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1,069 results for “Data Management”
IPBES Data Management Tutorials - Session 5.2: Tools to find and attribute DOIs
<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<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>The session on <em>tools to find and attribute DOIs </em>covers fundamental background information on digital object identifiers and how to resolve and reserve them.</p>
IPBES Data Management Tutorials - Session 2.4: Implementation of the data management policy
<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 <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session on the<em> Implementation of the data management policy </em>provides a brief overview of the contents of the following chapters and how it all works together to improve the transparency and credibility of IPBES. </p>
IPBES Data Management Tutorials - Session 6.2: Literature review from the Global Assessment chapter 4
<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>Literature review from the Global Assessment chapter 4 </em>walks you through each step of the data management of the systematic literature review from the Chapter 4 of the Global Assessment. </p>
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>
IPBES Data Management Tutorials - Session 5.3: Literature access tools
<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<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session on literature access tools introduces Research4Life, a tool which provides experts in middle to low income countries access to scientific and grey literature.</p>
Survey Data on Apple Farming in China: Agronomic Management, Advisory Channels, and Profitability
<p>The Survey results and original data are stored in a directory structured as the table:</p> <table style="width: 100%; height: 223.938px;"> <tbody> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"><strong>Type</strong></td> <td style="width: 21.7597%; height: 19.5938px;"><strong>File Name</strong></td> <td style="width: 59.4134%; height: 19.5938px;"><strong>Description</strong></td> </tr> <tr style="height: 47.5938px;"> <td style="width: 18.8269%; height: 47.5938px;"> <p>Raw_Data_Spearate_Source</p> </td> <td style="width: 21.7597%; height: 47.5938px;">raw_data_english_telephone.xlsx</td> <td style="width: 59.4134%; height: 47.5938px;">Translated data in English corresponding to the Chinese telephone interview data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_wechat.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Translated data in English corresponding to the Chinese Wechat Mini Program data</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Raw_Data_Total</td> <td style="width: 21.7597%; height: 19.5938px;">raw_data_english_total.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Combined data from raw_data_english_telephone.xlsx and raw_data_english_wechat.xlsx</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 18.8269%; height: 39.1875px;">Apple_Statistical_Data</td> <td style="width: 21.7597%; height: 39.1875px;">apple_2022_statistical_data.xlsx</td> <td style="width: 59.4134%; height: 39.1875px;">Contains data on apple planting area, production, and yield sourced from the China Statistics Bureau, along with the number of survey questionnaires collected from various provinces</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_eng.xlsx</td> <td style="width: 59.4134%; height: 19.5938px;">Contains the English version of the provinces' names</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;">Map_Boundary_line</td> <td style="width: 21.7597%; height: 19.5938px;">national_boundary_line.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The country boundaires of China</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 18.8269%; height: 19.5938px;"> </td> <td style="width: 21.7597%; height: 19.5938px;">province_boundary.shp</td> <td style="width: 59.4134%; height: 19.5938px;">The province boundaries of China</td> </tr> </tbody> </table> <p>For privacy reasons, personally identifiable information such as respondents’ names, telephone numbers, and specific addresses has been anonymized in the dataset. The file <em>raw_data_english_total.xlsx</em> contains 96 columns, each corresponding to a question in the questionnaire.</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>
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>
RiceFloodIT: Water Management in the Italian Rice Paddies Estimated from MODIS data
<p>This repository includes two datasets used in Ranghetti et al. (2018) and Ranghetti & Boschetti (2022) to analyse the magnitude of a decreasing trend in the extent of submerged rice paddies during the rice-sowing period in the Italian rice district: methods used to generate these data from MODIS remote sensing imagery are described in these papers.</p> <ul> <li><strong>ffavg_2021.csv</strong>: this dataset includes values of yearly FF<sub>avg</sub> (averaged Flooding Fraction) at pixel level. Each record represent the FF<sub>avg</sub> value of a specific pixel in a specific year. <ul> <li><strong>x</strong> and <strong>y</strong> identifies the latitude and longitude of each record (in UTM32 coordinates);</li> <li><strong>subdistrict</strong> represent the sub-district ID of each pixel ("A" to "G");</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ff</strong> is the FFavg value (range 0-1);</li> <li><strong>count</strong> is the number of MODIS images used to generate each FF<sub>avg</sub> aggregated value.</li> </ul> </li> <li><strong>ws_2021.csv</strong>: this dataset includes values of WS (proportion of Water-Seeded rice surface) at sub-district and district levels. <ul> <li><strong>subdistrict</strong> represent the sub-district ID of each record ("A" to "G", plus "all" which identifies values aggregated at district level);</li> <li><strong>year</strong> is the year whose each record refers to;</li> <li><strong>ws</strong> is the WS value (range 0-1);</li> <li><strong>count</strong> is the number of pixels used to generate each WS aggregated record.</li> </ul> </li> </ul> <p>Current data version (2021.01) includes estimated values in the period 2000-2021.</p> <p>References:</p> <p>Ranghetti, Luigi, Elisa Cardarelli, Mirco Boschetti, Lorenzo Busetto and Mauro Fasola. 2018. “Assessment of Water Management Changes in the Italian Rice Paddies from 2000 to 2016 Using Satellite Data: A Contribution to Agro-Ecological Studies.” <em>Remote Sensing</em> 10 (3). doi:<a href="https://doi.org/10.3390/rs10030416">10.3390/rs10030416</a>.</p> <p>Ranghetti, Luigi and Mirco Boschetti. 2022. “Updated trends of water management practice in the Italian rice paddies from remotely sensed imagery.” <em>European Journal of Remote Sensing</em> 55 (1), pp. 1-9. doi:<a href="https://doi.org/10.1080/22797254.2021.2002726">10.1080/22797254.2021.2002726</a>.</p>
Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys
<p>Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.</p>
IPBES Data Management Tutorials - Session 3.5: Data management report details: Sensitive data, anonymization, and ethical considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session on <em>data management report details: Sensitive data, anonymization, and ethical considerations </em>captures specific considerations and processes for IPBES experts regarding sensitive data and Indigenous and local knowledge within data management reports. </p>
IPBES Data Management Tutorials - Session 2.3: Roles and responsibilities
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management Policy </em>chapter provides an introduction of the IPBES data management policy. It discusses why IPBES has a data management policy and who is responsible for what in the implementation and further development of this policy. </p> <p>This session on <em>roles and responsibilities </em>outlines the responsibilities of all involved players as stipulated in the IPBES data management policy. These are discussed in light of the importance for IPBES experts.</p> <p>Following version 2.0 of the data and knowledge management policy, a new PDF supplement has been added which covers the roles and responsibilities of the task force and technical support unit on Indigenous and local knowledge. </p>
IPBES Data Management Tutorials - Session 2.2: Why a data management policy for IPBES and how it concerns experts
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management policy </em>chapter provides an introduction of the IPBES data and knowledge management policy. It discusses why IPBES has a data and knowledge management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session, <em>Why a data management policy for IPBES and how it concerns experts, </em>provides background on what IPBES wants to achieve with its data and knowledge management policy and how it impacts experts within IPBES. The session now has a supplement which covers the development of the policy from version 1.0 to 2.0. </p> <p>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p>
OpenAIRE and FAIR Data Expert Group survey about Horizon 2020 template for Data Management Plans
<p>This dataset is published in 2017 by the OpenAIRE project and the FAIR Data Expert Group.</p> <p>It contains two survey data files, two pdf-files summarising the results in a report and an infographic, and a Readme.txt file.</p> <p>The OpenAIRE project supports the open science ambitions of the European Commission. The project and in particular the Research Data Management team provide support, training and information on the Open Research Data Pilot. In this context, a survey was carried out to collect feedback on the Horizon 2020 template for Data Management Plans (DMPs). The team collaborated with the FAIR data expert group, which is providing recommendations to the European Commission on turning FAIR data into reality. One of the specific tasks of the Expert Group is contributing to an evaluation of the Horizon 2020 approach to DMPs, including future revisions of the template and the development of additional sector/ discipline-specific guidance. The aim of the survey was to collect experiences of researchers and DMP reviewers with the DMP template and guidelines on FAIR data management in Horizon 2020. The survey assesses the usefulness of the guidelines and any aspects that are confusing and unclear to determine what improvements can be made.</p> <p>Feedback was sought from both researchers and research support staff. The survey was initially scheduled to run from 22 May to 21 June 2017. Several organisations were asked to help announce the survey, including OpenAIRE’s National Open Access Desks, the FAIR data expert group, FOSTER, LIBER, and the RDA Interest Group on Active DMPs. When the first survey responses showed only a small share of researchers, more stakeholders were contacted to specifically target this community. The European Research Area was approached, whose project officers circulated the survey call among award holders of EC projects. Early-career researchers were also informed through the YEAR network and EURODOC. This resulted in an extension of the survey to 21 July 2017.</p> <p>At the close of the survey on 21 July 2017, a total number of 289 responses were reached. 50% of the respondents indicated that they were researchers, and 60% that they were (also) research support staff. OpenAIRE and the FAIR data expert group are very pleased with this balanced outcome and would like to thank all colleagues and organisations who promoted the survey, as well as everyone who took part in it.</p> <p> </p>
Quantitative assessment of research data management practice - University of Bordeaux
<p>This survey was run at the University of Bordeaux in January 2019 using the questionnaire "Quantitative assessment of research data management practice" :</p> <p>Teperek, M., Krause, J., Lambeng, N., Blumer, E., van Dijck, J., Eggermont, R., … der Velden, Y. T. (2019). Quantitative assessment of research data management practice. Retrieved from : <a href="https://osf.io/mz3fx/">https://osf.io/mz3fx/</a></p> <p>The questionnaire included all the primary and secondary common questions, institution-specific questions regarding services and file sharing (EPFL questions), institution-specific questions for profile information.</p> <p>Data from the 425 responses collected are published here.</p> <p>Details regarding data collection and curation are included in the README file.</p> <p> </p>
Research Data Management and Sharing for images: beautiful fountains require ugly piping!
<p>The consensus is clear: research data funded by public resources should be shared. Globally, the advantages of sharing research data are widely recognized. It promotes transparency and validation, reduces redundant efforts, accelerates discovery, enhances equity, and increases the impact of research through collaboration and efficient use of resources.</p> <p>Image data, however, presents unique challenges. Advanced technologies produce large, multimodal, and multiplexed datasets that span multiple targets across various spatiotemporal scales.</p> <p>This image data comes from a range of sources—such as optical, electron microscopy, and medical imaging—each with specific technical requirements. Managing this complexity is a daunting task without global metadata standardization as well as robust Research Data Management and Sharing (RDMS) cyberinfrastructure to bring it all together.</p> <p>The figure illustrates a common issue: while the importance of the <strong>“beautiful fountains”</strong> of scientific discoveries and medical treatments is widely understood, fewer people recognize the <strong>need to invest in building the often ignored “ugly plumbing” </strong>required to build a strong RDMS cyberinfrastructure.</p> <p> </p>
Bibliographic Data from the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review
<p>This database contains all the bibliographic information about the 8673 records found after applying the Search Strategy used for the Digital Twin Anomaly Detection Decision-Making for Bridge Management Systematic Review. Such strategy consisted on using seven initial keywords and similar terms of interest (namely: bridge and bridges, etc.): </p> <ul> <li>Bridge.</li> <li>Digital twin.</li> <li>Bridge information modelling.</li> <li>Finite elements.</li> <li>Bridge health monitoring.</li> <li>Anomaly detection algorithm.</li> <li>Cultural heritage.</li> </ul> <p>Six initial queries were done combining the first keyword with the rest of them:</p> <ul> <li>bridge* AND "digital twin*"</li> <li>bridge* AND (BrIM OR "bridge information model*")</li> <li>bridge* AND (FEM OR FEA OR "finite element method*" OR "finite element analy*")</li> <li>bridge* AND ("bridge health monitoring" OR "structural health monitoring")</li> <li>bridge* AND (ADA OR "anomaly detection algorithm*")</li> <li>bridge* AND ("cultural heritage" OR "monument* bridge*" OR "old bridge*" OR "ancient bridge*" OR "historic* bridge*")</li> </ul> <p>As a first screening step, the combination of these 6 initial searches was done to obtain relevant works containing at least three of the main keywords of interest:</p> <ul> <li>#1 AND #2</li> <li>#1 AND #3</li> <li>#1 AND #4</li> <li>#1 AND #5</li> <li>#1 AND #6</li> <li>#2 AND #3</li> <li>#2 AND #4</li> <li>#2 AND #5</li> <li>#2 AND #6</li> <li>#3 AND #4</li> <li>#3 AND #5</li> <li>#3 AND #6</li> <li>#4 AND #5</li> <li>#4 AND #6</li> <li>#5 AND #6</li> </ul> <p>All records found in Scopus where downloaded both in .ris and .csv format and are included in this database. The search was conducted on 10/12/2022.</p> <p>Note: Searches 10, 14, 17 and 21 did not return any records.</p>
Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison''
<p>Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison'', published in Applied Catalysis B: Environmental 2023 335:122885, doi: <a href="https://doi.org/10.1016/j.apcatb.2023.122885">10.1016/j.apcatb.2023.122885</a></p> <p>Folder names describe the type of data content.</p>
data for Hogan et al. 2023: "Functional consequences of animal community changes in managed grasslands: An application of the CAFE approach"
<p>Data to accompany the following publication:</p> <div> <div> <div> <div>Hogan, K. F. E., Jones, H. P., Savage, K., Burke, A. M., Guiden, P. W., Hosler, S. C., Rowland‐Schaefer, E., & Barber, N. A. (2023). Functional consequences of animal community changes in managed grasslands: An application of the CAFE approach. <em>Ecology</em>, e4192. <a href="https://doi.org/10.1002/ecy.4192">https://doi.org/10.1002/ecy.4192</a></div> </div> </div> </div> <p>Please see README for description of data. </p> <p>Paper abstract: In the midst of an ongoing biodiversity crisis, much research has focused on species losses and their impacts on ecosystem functioning. The functional consequences (ecosystem response) of shifts in communities are shaped not only by changes in species richness, but also by compositional shifts that result from species losses and gains. Species differ in their contribution to ecosystem functioning, so species identity underlies the consequences of species losses and gains on ecosystem functions. Such research is critical to better predict the impact of disturbances on communities and ecosystems. We used the ‘Community Assembly and the Functioning of Ecosystems’ (CAFE) approach, a modification of the Price equation to understand the functional consequences and relative effects of richness and composition changes in small non-volant mammal and dung beetle communities as a result of two common disturbances in North American prairie restorations – prescribed fire and reintroduction of large grazing mammals. Previous research in this system shows dung beetles are critically important decomposers, while small mammals modulate much energy in prairie food webs. We found that dung beetle communities were more responsive to bison reintroduction and prescribed fires than small non-volant mammals. Dung beetle richness increased after bison reintroduction, with higher dung beetle community biomass resulting from changes in remaining species (context-dependent component) rather than species turnover (richness components); prescribed fire caused a minor increase in dung beetle biomass for the same reason. For small mammals, bison reintroduction reduced energy transfer through the loss of species, while prescribed fire had little impact on either small mammal richness or energy transfer. The CAFE approach demonstrates how bison reintroduction controls small non-volant mammal communities by increasing prairie food web complexity, and increases dung beetle populations with possible benefits for soil health through dung mineralization and soil bioturbation. Prescribed fires, however, have little effect on small mammals and dung beetles, suggesting a resilience to fire. These findings illustrate the key role of re-establishing historical disturbance regimes when restoring endangered prairie ecosystems and their ecological function.</p>
Pirate Illustrations for Research Data Management Training
<p>This collection of icons and comics was created to illustrate a workshop on Data Management Plans (<a href="https://doi.org/10.5281/zenodo.5575920">https://doi.org/10.5281/zenodo.5575920</a>). It is provided here to allow further reuse, for example to illustrate presentations.</p> <p>The theme of this collection is revolving around pirates, their accessories, and maritime items in general.</p> <p>Created by Jeanne Wilbrandt.</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.