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.
1,648
datasets available to search
ShareScore release 0.9.0
Dataset results
1,648 results for “farm”
Data from on-farm trials of the InnovAfrica project in the South African case country
<p>The data set were generated through on-farm field trials that were established in Maluti-a-Phofung municipality, situated in the eastern Free State province of South Africa as part of a project entitled" Innovations in Technology, Institutional and Extension Approaches towards Sustainable Agriculture and enhanced Food and Nutrition Security in Africa (Acronym-InnovAfrica)". This project was implemented by a consortium of 16 institutions from Africa and Europe continents across six African countries (viz. Ethiopia, Kenya, Malawi, Rwanda, South Africa and Tanzania) from June 2017 to November 2021. Farmer-led trials were established for three planting seasons (2017/2018 to 2019/2020) to demonstrate, test and upscale the best-bet sustainable agricultural intensification practices for improved food and nutrition security in smallholder settings.</p>
Survey data Agro-ecological farming in Protected Areas
<p>“Produce better, eat well" is the subtitle of the Good Food Strategy of the Brussels-Capital Region. Can (sub)urban protected areas simultaneously contribute to this challenge and support biodiversity? In order to find inspiring cases, we launched an online survey in English, French and Dutch and spread it through networks on agro-ecological farming and nature conservation. We identified 76 Western-European cases of agro-ecological farming in protected areas. This database includes all of these cases. The survey was followed by in-depth interviews and the findings were summarized in this report.</p>
Active Power Control from Wind Farms for Damping Very Low-Frequency Oscillations
<p>Dataset used for results in paper "Active Power Control from Wind Farms for Damping Very Low-Frequency Oscillations"</p>
Birds and insects respond differently to combinations of semi-natural features in farm landscapes
<p>Semi-natural features among farmland have a key role in maintaining wildlife in rural landscapes. Practical conservation requires knowledge of which combinations of features are of greatest value and whether this differs among faunal groups. We used a 'landscape' approach to investigate the relative importance to birds and insects (bees, flies, wasps) of combinations of three wooded features typical of farmland in south-eastern Australia: scattered trees, wooded roadsides and wooded streamside vegetation. We selected 44 landscapes (1 km diameter) representing four combinations: a) landscapes with all three features present, b) landscapes lacking scattered trees, c) lacking wooded roadsides, and d) lacking wooded streamsides. We surveyed birds and selected insects, and compared mean alpha (α, site), beta (β, between site) and gamma (γ, landscape) diversity for each taxon between landscape types; and gamma (γ) diversity of bird species displaying breeding activity. Mean α-diversity of birds was reduced in landscapes lacking wooded roadsides or streams, relative to those with all three wooded features; while species differentiation (β-diversity) increased in these landscapes. Loss of streamside vegetation had the greatest landscape-scale impact, reducing γ-diversity by ~33% for all land-birds and ~50% for woodland birds. Bird breeding activity declined by ~50% in landscapes lacking wooded streamsides. In contrast, insects showed little response, except bees for which mean α-diversity was greater in more-open landscapes lacking scattered trees or wooded roadsides, compared with those containing all wooded features. This did not lead to differences in landscape-level (γ) diversity. Synthesis and applications. Marked differences in how birds and insects respond to different combinations of semi-natural features mean that a 'one size fits all' approach to nature conservation is insufficient. Wooded features, especially streamside vegetation, are critical for maintaining diversity and breeding activity of woodland birds. In the absence of adequate knowledge of many insect groups in southern Australia, a prudent approach is to foster a diverse farmland mosaic comprising semi-natural habitats together with floristically rich, modified features that provide temporally dynamic resources. Small semi-natural features have disproportionate value for conservation, relative to their area, while also benefitting farm productivity and supporting ecosystem services. </p>
Data from: Ecosystem multifunctionality is promoted by organic farming and hedgerows at the local scale but not at the landscape scale
<p><span>This dataset gathers information used for the paper Couthouis et al. "Ecosystem multifunctionality is promoted by organic farming and hedgerows at the local scale but not at the landscape scale", part of the BIOMHE project (2019-2022) funded by the Fondation de France. It contains data about (i) species richness and abundances used for calculating ecological performance, (ii) crop yield used for calculating agronomic performance, (iii) labour and semi-net margin used for calculating socio-economic performance. Combined, these information allows for the calculation of ecosystem multifunctionaliy indices.</span></p>
CS9 - Mussel offshore farm technology Sweden - profitability model 2022
<p>The dataset contains a standard profitability analysis of a mussel farm in Sweden utilising a novel farming method of tube and net technology with potential to be used in exposed and offshore areas. The profitability of the farm is analysed by estimating net present value and sensitivity analysis to calculate variations in financial returns under different conditions. Data on costs and productivity was classified as confidential for industrial property protection and will not uploaded in any public repository</p>
Text Mining of Archaeological Reports for Urban farming (data and code)
<p>This release is created to create a DOI in Zenodo for the data related to Fischer, AD, van Londen, H, Blonk-van den Bercken, AL, Visser, RM and Renes, J. 2021. Urban farming and ruralisation in the Netherlands (1250 up tot the nineteenth century), unravelling farming practice and the use of (open) space by synthesising archaeological reports using text mining. Nederlandse Archeologische Rapporten 68. Amersfoort: Rijksdienst voor het Cultureel Erfgoed. <a href="https://www.cultureelerfgoed.nl/publicaties/publicaties/2021/01/01/urban-farming-and-ruralisation-in-the-netherlands">https://www.cultureelerfgoed.nl/publicaties/publicaties/2021/01/01/urban-farming-and-ruralisation-in-the-netherlands</a></p>
Figure 2 in Mites occurring in farm buildings as allergic agents and indicators in forensic analyses
Figure 2 Dominance [%] of the Astigmatina mite species in all examined farm buildings.
Economics Analysis of Small and Large Farm Size Honey Bee Sub-Sector in Chitwan District, Nepal
<p>This is an SPSS file that can be used for calculating various descriptive statistics and performing statistical tests such as t-tests and chi-square tests. Ranking of scale can also be carried out from these datasets.</p>
Estimating net carbon balances and greenhouse gas radiative balances of potato and pea crops on a conventional farm in western Canada (Flux and meteorological data)
<p>Data accompanying the paper titled as "Estimating net carbon and greenhouse gas balances of potato and pea crops on a conventional farm in western Canada". Data includes measurements from eddy covariance, chamber, and meteorological sensors. Measurements were mainly conducted in 2018 and 2019, please refer to the paper for the detailed information.</p>
Scearce Family Tobacco Farm Homestead
Located outside of Danville, Virginia, the Scearce family farm was established in the early 20th century. Similar to many other farmers in Pittsylvania County, the Scearces made their living from raising tobacco. As a result, their modest farm was once doted with tobacco related structures. Many of the tobacco barns have previously burned down or fallen in due to neglect. Unfortunately, the last of the surviving structures were recently torn down. Most of the structures were of log construction which was later covered with weatherboard. The homestead has been recreated using a map drawn by a former resident dated as of 1928, photographs, and field observations. Not depicted is a family cemetery near the two houses that contains around 20 individuals. Source: Objaverse 1.0 / Sketchfab
Mastodon cranium. Neath Farm, Dane Co., WI
Mastodon (*Mammut americanum*) cranium from Neath Farm locality in Dane Co., WI. UWZP 20000. Scanned in June 2022. Source: Objaverse 1.0 / Sketchfab
Ruinous farm building, South Wales
had this in my drafts from 3 years ago.... ABC archaeological recording course in South West Wales. more to follow once I find the ground plans we made. Source: Objaverse 1.0 / Sketchfab
Mount Alt Farm - Cup Marked Stone
This stone is on display in the Smith Art Gallery & Museum, Stirling. It was found in 1893 near Mount Alt Farm in the Ochils along with a cinerary urn (whereabouts now not known). The precise location and circumstances of the find is unknown and the only record the museum now has of it is on its display card. It has been classified as a cup marked rock but the indentations are much largers and deeper than the usual cup marks and appear more like bowls. I would speculate that this is more likely to have been used as a 'knockin' stane' or was possibly a bullaun stone as the bowls would have collected water. Possibly it may have been associated with a cist as it is stated to have been found in association with a funerary urn. Source: Objaverse 1.0 / Sketchfab
Farm-Flow | AG-IoT Security: Intrusion Detection in Smart Agriculture Dataset
<div> <div> <p><strong>Introduction:</strong></p> <p>The "Farm-Flow" dataset was created to emulate real-world Agricultural Internet of Things (AG-IoT) systems, encompassing network attacks and data collection. Following comprehensive cleaning and processing, the "Farm-Flow" dataset comprises 532 MB of data with 1,310,000 instances, structured around "flows," which represent consecutive series of packets transmitted from a single source to a specific destination. The dataset demonstrates an intrusion detection accuracy of 92.67% and is intended to enhance the security of AG-IoT systems, safeguarding information such as crop health, weather patterns, and soil conditions</p> <p><strong>Captures:</strong></p> <p>The captures comprises three months of network traffic: August, September, and October of 2022. Each month is divided into folders, which categorize the network traffic. These folders contain numerous .pcap files, which have been divided into 5-second intervals. This segmentation is necessary because, as previously mentioned, flows aggregate packets, resulting in only one row of flow data for ongoing connections. To address this, a script was developed to segment the .pcap files into 5-second increments. This approach allows for the generation of multiple rows of flow connections, thereby providing more quantity of data for model training.</p> <p><strong>Dataset:</strong></p> <p>The dataset comprises 532 MB of data, encompassing 1,310,000 instances. These instances have been classified into eight distinct attack types and one category for normal traffic. The identified attacks include Arp Spoofing, BotNet DDoS, HTTP Flood, ICMP Flood, MQTT Flood, Port Scanning, TCP Flood, and UDP Flood. Among the data set, there are 27,458 instances of normal traffic and 1,282,429 instances of aggregated attack traffic.</p> <p><strong>Zip Folder:</strong></p> <p>The zip folder is structured into two main directories: Captures and Dataset. The Captures directory is organized by the month of capture and further categorized by network traffic type. The Datasets directory includes the Farm-Flow Dataset, alongside four additional datasets that have undergone pre-processing: the training and testing datasets for binary classification, and the training and testing datasets for multiclass classification. Additionally, there are further datasets categorized by month and type of network traffic.</p> <p><strong> Article Information:</strong></p> <p>The work involved in developing the Farm-Flow dataset is described in the following paper. Please cite the paper and the dataset when using the Farm-Flow dataset.</p> <blockquote> <p>Rafael Ferreira, Ivo Bispo, Carlos Rabadão, Leonel Santos, and Rogério Luís de C. Costa (2025). <em>Farm-flow dataset: Intrusion detection in smart agriculture based on network flows</em>, Computers and Electrical Engineering, Volume 121, 109892, DOI: <a href="https://doi.org/10.1016/j.compeleceng.2024.109892." target="_blank" rel="noopener"> 10.1016/j.compeleceng.2024.109892</a></p> </blockquote> </div> </div>
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 10. Блок-схема фиЗико-статистического прогноЗа уроЖайности спата приморского гребешка.
The negative association of sea lice from fish farms on recreational fishing catches of Atlantic salmon
<p>The question of whether and to what extent sea-louse (<em>L. salmonis)</em> infestations from salmon farms influence wild Atlantic salmon survival has been subject to sustained scientific debate and political controversy. Documenting the population-level effects of sea lice on wild salmon remains inherently challenging. We employ comprehensive sea-lice data and recreational catch data from Norway to assess the impact of farm sea-louse infestations on wild salmon catches in different production areas (PAs). Our analysis finds a significant correlation between declines in wild Atlantic salmon catches and increasing amounts of adult female sea lice per km<sup>2</sup>. The effect is most pronounced in PA 4 on the west coast of Norway, an area within the government's "traffic light" management system where out-migrating salmon smolts are deemed to face high exposure to louse-induced mortality risk. Our model predicts below-average catches when the total sea louse load exceeds the government's limit of 0.1 average adult female sea louse per farmed fish within some production areas. Furthermore, our results indicate that the risk of below-average catches increases by approximately 47% when salmon farms exceed this limit (estimated risk ratio of 1.47, 95 % CI [1.10, 1.96]).</p> <p><em>Synthesis and Applications</em>: Our study expands the existing body of evidence demonstrating a negative association between fish farming and the ecosystem services provided by wild salmon stocks. It has important implications for aquaculture management. First, it shows that farm sea lice directly affect peoples, rights holders, and interest groups, such as landowners and anglers, who rely on viable populations of wild salmon. Second, it suggests that a >0.1 sea lice limit may be insufficient to prevent subpar catches in some areas. Furthermore, in the context of allowing further growth in farmed salmon biomass, setting absolute sea-louse limits for entire production areas may be a more effective regulatory instrument than setting average lice limits per farmed fish. Our findings contribute to advancing a scientific basis for setting appropriate louse limits on farm and area scales.</p>
Fig 1 in Incidence of parasitic infection in adult and juvenile Clarias gariepinus in a private fish farm, Yola, Adamawa state
Fig 1: Sex Variation in Prevalence (%) of Parasitic Infection of Culture Clarias gariepinus
Fig 1 in Tail and fin rot disease and antibiotics resistance pattern in major rainbow trout farms of Nepal
Fig 1: Survey and Sample Collection site
Morphometrics and nutrient concentration of farmed eastern oysters (Crassostrea virginica) from the US Northeast Region
<p><strong><u>Acknowledgements</u></strong></p> <p>This work was supported by the NOAA Fisheries Northeast Fisheries Science Center and the NOAA Fisheries Office of Aquaculture. Thanks to Marta Gomez-Chiarri, PG Harris, Mark Luckenbach, and Christine Thompson for sharing data, although these data were not included in the final repository.</p> <p><strong><u>Background Information</u></strong></p> <p>The removal of excess nitrogen from eutrophic environments is an ecosystem service provided by shellfish aquaculture that is well described in the literature (Lindahl et al., 2005; Rose et al., 2014; Petersen et al., 2014; Clements and Comeau, 2019). Nitrogen removal associated with shellfish farms can occur via three mechanisms: the assimilation of nitrogen into tissue and shell, which is removed from the waterbody when animals are harvested; the enhancement of sediment denitrification through biodeposit production on farms; and the long-term burial of biodeposits.</p> <p>A robust calculation of the nitrogen removed at shellfish harvest has been previously published using relatively simple metrics: the nitrogen concentration of tissue/shell, the number and mean size of animals harvested, and a conversion of animal size to tissue and shell dry weight (Reichert-Nguyen et al., 2016; Clements and Comeau, 2019). The quantity of nitrogen removed at shellfish harvest has been previously predicted with high confidence, which has led to the integration of oyster and clam aquaculture into nutrient management programs at the local and estuary scale in the United States (Town of Mashpee, 2015; Reichert-Nguyen et al., 2016; Reitsma et al., 2017).</p> <p>The data in this repository were compiled with the intention of expanding the geographic scope of calculation of nitrogen removal associated with harvested eastern oysters (<em>Crassostrea virginica</em>), and to evaluate variation in this ecosystem service across common cultivation practices and ploidy. Data were obtained from sampling locations across the US from the state of North Carolina north to the state of Maine. Data are included for both diploid and triploid oysters, and for three common styles of cultivation: oysters grown on bottom without the use of aquaculture gear, oysters grown in bottom cages, and oysters grown in floating gear at the sea surface. Data curation was undertaken to obtain a dataset that most closely reflects on-farm conditions as possible. The highest priority was to find data collected from working oyster farms, and a second priority was to identify data collected by scientists who employed common cultivation practices in their research studies. In one state within the region (Rhode Island) we were unable to locate data that fit the previous description, and instead have included data from wild oysters from waterbodies that have oyster farms.</p> <p>This data set was used to support the development of the Aquaculture Nutrient Removal Calculator (ANRC,<a href="https://connect.fisheries.noaa.gov/ANRC/">https://connect.fisheries.noaa.gov/ANRC/</a>), a tool designed for use by both shellfish farmers and managers within the aquaculture permit review process. The ANRC is a publicly available, simple online tool that was developed in direct response to feedback from aquaculture resource managers. The ANRC accurately predicts harvest-based nitrogen removal from an eastern oyster farm located within the geographic range of North Carolina to Maine, USA. We have taken an adaptive management approach to tool development, basing our tool on current best available scientific information, with the intention of maintaining and updating this tool when new information and data become available in the future.</p> <p><strong><u>Data Description</u></strong></p> <p>This repository contains information on morphometrics and nitrogen concentration of tissue and shell for eastern oysters sampled within the US geographic region spanning the states of North Carolina to Maine. Some of the data in this repository (6 of 10 sources) were previously published as summary statistics in the peer-reviewed literature, but the raw data included here were not archived. Three datasets were not previously published in raw or summary form. One dataset is publicly available in a technical report.</p> <p>The repository is organized with individual oysters as rows and numerical/categorical information associated with those oyster samples as columns. Each sample in the repository contains data on oyster shell height (mm), tissue dry weight (g), data source, ploidy, cultivation practice, and location of sample collection. The repository also contains additional information provided by data sources as available, such as shell dry weight, nitrogen, carbon, sampling date, oyster stock, and other morphometric measurements.</p> <p><strong><u>References</u></strong></p> <p>Clements, J.C., Comeau, L.A., 2019. Nitrogen removal potential of shellfish aquaculture harvests in eastern Canada: A comparison of culture methods. Aquaculture Reports 13, 100183.</p> <p>Lindahl, O., Hart, R., Hernroth, B., Kollberg, S., Loo, L.-O., Olrog, L., Rehnstam-Holm, A.-S., Svensson, J., Svensson, S., Syversen, U., 2005. Improving marine water quality by mussel farming - a profitable solution for Swedish society. Ambio 34, 129-136.</p> <p>Petersen, J.K., Hasler, B., Timmermann, K., Nielsen, P., Tørring, D.B., Larsen, M.M., Holmer, M., 2014. Mussels as a tool for mitigation of nutrients in the marine environment. Marine Pollution Bulletin 82, 137-143.</p> <p>Reichert-Nguyen, J., Cornwell, J., Rose, J., Kellogg, L., Luckenbach, M., Bricker, S., Paynter, K., Moore, C., Parker, M., Sanford, L., Wolinski, B., Lacatell, A., Fegley, L., Hudson, K., French, E., Slacum, W., 2016. Panel recommendations on the oyster BMP nutrient and suspended sediment reduction effectiveness determination decision framework and nitrogen and phosphorus assimilation in oyster tissue reduction effectiveness for oyster aquaculture practices, Report to the Chesapeake Bay Program. Available online at <a href="https://www.oysterrecovery.org/wp-content/uploads/2017/01/Oyster-BMP-1st-Report_Final_Approved_2016-12-19.pdf">https://www.oysterrecovery.org/wp-content/uploads/2017/01/Oyster-BMP-1st-Report_Final_Approved_2016-12-19.pdf.</a></p> <p>Reitsma, J., Murphy, D.C., Archer, A.F., York, R.H., 2017. Nitrogen extraction potential of wild and cultured bivalves harvested from nearshore waters of Cape Cod, USA. Marine Pollution Bulletin 116, 175-181.</p> <p>Rose, J.M., Bricker, S.B., Tedesco, M.A., Wikfors, G.H., 2014. A Role for Shellfish Aquaculture in Coastal Nitrogen Management. Environmental Science & Technology 48, 2519-2525.</p> <p>Rose J.M.,, Morse, R., and Schillaci, C. 2024. Development and application of an online tool to quantify nitrogen removal associated with harvest of cultivated eastern oysters. PLoS ONE 19(9): e0310062. https://doi.org/10.1371/journal.pone.0310062</p> <p>Town of Mashpee Sewer Commission, 2015. Comprehensive watershed nitrogen management plan, Town of Mashpee, Available online at http://www.mashpeewaters.com/documents.html.</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.