Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
13,275
datasets available to search
ShareScore release 0.7.1
Dataset results
13,275 results for “management”
Survey of Pacific Northwest public land managers science and values project, 2023
This dataset records survey data about public land managers who work in Oregon and Washington (Forest Service, Bureau of Land Management, Fish and Wildlife Service, National Park Service, Oregon Department of Forestry, Washington Department of Natural Resources). Data was collected in 2023 via the online survey platform Qualtrics. Data collection is complete. The dataset includes measures of managers beliefs about 1) variable density thinning of mature growth forests, 2) salvage logging of burned areas, 3) translocation of plant species from hotter and drier seed zones to adapt to climate change. It includes how managers evaluate the usefulness of scientific evidence and the soundness of action prescriptions for each of the three management issues Respondents were randomly assigned to either receive long-term or short-term studies, and positive or negative results. The dataset includes measures of sense of belonging (how much managers believe they belong at their workplace) and measures of public support/public threat (how much they believe the public understands and supports the actions they take on the landscape). The dataset includes respondent agency.
Bald cypress radiocarbon and dendrochronological data from trees located in the Altamaha Wildlife Management Area and on Sapelo Island, Georgia, USA
Ancient bald cypress trees buried under anoxic mud on the Altamaha Wildlife Management Area islands were sampled, prepared, radiocarbon dated, and the ringwidths measured for crossdating. Modern bald cypress samples were also obtained from Sapelo Island. Tree rings were measured using a Velmex and Measure J2X software and/or the ObjectJ extension of ImageJ. Measured radii were crossdated using visual dendrochronological methods and Cofecha software. Tree rings anchored to the present date back to 3161 B.C.E. and extend to 2016 C.E. In addition, the oldest two trees provide another 529 years of ringwidth data. This work was conducted under the National Science Foundation Doctoral Dissertation Improvement Award: Human Adaptation to Long Term Environmental Change (Award #1834682). All samples are part of the accessioned collections of the University of Georgia Laboratory's of Archaeology.
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.
Demonstration of Ecosystem Management Options (DEMO) Study, western Oregon and Washington (post-treatment data, 1998-2016)
The Demonstration of Ecosystem Management Options (DEMO) Study is a regional-scale experiment in variable-retention harvest, established at six sites in western Oregon and Washington. Initiated in 1994, DEMO was designed to assess newly established standards and guidelines for regeneration harvests in mature, coniferous forests of the Pacific Northwest. The experiment is a randomized complete block design. It includes six treatments that represent strong contrasts in the level of retention (15-100% of original basal area) and the spatial pattern in which trees are retained (uniformly dispersed vs. aggregated in 1-ha patches). The factorial nature of the design (15 and 40% retention in both an aggregated and dispersed pattern) is unique among variable-retention experiments, regionally and globally. Long-term measurements of vegetation response lie at the core the DEMO Study. Key response variables include overstory tree growth and mortality, the dynamics of snags, regeneration of conifers (including planted seedlings and natural recruitment), and the composition, structure and diversity of the understory (including herbaceous, woody, and bryophyte species). Pre-treatment measurements were made between 1994 and 1996 (data are archived under Study Code TP104). Post-treatments measurements have occurred at ~5- to 7-year intervals between 1998 and 2016 (data are archived under Study Code TP108).
Managing Crop Yield Risk at the Kellogg Biological Station, Hickory Corners, MI (2022 to 2023)
Dataset Abstract As farmers adapt to changing climate, they modify practices and technologies to manage evolving risk. Adaptive changes may be as small as adjusting a crop insurance coverage level or as large as investing in an irrigation system. Farmer attitudes toward risk and their subjective perceptions of the evolving probability distributions of crop yields drive adaptation decisions. To understand climate change adaptation behavior by farmers, we undertook the study “Elicitation and Estimation of Risk Preference and Subjective Probabilities to Understand Farmer Decisions on Climate Change Adaptation.” We interviewed 44 Michigan corn and soybean farmers to elicit mathematical expressions of their risk attitudes. During the interviews, each completed two sets of lottery choices, the first using 25 general risky gambles and the second using 18 risky gambles in a crop farming context that enable econometric estimation of risk attitudes (using variants of Expected Utility Theory). Next, they answered questions about corn yield probability distributions over the past ten years and the next ten years (triangular distributions of minimum, most likely, and maximum values) with no water management, irrigation, tile drainage, and drought-resistant seed. After that, they reported on water management investments that they have made in past and intend to make in future. Finally, they provided background information about themselves and their farms. This study (MSU Study ID: STUDY00007871) was submitted to the Michigan State University Institutional Review Board (IRB) by principal investigator Scott Swinton. On July 5, 2022, it was determined to be exempt under 45 CFR 46.104(d) 3(i)(B). Data collection took place during September 2022 through March 2023. Farmer respondents completed the survey instrument on Qualtrics with assistance from graduate students in Agricultural, Food, and Resource Economics at Michigan State University at various MSU Extension offices and restaura
Long-term Agricultural Experiments: Data Management Survey
<p>Results of an online survey used to guage views of researchers within the LTE community on data management issues and knowledge. The survey was broken down in to 4 main questions and can be found at the following link - further responses are still welcome: <a href="https://forms.office.com/e/8DmapwLRr8" target="_blank" rel="noopener">https://forms.office.com/e/8DmapwLRr8</a>.</p> <ul> <li>About your role</li> <li>Data management & sharing</li> <li>Describing LTEs and their data</li> <li>Challenges for data management & sharing </li> </ul> <p>At the time of publication, 55 responses had been recieved.</p> <p>The survey was developed in response to an LTE Conference Workshop held at Rothamsted Research, UK in June 2023.</p>
Phoenix, Arizona (USA) residential yard management and vegetation change over time: 2008-2019
This project sampled residential front yards in four Phoenix, AZ neighborhoods to address questions about how residential yard vegetation varies among neighborhoods and changes over time and in response to resident attitudes. Neighborhoods were located on an approximate north-to-south transect in the city of Phoenix and represented different dominant landscape types (xeric or mesic) and different socioeconomic conditions. The project originated in summer 2008, when approximately 100 parcels were selected in each of the four neighborhoods for front yard vegetation sampling. All yards which could be relocated and accessed in the summer of 2018 were resampled, and current residents were surveyed to understand their yard management motivations, attitudes, and changes made. Yards of 100 survey respondents were resampled a third time in 2019. Yard sampling primarily focused on yard woody vegetation identification, but also included ground cover, yard type of neighboring yards, and features such as fences, lighting, and social infrastructure. Social and yard survey data can be linked with unique identifiers provided in the datasets.
GPH01 Grazing management effects on pollinator communities and habitat at Konza Prairie, 2024-2025
For all data files, data were collected in Konza Prairie LTER sites: N1A, N1B, C1A, K1B, and 1D during May, July, and August in both 2024 and 2025. The goal was to investigate variation in pollinator foraging and nesting habitat and in foraging and nesting pollinator communities across grazing regimes. Details for each file follow: 1. Plant-pollinator interactions (file = pollinatorNetworks); 2. Bees collected from ground nests (file = nestingBees); 3. Floral resources observed along transects (file = floralResources); 4. Bare ground cover and vegetation height measured along transects (file = otherHabitat); 5. Soil characteristics (file = soils)
North Temperate Lakes LTER: Manure Managment in Urbanizing Settings 2003 - 2004
The management of manure in urbanizing settings is a critical issue in the Lake Mendota watershed. The primary focus of this project was to examine the difficulties faced by livestock operations when managing manure on field systems that are fragmented by development. A survey regarding manure management was sent to Dane County, WI farms within the Lake Mendota watershed. The survey was conducted in two phases; March to May 2003 and March to May 2004. This dataset and accompanying survey entitled "Manure Management on the Urban Fringe" is available for users wishing to ascertain animal feeding operation size and management patterns in the Lake Mendota watershed. The data also include the distance from animal feeding operations to nearest urban centers via Euclidean (crow flies) and Road Network distances. Results suggest that exurban developments exert a strong influence on manure management routines of livestock producers. This influence is very local. Farmers in an urbanizing setting were more likely to encounter problems during manure hauling when the fields they were accessing were in close proximity to urban developments, regardless of their proximity to the urban core. The distances and times required to haul manure between the farm and the most distant field increased in the last five years. Land rental rates steadily increased at the same time that lease lengths shortened. Cash grain land tends to be sparse as livestock producers compete with developers for tracts on which to distribute manure. Manure brokering is a possible strategy to monitor land availability and coordinate manure placement between farms Cabot, P. E., S. K. Bowen, and P. J. Nowak. 2004. Manure management in urbanizing settings. Journal of Soil and Water Conservation 59:235-243. The survey "Manure Management on the Urban Fringe" was developed with assistance from Roger Schmidt and Charmaine Tryon-Petith with the Integrated Crop and Pest Management Program, University of Wisconsin-Madison.
Raw data for the submitted manuscript entitled "Mapping and Disposal of Irrigation Pipes for a Sustainable Management of Agricultural Plastic Waste", authors Ileana Blanco, Giuliano Vox, Fabiana Convertino, and Evelia Schettini
<p><span>The file regards the evaluation of plastic indexes and agricultural plastic waste quantities in Apulia region due to the use of irrigation pipes. The data is used to identify the critical areas for plastic waste production due to irrigation pipes.</span></p>
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>
Knowledge base for NBS for water treatment and stormwater management
<p>Five tables containing:</p> <ol> <li>nbs_catalog.csv: A catalogue of nature-based solutions for wastewater treatment and stormwater management. For each solution there is information on its performance, types of water, cobenefits, barriers and cost.</li> <li>sci_publications.csv: A list of scientific publications focused on one or several technologies of the above catalogue.</li> <li>sci_publications_treatment_details: For solutions for water treatment, a second table containing data about treatment performance extracted from previous scientific publications.</li> <li>description_nbs_catalog.csv: Descriptors for the catalogue.</li> <li>description_sci_publications_treatment_details.csv: Descriptors for the treatment performance data.</li> </ol> <p>The most updated version of each table can be queried from https://snappapi-v2.icradev.cat/</p>
IPBES Data Management Tutorials - Session 1.4: Introduction to the IPBES data management tutorials
<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>Introduction to the IPBES data management policy</em> chapter provides an overview on data management within the IPBES platform, and the series of the tutorials prepared by the task force on knowledge and data that will assist experts with the implementation of the IPBES data management policy.</p> <p>This session, <em>Introduction to the IPBES data management tutorials</em>, summarizes the purpose of the data management tutorials, what they cover, and where to find transcripts and documentation.</p>
IPBES Data Management Tutorials - Session 4.4: Examples of active data management
<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>Data management of active research data </em>chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session, <em>Examples of active data management</em>, gives concrete best-practice examples of how the recommendations could be integrated into the daily work of researchers</p>
IPBES Data Management Tutorials - Session 4.3: Recommendations and considerations for data backups
<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>Data management of active research data </em>chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session,<em> Recommendations and considerations for data backups</em>, reviews the importance of data backups, provides resources for further information, and discusses specific considerations one should keep in mind. </p>
IPBES Data Management Tutorials - Session 1.2: Introduction to IPBES tutorials and training
<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>Introduction to the IPBES data management policy</em> chapter provides an overview on data management within the IPBES platform, and the series of the tutorials prepared by the task force on knowledge and data that will assist experts with the implementation of the IPBES data management policy.</p> <p>This session, <em>Introduction to the IPBES tutorials and training</em>, provides<strong> </strong>a short introduction detailing the objectives of these tutorials and the responsibilities of the IPBES secretariat regarding data management. </p>
IPBES Data Management Tutorials - Session 2.1: Introduction to 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,<em> Introduction to the data management policy</em><em>, </em>defines what a data management policy is and why it is important.</p>
IPBES Data Management Tutorials - Session 3.4: Data management report details: File formats
<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 reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session, <em>Data management report details: File formats</em>, focuses on specific recommended file formats for text, tabular data, images, sound, and geospatial data. </p>
IPBES Data Management Tutorials - Session 5.4: Processing and analysis
<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 <em>processing and analysis </em>reviews common scripting languages for data analysis and processing, such as python and R. </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.