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1,069 results for “Data Management”
Data to explore circular manureshed management in beef supply chains of the United States and western Canada
Circular management of beef supply chains holds great promise for improving sustainability from grazing agroecosystem to dinner plate. In the United States and Canada, one approach to circularity entails transporting manure nutrients from cattle produced in feedlots back to the grazing agroecosystems where they originated to enrich haylands for further grazing cattle production. We provide data to assess this strategy centered around three grazing agroecosystems: Florida, New Mexico, and the provincial assemblage of Manitoba, Saskatchewan, Alberta, British Columbia. We describe four datasets that can be used to estimate the potential nutrient utilization of hay fed to grazing cattle in the three grazing agroecosystems and the magnitudes of feedlot manure nutrients available for transport back to them. We found that although biogeography and management differ among the three grazing agroecosystems, the hay allocated for grazing cattle represented approximately 65% of the total harvested hay produced per agroecosystem after accounting for harvest losses, and that on average all three areas exported about 450,000 cattle annually for feedlot, pasture, and slaughter to states across the US. Although we highlight only three grazingland settings, our approach relies on methods that could ultimately be scaled nationally and internationally, with applicability to other animal industries for which circular management is an aspiration for sustainability outcomes.
Cooperative Proactive resource management for 5G in the unlicensed spectrum open data
<p>The data set consists of the following files:</p> <p><strong>1)COT information:</strong> The channel occupancy time of each channel for the first 5000 measurements. The COT values range from 0 to 1.</p> <p><strong>2)QL decisions uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider uniform traffic generation patterns.</p> <p><strong>3)QL decisions NON uniform traffic:</strong> The decisions of QL for the channel utilization of the available SBS and their impact to the achieved throughput. In this file we consider non-uniform traffic generation patterns.</p> <p><strong>4)Performance measurements: </strong>The final results of the experiment in respect to the transmit power control and throughput measurements under different QL configurations.</p>
Management and plant physiology data for grassland sites in Germany
<p>Management and vegetation data for sites Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg) in Southern Germany, observed between 2012 and 2017. Weekly resolution vegetation traits are included for 2015.</p> <p>The sites are part of TERENO, a network of observatories in Germany. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
IPBES Data Management Tutorials - Chapter 5: Tools for data management
<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>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p> <p>This 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. The chapter includes the following sessions: </p> <ul> <li>Session 5-1: Introduction to tools for data management (<a href="https://doi.org/10.5281/zenodo.4018639">10.5281/zenodo.4018639</a>)</li> <li>Session 5-2: Tools to find and attribute DOIs (<a href="https://doi.org/10.5281/zenodo.4018643">10.5281/zenodo.4018643</a>)</li> <li>Session 5-3: Literature access tools (<a href="https://doi.org/10.5281/zenodo.4014824">10.5281/zenodo.4014824</a>)</li> <li>Session 5-4: Processing and analysis (<a href="https://doi.org/10.5281/zenodo.4018647">10.5281/zenodo.4018647</a>)</li> <li>Session 5-5: References and citation manager: Zotero (<a href="https://doi.org/10.5281/zenodo.4018653">10.5281/zenodo.4018653</a>)</li> <li>Session 5-6: Publishing and sharing (<a href="https://doi.org/10.5281/zenodo.4018655">10.5281/zenodo.4018655</a>)</li> </ul>
Data Management Plan (DMP) Process Example
<p>This diagram is an example of a funding program solicitation mapped to the select key components of a data management plan, major data lifecycle process, infrastructure and resources, and proposed elements for sustainability of a funded research project. This diagram was developed out of a need to illustrate introductory DMP processes workflows for education, teaching, and training purposes. The DCC Checklist for a Data Management Plan (2013) and the USGS Data Lifecycle Model (2013) were adapted in this diagram.</p>
LAMASUS Land Use Management Data Set
<p>The land use management classes developed as part of the LAMASUS project have been aggregated to a 1 km grid (INSPIRE) provided as three geopackages and NUTS2 regions, provided as shares in each grid cell and number of pixels in each region, respectively. The NUTS2 data can be joined to the NUTS2 shapefile (contained in NUTS0_2016_01M_3035_corrected.zip, including all NUTS levels), which is provided here as some corrections to the NUTS layer have been made. A list of the land use management classes is provided in the excel file. Note that these aggregated files are consistent with official statistics from the Forest Resources Assessment of the Food and Agriculture Organization (FRA-FAO) for forest areas and Eurostat for cropland and grassland areas. </p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 2.1 titled "The LUM Geodatabase and Area Estimates of Land Use Change to 2018 ". The data is directly linked to the work described on pages 12-34, belonging to section 3. The Land Use Management (LUM) Geodatabase. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D2.1_LUMGeodatabase.pdf.</a></p> </div>
University Learning Management System xAPI data for the ILEDA project
<p>Anonymized data in xAPI format collected from learning management systems (Moodle and LAMS) for the ILEDA (2021-1-BG01-KA220-HED-000031121) project https://ileda.eu/ileda-project. The dataset contains 306,741 records of 829 individuals participating in eight different blended learning courses following either a flipped classroom methodology or a project-based learning methodology from four universities: University of Eastern Finland (Finland), University of León (Spain), Belgrade Metropolitan University (Serbia) and Sofia University (Bulgaria).</p>
Global forest management data at a 100m resolution for the year 2015
<p>We provide four data records:</p> <p>1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes: </p> <ul> <li> <p>“ID” is a unique location identifier </p> </li> <li> <p>“Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel. </p> </li> </ul> <ul> <li> <p>“Land_use_ID “is a land use class: </p> <ul> <li>11 - Naturally regenerating forest without any signs of human activities, e.g., primary forests. </li> <li>20 - Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc. </li> <li>31 - Planted forest. </li> <li>32 - Short rotation plantations for timber. </li> <li>40 - Oil palm plantations. </li> <li>53 - Agroforestry. </li> </ul> </li> <li> <p>“Flag” identifies a data origin: 1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach.</p> </li> </ul> <p>2. The 100 m forest management map in a geoTiff format with the classes presented - "FML_v3.2.tif ".</p> <p>3. The predicted class probability from the Random Forest classification in a geoTiff format - "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management--layer-proba_EPSG-4326.tif"</p> <p>4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes: </p> <ul> <li> <p>“ID” is a unique location identifier </p> </li> <li> <p>“pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection </p> </li> <li> <p>“first_landuse_class “is a land use class, as in (1). </p> </li> </ul> <ul> <li> <p>“second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence. </p> </li> </ul> <p>5. Original crowdsourced data set as a .csv table.</p> <p>6. Compiled FAO FRA forest statistics and mapped classes by countries into one table (.csv format).</p> <p> </p>
Data Evolution of Blended Learning and its Prospects in Management Education
<p>Data Scopus - Article "Evolution of Blended Learning and its Prospects in Management Education"</p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 3 - Raw Data survey entries
<p>This document provides extended, supplementary data and information to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- The raw dataset of survey entries, anonymized (IP addresses and personal comments deleted)</p>
Including Data Management in Research Culture Increases the Reproducibility of Scientific Results
<p><strong>General Information:</strong></p> <p>This dataset contains artifacts related to Riedel et al. (2022) (https://dx.doi.org/10.18420/inf2022_114). Here, we investigate the reproducibility of 108 research papers published between 2017 and 2021 by members of the Collaborative Research Center 1294 – Data Assimilation. To that end, we relate to a previous study by Stagge et al. (2019) that relies on a questionnaire that we extended. </p> <p>The publication by Stagge et al. (2019) is available here: https://doi.org/10.5281/zenodo.2562268<br> The dataset by Stagge et al. (2019) is available here: https://doi.org/10.1038/sdata.2019.30</p> <p>This dataset contains the questionnaire that we used to evaluate the reproducibility of scientific publications, a csv file containing the questionnaire’s answers, and a Jupyter notebook script to evaluate the given data.</p> <p><strong>Run the code:</strong></p> <p>To run the code, you must install Anaconda [1] and then open the jupyter notebook. All necessary libraries are listed in "requirement.txt". </p> <p>Alternatively, you can import the .ipyab file in the colab [2] and run it. </p> <p><br> [1]. https://www.anaconda.com/<br> [2]. https://research.google.com/colaboratory/<br> </p>
Research Data Management Lifecycle
<p>The Research Data Management (RDM) lifecycle describes the various phases of a research project from a data management perspective. The Cycle diagram illustrates all the steps with multiple levels of granularity and details. The text free images can be used for other purposes where a cycle diagram is needed.</p>
Data to support the publication "Impact of agricultural management on soil aggregates and associated organic carbon fractions: Analysis of long-term experiments in Europe"
<p><strong>Raw data:</strong> Experimental plot ids and information, mass distribution of all aggregate fractions after wet sieving, Sand content of each fraction to conduct the sand correction, mass distribution of all fractions after isolating the micro-aggregates held within the macroaggregates, yields per treatment, carbon content per fraction (raw data)</p> <p><strong>All data per plot: </strong>SOC content, MAOM and POM content of each fraction presented in the fractionation scheme included in the manuscript, together with the mass of the relative fractions. </p> <p> </p>
Research data management in the German-speaking Sports Sciences - Survey on the Status Quo
<p>The data set contains survey data on the status quo of research data management within the German-speaking sports science community. The survey was conducted as an online survey in the period from August 16<sup>th</sup> to September 30<sup>th</sup>, 2023.</p>
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</p>
Data from: Long-term effects of meadow management on seed bank diversity and composition
<p>Aims: Oligotrophic grasslands are habitats that host among the most diverse plant communities in Europe. Altering management regimes by either intensifying or ceasing management is known to decrease plant diversity. Yet, despite its importance for the recovery of plant communities after disturbances, little is known about whether seed banks are also affected by changes in management. Here, we investigate the effect of management practices on a meadow seed bank using a long-term manipulative experiment. We focus on the response of the seed bank to the treatments, and the relationship between the seed bank and the vegetation response.</p> <p>Methods: The study was conducted in a species-rich wet meadow. The experiment consists of a factorial combination of fertilization, mowing, and removal of the dominant species. After 20 years of management, the seed bank was sampled seasonally at two soil layer depths. Standing vegetation was recorded in June at the peak of vegetation.</p> <p>Results: All seed bank characteristics varied between soil layers. Mowing decreased seed density and diversity, while fertilization significantly affected the species composition. Dominant removal had no effect on the seed bank. While seed bank diversity was not correlated to vegetation diversity, individual species’ responses to mowing and fertilization were positively correlated in the seed bank and the vegetation.</p> <p>Conclusions: Our results show that long-term management influences the seed bank down to 10 cm of soil depth. Whereas mowing apparently reduced seed density and diversity, the effects of fertilization on these characteristics were harder to interpret. After 20 years, most species had concordant responses to both mowing and fertilization, indicating a low legacy of previous management regimes on the seed bank. Our study reveals that the intensification of grassland management has a profound effect on plant diversity by directly affecting plant communities and their seed bank-driven recovery potential.</p>
Dataset for: Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manusript:<br> Perrier L, Blondal E, MacDonald H. Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies. 2018; 40(3-4): 173-183. doi: 10.1016/j.lisr.2018.08.002</p> <p>Full-text available at: <a href="https://doi.org/10.1016/j.lisr.2018.08.002">https://doi.org/10.1016/j.lisr.2018.08.002</a> </p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset:</p> <ol> <li>Data Dictionary: RDMMetaEthnography_DataDictionary_v1.pdf</li> <li>Data Abstraction Sheet: RDMMetaEthnography_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_ParticipantCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_Outcomes.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_COREQ,csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Data Management Plan
<p>Video of a presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></p>
Plankton Temperature Measurements - Data Management - University of Tennessee - Mock Data
<p><strong>Comparison of conochilus unicornis (CONI) and conochilus hippocrepus (CHIP) depth and colony size over time at three different locations. </strong></p> <p>This contains colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonNamePond_v7.27.2012.csv">SEH_PlanktonNamePond_v7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempA_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from site A and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempB_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Site B and the data on which these data were gathered wereTHESE DATES.</p> <p><strong>Metadata</strong></p> <p>Date: Day that samples were collected</p> <p>Time_Day_Night: Gives time that the sample was gathered in the day</p> <p>Temp_C: Temperature of the water containing the plankton.</p> <p>CONI: Conochilus unicornis - species of plankton</p> <p>CHIP: Conochilus hippocrepis - plankton</p> <p>XXXX_ColonySize_mm: Average diameter (mm) of 5 randomly chose plankton colonies in the sample </p> <p>Temp: TemperatureYSI probe taken once at each depth</p> <p>ChlorophyllA_Units: Chlorophyll A values</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.