p>Do you know what a bacterial capsule is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>D</em></p> </td> <td> <p>Do bacteria have tools to harm each other?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>E</em></p> </td> <td> <p>Do bacteria reproduce at the same pace?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>F</em></p> </td> <td> <p>What is sporulation?</p> </td> <td> <p>A resistant state that some bacteria can achieve under unfavorable conditions.</p> </td> </tr> <tr> <td> <p>The release of toxins by bacteria.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>G</em></p> </td> <td> <p>What are flagella used for?</p> </td> <td> <p>Sticking to surfaces.</p> </td> </tr> <tr> <td> <p>Motility in liquid environments.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>H</em></p> </td> <td> <p>What does it mean to be lithotrophic?</p> </td> <td> <p>A bacterium can get energy from minerals.</p> </td> </tr> <tr> <td> <p>A bacterium can get energy from the sunlight.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>I</em></p> </td> <td> <p>Can bacteria be infected by viruses?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>J</em></p> </td> <td> <p>Are all bacteria harmful for humans?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>K</em></p> </td> <td> <p>How many bacteria are in a coffee spoon of yoghurt?</p> </td> <td> <p>Millions</p> </td> </tr> <tr> <td> <p>Hundreds</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>L</em></p> </td> <td> <p>How easy did you find the gameplay?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>M</em></p> </td> <td> <p>Did you find the card content easy to understand?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>N</em></p> </td> <td> <p>Did you like the setup of the game?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>O</em></p> </td> <td> <p>Would you like to play this game again?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>P</em></p> </td> <td> <p>What can we improve?</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>1) Question A categorizes the player’s age; B and C assess the initial level of knowledge in microbiology (none -both questions are answered negatively-, basic -player knows what a bacterium is but not a bacterial capsule-, or advanced -both answers are positive-); questions D-I score knowledge acquisition; J and K are control questions; L-O evaluate the appreciation of the game; and P is an optional free text-entry answer for additional feedback. <br>2) y= yes, n=no, idk=I don’t know, VE=very easy, E=easy, A=adequate, D=difficult, VD=very difficult.</p>
Data for SciKit-SurgeryFRED publication "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research."
<p>This is data used in the publication;</p> <p><a href="https://www.spiedigitallibrary.org/profile/Steve.Thompson-90188">Stephen Thompson</a>, <a href="https://www.spiedigitallibrary.org/profile/Thomas.Dowrick-4289932">Tom Dowrick</a>, <a href="https://www.spiedigitallibrary.org/profile/Mian.Ahmad-4289934">Mian Ahmad</a>, <a href="https://www.spiedigitallibrary.org/profile/Jeremy.Opie-4314392">Jeremy Opie</a>, and <a href="https://www.spiedigitallibrary.org/profile/notfound?author=Matthew_Clarkson">Matthew J. Clarkson</a> "Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980U (15 February 2021); <a href="https://doi.org/10.1117/12.2580159">https://doi.org/10.1117/12.2580159</a></p> <p>Data in summerSchoolGameLogs was collected using scikit-surgeryfred: v0.0.3 summer school 2020 (2020). DOI 10.5281/zenodo.3946090</p> <p>Data in in registration_results was collected using scikit-surgeryfred: v0.0.8 browser based user interface (2020). DOI 10.5281/ zenodo.4314971</p> <p>Each directory contains Python scripts to analyse the data as described in the above paper.</p> <p> </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>
Public Available Data Set of Process Flows from Internal Physical Inspections in the Failure Analysis Laboratory
<p>This data set was generated in accordance with the semiconductor industry and contains data of certain process flows in Failure Analysis (FA) laboratories focusing on the identification and analysis of anomalies or malfunctions in semiconductor devices. It comprises logistic data about the processing steps for the so-called Internal Physical Inspection (IPI).</p><p>A so-called IPI job is given as a sequence of tasks that must be performed to complete the job they belong to. It has an assigned unique ID and timestamps indicating the submission, the end, and the deadline to be met. A job also has an IPI classification assigned to it, providing general guidelines on the operations to be performed.</p><p>Every task within a job has its own type and working time, as well as the assigned resources. There are two main resources involved:</p><p> - the equipment; the machine used to perform the task,</p><p> - the operator; the person who performed the task.</p><p>In addition, general information about the type of the device to be analyzed is also available, such as the given (anonymized) package and basictype. Data also include the number of stressed samples within a device and the samples a task is performed on.</p><p>The dataset includes data from 4 years, specifically from January 2020 to December 2022.</p><p>Finally, the exact column structure is given as follows (python 3.9.5 datatype):</p><ul><li>JOB_ID [int64]: the unique ID of the job</li><li>JOB_SUBMISSION_DATE [object]: the date of the job submission</li><li>JOB_REQ_END_DATE [object]: the required end date (deadline)</li><li>JOB_FINISH_DATE [object]: the actual end date</li><li>JOB_BASICTYPE_H [object]: the given basictype denotation</li><li>JOB_PACKAGE_H [object]: the package denotation of the device</li><li>JSH_QTY_STRESSED [float64]: number of stressed samples</li><li>TASK_SUBMISSION_DATE [object]: the date of the task submission</li><li>TASK_WORKING_TIME [float64]: the amount of time (hours) the task needs to be completed</li><li>TASK_SAMPLE_NO [object]: the samples the task was performed on </li><li>TASK_CEQ_ID [float64]: the ID of the machine used to perform the task</li><li>TASK_CTKS_ID [int64]: the ID representing the task type</li><li>TASK_USR_ID [int64]: the ID of the operator performing the task</li><li>CIPI_LEVEL_0 [object]: a series of IPI classifications, indicating what is required to execute for a specific job</li></ul>
Wind measurement data from the publication: "Development of a load model validation framework applied to synthetic turbulent wind field evaluation"
<h3>Dataset description:</h3> <p>This datasat represents supplementary material used in the contribution "Development of a load model validation framework applied to<br>synthetic turbulent wind field evaluation" by Meyer, Huhn and Gottschall.</p> <p>Wind measurements from the Testfeld BHV are made available. For installation details, see the mentioned reference.</p> <p> </p> <h3>File description:</h3> <ul> <li>Lidar_HWS.nc - Horizontal wind speed measurements (10 min averages) from a WindCube V2 vertical profiler for one day with a low-level jet occurrence ( <div> <div>2021-04-20)</div> </div> </li> <li>Cups_HWS.nc - Horizontal wind speed measurements (10 min averages) from cup anemometer installed on a met mast for the same day</li> <li>Ensemble_averaged_Spectra.nc - Ensemble averaged spectra for neutral and near neutral situations from a Gill Windmaster at 110m above ground level, used to fit the Mann and KSEC model parameters</li> </ul> <h3> </h3> <h3>Referencing:</h3> <p>When used, please cite like the following:</p> <p>Meyer, Paul J., Matthias L. Huhn, and Julia Gottschall. 2024. "Development of a Load Model Validation Framework Applied to Synthetic Turbulent Wind Field Evaluation" <em>Energies</em> 17, no. 4: 797. https://doi.org/10.3390/en17040797</p> <p> </p> <p> </p>
Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"
<p><span><span>These data are published as part of the paper: “</span><span>Exploring the microst</span><span>ructure, mechanical properties, </span><span>and corrosion resistance of innovative bioabsorbable Zn-Mg-(S</span><span>i) alloys fabricated via powder </span><span>metallurgy techniques</span><span>” published in journal: “</span><span>Journal of Materials Research and Technology</span><span>”.</span></span><span> </span></p>
Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation"
<p>Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation" (DOI: <a title="" href="https://doi.org/10.48328/tudatalib-1376">https://doi.org/10.48328/tudatalib-1376</a>)</p>
Opinions and Views of the Population of Ukraine: May 2024 (KIIS Omnibus 2024/05) – Data from a nationwide public opinion poll conducted by KIIS in May 2024
"Opinions and Views of the Population of Ukraine" is a regular omnibus survey, conducted by Kyiv International Institute of Sociology (KIIS) among Ukraine's adult population and covering a wide range of topics. The data presented here is a subset of the survey conducted in May 2024 and include KIIS's own research questions. Questions included are: readiness for concessions for peace, views on Ukraine's relationship with Russia, perceptions of the war between Russia and Ukraine, views on security agreements, perceptions of Ukrainian society's unity, attitudes toward criticism of the government, attitudes toward the legalization of medical cannabis, and perceptions of Ukraine's statehood during the Soviet era. Data collection took place from May 16 to 22, 2024, with 1,067 respondents interviewed. The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook). The Data Documentation (pdf file) also includes a short overview and discussion of survey results as well as the relevant parts of the original questionnaire.
Opinions and Views of the Population of Ukraine: February 2024 (KIIS Omnibus 2024/02) – Data from a nationwide public opinion poll conducted by KIIS in February 2024
"Opinions and Views of the Population of Ukraine" is a regular omnibus survey, conducted by Kyiv International Institute of Sociology (KIIS) among Ukraine's adult population and covering a wide range of topics. The data presented here is a subset of the survey conducted in February 2024 and include KIIS's own research questions. The questions cover the following topics: readiness for concessions for peace; perceptions of Russia, its people, and leadership; sources of information; perceptions of the war between Russia and Ukraine; views on Western support for Ukraine; factors contributing to Ukraine's success in the war; perceptions of recent investigations into large businesses and businessmen in Ukraine; state control over online information; state policy on the Russian language in Ukraine; the level of democracy in Ukraine; opportunities for personal success; and favorite national holidays. Data collection took place from February 17 to 28, 2024. Some of the survey questions were asked to all respondents (n=2,008), while others were directed to a sub-sample of 1,052 respondents. The data is available in an SAV format (Ukrainian, English) and a converted CSV format (with a codebook). The Data Documentation (pdf file) also includes a short overview and discussion of survey results as well as the relevant parts of the original questionnaire.
Data for paper publication "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3"
<p>The dataset presented here is related to the article by Leon-Marcos et al. 2025: "Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol-climate model ECHAM6.3–HAM2.3" accepted for publication in GMD. It comprises global fields of the FESOM2.1-REcoM3 biogeochemistry model tracers employed to calculate the ocean biomolecule concentration that serve as input data for the aerosol model. Additionally, the ECHAM6.3–HAM2.3 code of the marine aerosol implementation and the required scripts to run the model experiments are provided here. The aerosol-climate model simulation results of the marine aerosol emission, as well as the evaluation of the model results compared to observations, are also included. For further information, please refer to the attached data description. </p> <p> </p> <h2> </h2>
Data of publication Ultra-narrow Optical Linewidths in Rare-Earth Molecular Crystals
<p>Data corresponding to main text Figures, Extended Data figures, and Supplementary Figures in publication 'Ultra-narrow Optical Linewidths in Rare-Earth Molecular Crystals, by D. Serrano, S. Kumar Kuppusamy, B. Heinrich, O. Fuhr, M. Ruben and P. Goldner.</p>
Data to support the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362
<p>Soil organic carbon content and water content at the different pressure points, as measured by Ioanna Panagea for the publication "Soil Water Retention as Affected by Management Induced Changes of Soil Organic Carbon: Analysis of Long-Term Experiments in Europe", https://doi.org/10.3390/land10121362 from the the long term experiments belonging in some of the SoilCare project partners. </p>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
Sample data for evaluating Scholix relationship SubTypes for linked data publications
<p>Scholix links provide a standardized framework for establishing connections between research publications and their associated datasets or related data publications, thereby fostering improved discoverability, reusability, and reproducibility of research data.<br><br>This dataset aims to facilitate the evaluation of the degree of relatedness between literature publications and their associated linked data publications. It comprises 3,600 tuples, each representing a pair of a literature publication (A) and a linked data publication (B) connected through Scholix links.</p> <p><strong>Dataset Contents</strong></p> <p>1. <em>Scholix Links</em>: The dataset includes 450 Scholix links for each of the eight most frequently observed relationship types between literature and linked data publications, as expressed in the "RelationshipType - SubType" field of Scholix metadata:</p> <ul> <li>IsSupplementedBy</li> <li>IsReferencedBy</li> <li>IsRelatedTo</li> <li>References</li> <li>Documents</li> <li>Cites</li> <li>IsSupplementTo</li> <li>IsCitedBy</li> </ul> <p>2. <em>Publication Metadata</em>: In addition to the Scholix links, the dataset is augmented with metadata for each publication, including titles and author names. This metadata was harvested from the Crossref and DataCite APIs.</p> <p>3. <em>Relatedness Measures</em>: To estimate the degree of relatedness between literature and linked data publications, the dataset includes numeric measures for the similarity of authors' lists and publication titles for each tuple.</p> <p><strong>Data Sources</strong></p> <ul> <li>Scholix links were harvested from the Scholexplorer API.</li> <li>Publication metadata (titles and author names) were obtained from the Crossref and DataCite APIs.</li> </ul> <p>This dataset can be valuable for researchers and practitioners working on linked literature and data publications, evaluating the quality of existing links, or developing algorithms to identify related publications across different domains.</p>
Figures in Scientific Open Access Publications - Underlying Data
<p>This publication contains data for a statistical analysis of an OA article corpus. The underlying dataset consists of over 1 million open access articles from different publishers (Copernicus: 9592; Springer:78418; Hindawi: 147848; Frontiers: 57621; PMC (aggregator): 747839)</p>
Modelling of ready biodegradability based on combined public and industrial data sources
<p>The European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) Regulation, requires marketed chemicals to be evaluated for Ready Biodegradability (RB). In-silico prediction is a valid alternative to expensive and time-consuming experimental testing. However, currently available models may not be relevant to predict compounds of industrial interest, due to accuracy and applicability domain restriction issues.</p> <p>In this work we present a new and extended RB dataset (2830 compounds), issued by the merging of several public data sources. It was used to train classification models, which were externally validated and benchmarked against already-existing tools on a set of 316 compounds coming from the industrial context. New models showed good performances in terms of predictive power (BA = 0.74 – 0.79) and data coverage (83 – 91 %).</p> <p>The Generative Topographic Mapping approach was employed to compare the chemical space of the various data sources: several chemotypes and structural motifs unique to the industrial dataset were identified, highlighting for which chemical classes currently available models may have less reliable predictions.</p> <p>Finally, public and industrial data were merged into Global dataset containing 3146 compounds and including a significant subset of compounds coming from the industrial context. This is the biggest dataset reported in the literature so far which covers some chemotypes absent in the public data. Thus, predictive model developed on the Global dataset has much larger applicability domain than related models built on publicly available data. The developed model is available for the user on the Laboratory of Chemoinformatics website.</p> <p>This dataset is only the "All-Public" set, since the industrial compounds cannot be disclosed.</p> <p>This update contains additional entries from [J. Chem. Inf. Model. 52 (2012), pp. 655–669] and [J. Chem. Inf. Model. 53 (2013), pp. 867–878]</p>
ScienceDex guides
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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.