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3,673 results for “, Practices”
Soil microbial and physicochemical data from watersheds impacted by different management practices or wildfire in the Southern Appalachian Mountains, 2023
Four forested watersheds in Western North Carolina with different management practices or disturbance were sampled in the summer of 2023 to compare soil physicochemical, microbial, and functional differences. These data include mineral soil physicochemical properties (location, elevation, aspect, gravimetric moisture content, pH, total carbon and nitrogen, total organic carbon, dissolved organic carbon and nitrogen, total dissolved nitrogen, dissolved inorganic nitrogen (NO3 and NH4), and microbial biomass carbon and nitrogen), soil microbial properties (16S ASV community sequences, ITS ASV community sequences, extracellular enzyme activity, carbon mineralization rates, and ammonium mineralization rates), and organic soil properties (total organic carbon, total carbon and nitrogen, 16S ASV sequences, pH, and moisture). Together, this dataset provides context to understanding the impacts of different management practices and relevant disturbances, such as severe wildfire, on soil in the Southern Appalachian region.
WSC 2007 - 2012 Yahara Watershed surface water quality policies and practices created and implemented by public agencies
This dataset was created June 2012 - August 2013 to contribute to research under the Water Sustainability and Climate project. Interventions collected are those land-based policies and practices written and implemented by public agencies. Policies were implemented in Wisconsin's Yahara Watershed the period 2007-2012. They aim to improve surface water quality through nutrient (phosphorus and nitrogen) and sediment reduction. Interventions included in the mapping must have spatially-explicit, publicly available data through personal communication or website.
Quantitative Assessment of Research Data Management Practices - 2023
<p>This survey investigates <strong>Research Data Management (RDM) practices across five Swiss higher education institutions</strong>, including EPFL, ETH Zürich, Eawag, FHNW, and DaSCH, with the goal of gathering insights into how researchers manage data and code throughout the lifecycle of their projects, as well as using such findings to inform academic services related to RDM for researchers. Previous surveys, conducted at EPFL in 2017, 2019, and 2021, primarily focused on the planning and publishing stages of the research data lifecycle, such as data management planning and open data dissemination. The 2023 edition expanded to other institutes and places a stronger emphasis on <strong>Active Data Management</strong>, particularly during research projects, including a range of topics such as:</p> <ul> <li>Storage and backup solutions</li> <li>Data and code sharing platforms</li> <li>Documentation and metadata usage</li> <li>Compliance with legal and ethical standards</li> <li>Long-term data preservation strategies</li> <li>Use of open formats and open-source software</li> <li>Adoption of Data Management Plans (DMPs)</li> </ul> <p>This dataset was collected using the SurveyHero platform in compliance with GDPR and Swiss FADP regulations. enuvo GmbH acted as the data processor under a signed Data Processing Agreement. No personal identifiable information was purposefully collected, and data has been aggregated to further ensure respondents’ privacy.</p> <p>Included in this dataset:</p> <ul> <li>A CSV and XLSX file with the aggregated, anonymized data from the survey.</li> <li>Two PDF files containing graphical representations of the survey results, automatically generated by the SurveyHero platform in portrait and landscape mode.</li> <li>A README file providing context.</li> </ul> <p>This dataset is made openly available under the CC-BY 4.0 license. Users are encouraged to reuse it with appropriate attribution.</p>
Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report
<p>This is a comprehensive data repository of the <em>data management survey</em> carried out in Autumn of 2023 through a collaboration between the <a href="https://opensciencestudies.eu/">PHIL_OS</a> project and the <a href="https://agres.elgo.gr/">Research Directorate of the Hellenic Agricultural Organization ELGO-DIMITRA</a>.</p> <p>Please cite as: </p> <blockquote> <p>Tsiroukis F., Leonelli S. and ELGO-DIMITRA (2024) <em>Dataset for ELGO-DIMITRA Data Management Practices & Requirements: A Scoping Report.</em> PHIL_OS Report. DOI: 10.5281/zenodo.14003418</p> </blockquote>
A Decade of Progress: Open Data Practices in Bioscience at the University of Edinburgh
<p><strong>General Information:</strong></p> <p>This reposotory contains the outcomes of a project executed at the Biosciences Institutes of the University of Edinburgh. This research project assesses the openness and FAIRness (Findable, Accessible, Interoperable, and Reusable) of data linked to publications from these institutes. Here, you will find datasets, analytical codes, and figures that detail our project’s methodology and results aiming to enhance data-sharing practices and promote the adherence to FAIR principles within and beyond our community. </p> <p>This repository is linked to a publication that has been submitted to: Proceedings of the Royal Sociaty B - Biological Sciences</p> <p>The main project: You can find the main repository and workspace of this project on Github containing the data and code of this project and all the previous related projects: <a href="https://github.com/BioRDM/InsightsOfOpenPracticesInBiosciences">Here</a></p> <p><strong>The Protocol:</strong></p> <p>The protocol for this project can be found on Protocol.io, where detailed step-by-step guidelines are provided to ensure that the research methods are transparent and reproducible. <a href="https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2" rel="nofollow">https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2</a></p> <p>The main project</p> <p><strong>Contact us:</strong></p> <p>for General Queries, Collaboration and Data Management: <em>bio_rdm@ed.ac.uk (<a href="https://biology.ed.ac.uk/research/facilities/research-data-management">BioRDM</a>) </em>or the Principal Investigator and Corresponding Author: Andrew Millar (<em>andrew.millar@ed.ac.uk</em>) - Orcid: 0000-0003-1756-3654</p> <p><strong>Data Collection</strong></p> <p>The Dataset of this project was collected in two different periods by the honour students (Creasey, de Ugarte, Strevens, Usman, Yun Wong) in our department:<br>- Project one from Januray 2023 to June 2023<br>- Project two from January 2024 to June 2024</p>
Data from: ChatGPT performance on radiation technologist and therapist entry to practice exams
<p>This dataset contains the data needed to reproduce all results and figures described in "ChatGPT performance on radiation technologist and therapist entry to practice exams".</p> <p>Details about the data collection can be found in the paper referenced below. Briefly, ChatGPT (GPT-4) was prompted with multiple choice questions from 4 practice exams provided by the Canadian Association of Medical Radiation Technologists (CAMRT). ChatGPT was promted with the questions from each exam 5 times between July 17 and August 13, 2023. Table 1, below, provides details about the dates for data collection.<br><br></p> <p><strong>Variable descriptions</strong></p> <ul> <li><code>question</code>: Question number, provided by CAMRT. Skipped question numbers indicate image-based questions that were excluded from the study.</li> <li><code>discipline</code>: Indicates the CAMRT exam discipline, abbreviated as follows <ul> <li>RAD: radiological technology</li> <li>MRI: magnetic resonance</li> <li>NUC: nuclear medicine</li> <li>RTT: radiation therapy</li> </ul> </li> <li><code>question_type</code>: Indicates the type of competency being assessed by the question (Knowledge, Application, or Critical thinking). Competency categories were assigned by CAMRT.</li> <li><code>corrrect_response</code>: The correct multiple choice response ("A", "B", "C", or "D"), assigned by CAMRT.</li> <li><code>attempt1-5</code>: ChatGPT's response to the multiple choice questions for attempts 1 through 5, indicated using the letters "A", "B", "C", or "D". In a few cases, ChatGPT did not provide a reference to a multiple choice response and "NA" is recorded in the dataset. </li> </ul> <p><em>Note: The long-form questions from CAMRT and answers provided by ChatGPT are not available as a part of this dataset.<br><br></em></p> <p><strong>Table 1</strong>: Dates for data collection</p> <table> <tbody> <tr> <td> </td> <td><strong>Attempt 1</strong></td> <td><strong>Attempt 2</strong></td> <td><strong>Attempt 3</strong></td> <td><strong>Attempt 4</strong></td> <td><strong>Attempt 5</strong></td> </tr> <tr> <td><strong>Radiological technology</strong></td> <td>2 Aug 2023</td> <td>2 Aug 2023</td> <td>8 Aug 2023</td> <td>9 Aug 2023</td> <td>11 Aug 2023</td> </tr> <tr> <td><strong>Magnetic resonance </strong></td> <td>17 Jul 2023</td> <td>18 Jul 2023</td> <td>18 Jul 2023</td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> </tr> <tr> <td><strong>Nuclear medicine</strong></td> <td>8 Aug 2023</td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> </tr> <tr> <td><strong>Radiation therapy</strong></td> <td>9 Aug 2023</td> <td>12 Aug 2023</td> <td>12 Aug 2023</td> <td>13 Aug 2023</td> <td>13 Aug 2023</td> </tr> </tbody> </table> <p> </p>
Datasets for practical model selection for prospective virtual screening
<p>This repository contains datasets for the manuscript "Practical model selection for prospective virtual screening":</p> <ul> <li><strong>pria_rmi_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets. The files also contain the associated continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation.</li> <li><strong>pria_rmi_pcba_cv.tar.gz</strong>: A compressed directory containing chemical screening data for the <strong>PriA-SSB AS</strong>, <strong>PriA-SSB FP</strong>, and <strong>RMI-FANCM FP</strong> binary datasets as well as public PubChem BioAssay datasets. The files also contain the PriA-SSB and RMI-FANCM continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints. The dataset has been split into five folds for cross validation. Missing values are left blank.</li> <li><strong>pria_prospective.csv.gz</strong>: A compressed file containing chemical screening data for the binary dataset <strong>PriA-SSB prospective</strong>. The file also contains the continuous % inhibition values and chemical features represented as SMILES and Morgan fingerprints.</li> </ul> <p>If you use these data in a publication, please cite:</p> <p>Shengchao Liu<sup>+</sup>, Moayad Alnammi<sup>+</sup>, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. Practical Model Selection for Prospective Virtual Screening. Journal of Chemical Information and Modeling. 2018 <a href="https://doi.org/10.1021/acs.jcim.8b00363">doi:10.1021/acs.jcim.8b00363</a></p> <p>PubChem data were provided by the <a href="https://pubchem.ncbi.nlm.nih.gov/">PubChem database</a>. Follow the <a href="https://pubchemdocs.ncbi.nlm.nih.gov/citation-guidelines">PubChem citation guidelines</a> if you use the PubChem data. See <a href="https://doi.org/10.1177/2472555217712001">Voter et al. 2017</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1272365">1272365</a>) for the PriA-SSB screening data and <a href="https://doi.org/10.1177/1087057116635503">Voter et al. 2016</a> (PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1159607">1159607</a>) for RMI-FANCM.</p> <p>Version 1.1.0 updates all of the data files. We standardized the SMILES in all files by generating canonical SMILES with RDKit version 2016.03.4. In addition, we removed 2845 chemicals from pria_prospective.csv.gz that were duplicates of compounds in pria_rmi_cv.tar.gz.</p>
GoNEXUS SEF application: Andalusia practical case study
<p>Data for the figures in the journal article:</p> <p>Gonzalez-Rosell A, Arfa I and Blanco M (2023) Introducing GoNEXUS SEF: a solutions evaluation framework for the joint governance of water, energy, and food resources. Sustainability Science. https://doi.org/10.1007/s11625-023-01324-1</p> <p>Figure 3. Generation of new quantitative evidence: Percentage of variation of indicators between the water price scenario (WP) and baseline scenario (BS) for the year 2030. Source: Own elaboration based on the participatory SDM results (González-Rosell et al., 2020).</p> <p>Figure 4. Cross-impact matrix of the indicator system and solution from Table 2 for the water pricing in the Andalusia case study. Source: Own elaboration.</p> <p>Figure 5. Analysis of the degree of distribution of the network system based on the cross-impact matrix in Figure 4. Source: Own elaboration.</p> <p>Figure 6. (a) Full network graph: links between 16 indicators and WP solution based on the cross-impact matrix. (b) Tree network graph of the total influence of the WP solution at the first and second order based on the cross-impact matrix. Colour scale as in</p> <p>Figure 8. Synergies (green) and trade-off (red) of the WP solution on policy objectives and nexus objectives in Andalusia. Source: Own elaboration.</p> <p> </p>
Retrieval practice facilitates memory updating by enhancing and differentiating medial prefrontal cortex representations
Open the record for dataset details and reuse information.
IPBES Data Management Tutorials - Session 6.1: Data management best practices
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The chapter on<em> Examples of implementing the IPBES data management Policy</em> contains examples of how certain data management tasks and workflows were implemented within IPBES so that they follow the data management policy. <strong>Currently, this chapter contains legacy videos and the most recent examples can be found within the IPBES technical guidelines here:</strong> <a href="https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines">https://ict.ipbes.net/ipbes-ict-guide/data-management/technical-guidelines</a></p> <p>This session,<em> data management best practices</em>,<em> </em>provides a general review of some best practices of data management and what to expect for this chapter.</p>
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Data from DIAMAS follow-up survey about funding practices
<p>The WP5 team of the DIAMAS project designed a follow-up survey to investigate the funding <br>practices of IPSPs more deeply. We examined the proportion of Diamond publishing within the <br>same IPSP by output type, and the capability to plan for the future. We also enquired about <br>spending priorities, reasons for fundraising and the amount of work required, and asked about <br>views on institutional publishing funding.</p> <p><br>The project sent the follow-up survey to respondents of the DIAMAS survey (metadata and <br>aggregated data available <a href="../records/10590503">here</a>) who agreed to be contacted. Emails used unique identifiers, <br>enabling us to merge databases and easily recover information gathered from the first survey <br>for more advanced cross-analysis.</p> <p><br>This follow-up survey was open during the last two months of 2023 and successfully garnered <br>469 answers. After cleaning (mainly deleting blank surveys and duplicates), we retained 383 <br>relevant answers, a response rate of 56%.</p>
Dataset - paper: Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors
<p>Dataset corresponding to a paper that has been accepted for publication in Appetite (Philippe, K., Chabanet, C., Issanchou, S., Grønhøj, A., Aschemann-Witzel, J., & Monnery-Patris, S. (2022, in press). <em>Parental feeding practices and parental involvement in child feeding in Denmark: gender differences and predictors</em>. Appetite).</p> <p>The objectives of the study were (1) to examine possible differences between Danish mothers and fathers with regard to their involvement in child feeding and their feeding practices, and (2) to identify possible parent-related predictors of parental feeding practices and of parental involvement in child feeding at home.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-Denmark.docx".</p>
Dataset - paper: Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (How) did they change? </em>Appetite. 2021 Jun 1;161:105132. doi: <strong>10.1016/j.appet.2021.105132</strong>. Epub 2021 Jan 23. PMID: 33493611; PMCID: PMC7825985).</p> <p>The objective of the study was to evaluate possible changes in eating behaviors in children aged 3–12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-COVID.docx".</p>
Dataset - paper: Are food parenting practices gendered?
<p>Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. <em>Are food parenting practices gendered? Impact of mothers' and fathers' practices on their child's eating behaviors</em>. Appetite. 2021 Nov 1;166:105433. doi: <strong>10.1016/j.appet.2021.105433</strong>. Epub 2021 Jun 1. PMID: 34087257).</p> <p>The objectives of the study were (1) to identify possible gender differences regarding parental feeding practices and styles and parental perceptions of the child’s eating behaviours, and (2) to assess the associations between maternal and paternal feeding practices and styles and child eating behaviours, and to study possible effects of concordant/discordant feeding practices in families.</p> <p>Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-couples.docx".</p>
Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs
<p>Datasets describing the fungal species diversity, microbial density and acidity of French sourdoughs, phenotypic variation of Kazachstania bulderi and Kazachstania humilis strains as well as the diversity of bread-making practices of 40 bakers and farmers-bakers.The data were collected, analyzed, and reported within the following publication :</p> <p>Elisa Michel, Estelle Masson, Sandrine Bubbendorf, Léocadie Lapicque, Thibault Nidelet, Diego Segond, Stéphane Guézenec, Thérèse Marlin, Hugo deVillers, Olivier Rué, Bernard Onno, Judith Legrand, Delphine Sicard and the participating bakers: <strong>Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs</strong>. PCI Evol. Biol.</p> <p> </p>
Data for "Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis"
<p>Supplementary Files for systematic review and meta-analysis: Temperate Regenerative Agriculture practices increase soil carbon but not crop yield – a meta-analysis</p> <p> </p>
FolkArtiNet: Folk music groups: their artistic practice and infrastructural needs in the COVID-19 era and beyond - survey data
<p> The online survey was one of three methods used for collecting information about the infrastructural needs of the folk music groups. It included a series of questions about different areas of artistic activity, such as working on repertoire, collaboration among group members, storage and sharing of data, and organization of artistic events. The survey data includes all questions and answers in csv format.</p>
Dataset from the Survey on Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice
<p>This database contains all the responses from the participants in the survey: Industry 5.0 Concepts and Enabling Technologies, Towards an Enhanced Conservation Practice.</p> <p>The main purpose of this survey was to explore how the Architecture, Engineering, Construction, Management, Operation, and Conservation (AECMO&C)<br>industry can adapt and better prepare to embrace the innovative principles and enabling technologies of Industry 5.0. This could ultimately result in<br>enhanced conservation practices for built cultural heritage.</p> <p> </p>
Navigating protected areas networks for improving diffusion of conservation practices
<p>The Natura 2000 protected area network is the cornerstone of European Union's biodiversity conservation strategy. These protected areas range across multiple biogeographic regions, and they include a diversity of species assemblages along with a diversity of managing organizations, altogether making difficult to pool relevant sites to facilitate the flow of knowledge significant to their management. Here we introduce an approach to navigating protected area networks that has the potential to foster systematic identification of key sites for facilitating the exchange of knowledge and diffusion of information within the network. To demonstrate our approach, we abstractly represented Romanian Natura 2000 network as a co-occurrence network, with individual sites as nodes and shared species as edges, further combining into our analysis network topology, community detection, and network reduction methods. We identified most representative Natura 2000 sites that may increase the transfer of information within the national network of protected areas, detected clusters of sites and key sites for maintaining network cohesiveness, and highlighted the subsample of sites that retain the characteristics of the entire network. Our analysis provides implications for protected area prioritization by proposing a network perspective approach to collaboration rooted in ecological principles.</p>
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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.