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149 results for “farmers”
Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs
<p>Datasets describing the fungal species diversity, microbial density and acidity of French sourdoughs, phenotypic variation of Kazachstania bulderi and Kazachstania humilis strains as well as the diversity of bread-making practices of 40 bakers and farmers-bakers.The data were collected, analyzed, and reported within the following publication :</p> <p>Elisa Michel, Estelle Masson, Sandrine Bubbendorf, Léocadie Lapicque, Thibault Nidelet, Diego Segond, Stéphane Guézenec, Thérèse Marlin, Hugo deVillers, Olivier Rué, Bernard Onno, Judith Legrand, Delphine Sicard and the participating bakers: <strong>Artisanal and farmer bread making practices differently shape fungal species community composition in French sourdoughs</strong>. PCI Evol. Biol.</p> <p> </p>
Dataset to manuscript: Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems
<p>Raw data to the manuscript entitled "Tailor-made biochar systems: Interdisciplinary evaluations of ecosystem services and farmer livelihoods in tropical agro-ecosystems" by Severin-Luca Bellè, Jean Riotte, Norman Backhaus, Muddu Sekhar, Pascal Jouquet and Samuel Abiven. </p> <p>Data files include all raw data of farmer interviews (20211209_Biochar_India_rawdata_Bellè_Abiven_farmer_interviews) and all raw data from the soil incubation study (20211209_Biochar_India_rawdata_Bellè_Abiven_soil_incubation). </p> <p>File ending with var_names is the README file. </p>
Questionnaire data to research small-scale farmers' information sharing for adapting to climate change in Mozambique (2019-2020)
<p>Data collected from individual questionnaires with local communities of 4 districts of Mozambique in November 2019 and July 2020. It contains as well data from nine individual questionnaires to institutions (government and NGOs) working with local communities for their development.</p> <p>Data are replies from interviews containing open and closed questions about a) climate change adaptation options necessary for Mozambican small scale farmers, about b) the most used and preferred information sources of farmers, about c) the main barriers for a better exchange of information, and about d) proposals for improving it. The questionnaire can be consulted in Appendix A (in English and Portuguese). The open questions had the purpose to understand the causes and explanations about the themes presented. The closed questions followed a 0-5 likert scale approach, where 5 meant a very important factor and 0 non important one. This format was pursued for developing statistical analysis and comparison between the different types of participants. We used the same questions and format for interviewing farmers and stakeholders, although the questionnaire for farmers included also personal aspects like gender, age, and education.</p>
CONSOLE_WP3_Task3.2_Pan-EU survey of farmers and other rural landowners__IT_UNIPI_2022.10.19_v02
<p>Dataset containing information about the choice experiment carried out in Liguria Region for the CONSOLE Project</p>
Farmers' fields network of oilseed rape intercropped with service plant in Western Switzerland.
<p>These data are associated with the publication of Bousselin et al. (2024) in which the experiment and the protocols are explained into details.</p> <p><span>Bousselin, X., Lorin, M., Valantin-Morison, M. </span><em>et al.</em><span> Determinants of oilseed rape-service plant intercropping performance variability across a farmers’ fields network in Western Switzerland. </span><em>Agron. Sustain. Dev.</em><span> </span><strong>44</strong><span>, 40 (2024). https://doi.org/10.1007/s13593-024-00972-6</span></p>
James Michael Farmer (f1860)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: James Michael Farmer<br><u>musiXplora-ID</u>: f1860<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/f1860">https://musixplora.de/mxp/f1860</a><br><u>Gender</u>: m<br><u>First Mentioned</u>: 1890<br><u>Sectors</u>: Automatenbau<br><u>Professions (Historical)</u>: Patentinhaber<br><u>Other Places of Activity</u>: London<br><br><br><u>Patentrecht:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>ErfinderInnen</td><td>Erfinder</td><td>Patentschrift. Mechanisches Accordeon mit durchlochtem Notenblatt. Anmeldedatum</td><td><a href="https://musixplora.de/mxp/5080730">5080730</a></td></tr></tbody></table><br><u>Titel/Medien:</u><br><table><tbody><tr><th>Role</th><th>Sigel</th><th>Title</th><th>mXp-ID</th></tr><tr><td>Related</td><td>Patent DRP 56924</td><td>Patentschrift. Mechanisches Accordeon mit durchlochtem Notenblatt. Anmeldedatum</td><td><a href="https://musixplora.de/mxp/5080730">5080730</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
Archetypes of climate change adaptation among large-scale arable farmers in southern Romania
<p>Supplementary material belonging to the publication.</p> <p>Two files:</p> <p>1. Excel file with database containing raw data and information resulted from surveying a sample of 30 farmers/farm managers in southern lowlands of Romania between April and June 2020.</p> <p>2. PDF with interview guideline</p>
Dataset on consumers' perception of different types of sustainability levies, Swiss agriculture and farmers and willingness to choose suboptimal potatoes in different settings
<p><em><span>This dataset includes survey data from 481 Swiss consumers. Data were collected in the German-speaking parts of Switzerland in February and March 2024. The survey includes three independent main parts. </span></em></p> <p><em><span>In a first part, we collected qualitative and quantitative data on participants’ perception of Swiss agriculture and farmers. Specifically, participants’ trust in crop and livestock production farmers and their perceived knowledge about production methods and their affect towards farmers was assessed. </span></em></p> <p><em><span>In a second part, we collected quantitative data on participants’ preference for different sustainability levies. For this, six different products were used (i.e., fresh/processed vegetables, dairy, and meat). For each of these six products, participants were shown four levy options from which they had to choose the one that they found most appealing. For vegetables, the options were: (A) reduction of risks related to plant protection products, (B) more support for local farmers, (C) support for environmental sustainability, and (D) sustainability projects in general. For the animal products, option (A) was an increase in animal welfare, whilst options (B), (C) and (D) were the same as for the vegetable products.</span></em></p> <p><em><span>In a third part, we collected qualitative and quantitative data on participants preferences for suboptimal or optimal potatoes. Here, a 2 × 2 experimental design (setting × information) was used. This means that participants were presented with either a supermarket or farm shop setting and with or without food waste information. Participants then chose between two potatoes: optimal potato A, suboptimal potato B, or neither. Both potatoes were equally expensive.</span></em></p>
Farmer surveys at the Kellogg Biological Station, Hickory Corners, MI (2011)
Dataset AbstractFarmers are surveyed to understand their perception of and response to climate change. This data forms part of the basis for Stuart, D., R. L. Schewe, and M. McDermott. 2012. Responding to climate change: barriers to reflexive modernization in US agriculture. Organization & Environment 25:308-327original data source http://lter.kbs.msu.edu/datasets/123
Participatory Conceptual Diagrams to research small-scale farmers´ information sharing for adapting to climate change in Mozambique
<p>Data collected from focus groups discussions with local communities of 4 distrcits of Mozambique in November 2019. The data are a series of conceptual maps describing a) the farming practices improvements most needed to adapt to climate change, and b) the most useful information for enabling the selected improvements, the most effective information sharing sources - e.g. institutional actors, members of the community, technical support, etc. - and means of communication - e.g. radio, mobile phone, word-of-mouth, etc. For the second purpose, connections were drawn by the members of the community between information sources and the actions needed for climate change adaptation. Participants also assigned a weight to the connections, selecting between: strong, medium or a weak connection.</p> <p>Notes about the discussions and opinions expressed by participants, written down by the research team, are also included.</p> <p>Together with the data, PDF files describing metadata and detailed methodology followed are included.</p>
Farmer adaptive behavior and risk management in EU agriculture
<p>Risk and risk management are essential elements of agriculture and affect the wellbeing of farm households. Farmers react to production, market and institutional risks and challenges by taking measures on or off the farm. Such risk management measures are often costly and have implications for up- and downstream industries as well as the environment. The risk exposure of European farms is increasing. For example, climate change will increase the frequency and magnitude of extreme weather events like droughts, heatwaves and heavy rainfalls that potentially have detrimental effects on agricultural production. Thus, the adaptive capacity and risk management options in European agriculture need to be improved. Policy shall support this process. Policies are needed to support a diversity of risk management solutions and not only focus on a few solutions. Strategies to cope with risk often go beyond the level of the individual farm. Cooperation, learning and sharing of risks play a vital role in European agriculture and shall be strengthened. Thus, coordinated policies targeting beyond the individual farm and considering all the stakeholders involved in the risk management strategies are needed to ensure their effective implementation. Moreover, policies need to facilitate to take full advantage of the rapid technological progress and improved data availability (e.g. based on satellite imagery) to develop a wider set of risk management strategies.</p>
Robots for Microfarms (ROMI) - Farmers Dashboard Video - D3.4
<p><strong>The following video shows the functionalities and usage of the Farmer’s Dashboard developed within the Robots for Microfarms (ROMI) project funded by EU Grant 773875</strong></p> <p><em>You can also watch it on <a href="https://www.youtube.com/channel/UCT55o32SE30a8pTu-chatTA/videos">Youtube</a></em></p> <p><strong>Video content:</strong></p> <ul> <li><em>Farmers Dashboard Summary.mp4</em></li> <li><em>Tell me more - a deeper dive into the Cablebot.mp4</em></li> <li><em>Tell me more - a deeper dive into the Farmers Dashboard.mp4</em></li> </ul> <p> </p> <p><strong>Videos summary:</strong></p> <p><br> The Farmer’s Dashboard is a farming tool that provides daily automated insights about your crops. It helps with mapping of crop bed, the location and identification of individual plants, and the extraction of their growth curves from the collected data. </p> <p>The dashboard benefits polycrop farmers and researchers. It opens-up technology and practices common to industrial-scale agriculture, making them accessible and useful to ecological and sustainable farmers.</p> <p>It relies on an automated system for data acquisition, a set of tools for image analytics, and finally, an online platform for spatial management and data visualization. The data can be provided by different types of devices: a cablebot, a drone or a rover according to the configuration of each farm. </p> <p>The Cable Bot can be fixed above a crop bed using a tensioned cable, which is especially easy using a polytunnel. We can use the manual remote to correctly position the camera, to capture all of the crops. Once set up, the Cable Bot will move multiple times a day across the crop bed, taking high definition images and sending them to a ROMI server. The images are assembled into a unique portrait of your crop bed. After plants are detected, a catalogue of individual plants is created. By comparing them with historical data, we can obtain plant growth curves. All of the information is then combined into a weed map which is made available on the Farmers Dashboard website.</p> <p>Because of the legal restrictions on the use of drones and because of the rapid evolution of the drone market, the ROMI project has decided to direct its effort to a hardware solution that complements the existing tools: the Cablebot. Multiple iterations were needed to achieve a powerful yet robust and low-cost solution. The current system is a fully automated imaging device able to collect data on a high variety of crops. </p> <p>The first iteration of the software focuses on the mapping of crop rows, the location and identification of individual plants, and the extraction of their growth curves from the collected data. </p> <p>The rover is available as an Open Source project. All of the source code and plans are freely available. That allows us to improve the design over time using input from farmers and engineers. That is also why we made the design modular using components that can be found “off-the-shelf” or that can be produced using 3D printers and laser cutters. People with development skills can also contribute. Our software is available online on Github. That makes the Romi Farmers Dashboard an excellent platform to experiment with innovative tools for farming.</p>
CONSOLE_WP3_Task3.2_ Pan-EU survey of farmers and other rural landowners_AT_2022.10.25
<p>Dataset containing information about the structural equation model carried out in Austria for the CONSOLE (CONtract Solutions for Effective and lasting delivery of agri-environmental-climate public goods by EU agriculture and forestry) Project</p>
Maize management and yield of smallholder farmers in Sub-Saharan Africa between 2016 and 2022
<p>Yield and management practices data were collected from smallholders’ maize fields from 2016 to 2022. All fields corresponded to maize grown in pure stands (no intercropping). Data were collected from five maize producing regions in Sub-Saharan Africa: (i) north-central Nigeria (<em>n</em> = 115), (ii) Rwanda and Burundi (<em>n</em> = 2720), (iii) central Zambia (<em>n</em> = 861)<strong>,</strong> (iv) southwest Tanzania (<em>n</em> = 3710), and (v) eastern Uganda and western Kenya (<em>n</em> = 7367). Data were collected by One Acre Fund (https://oneacrefund.org/), an NGO that provides smallholder farmers access to agricultural training, credit, crop insurance services, and farming supplies. About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. </p> <p>Maize grain yield, plant density, and row spacing were measured in two randomly placed boxes of 36 square meters at harvest, avoiding field edges. Field geolocation was recorded in 70% of the observations. When missing, the field geolocation was defined based on the nearby town (21%) or associated district (9%) location for the purpose of retrieving climate data. Management practices associated with each field were reported by farmers, including sowing and harvest dates, cultivar name, fertilizer inputs (types and total quantities for both organic and inorganic), fertilization method, liming, weeding, and pesticides (mainly insecticides to control fall armyworms). Farmers also reported the incidence of adversities (such as pests, diseases, Striga witchweed, hail, and excess water). Field size was reported by farmers and, in those cases in which farmers could not provide an accurate measure of their field size, or there was a strong indication of mistakes (e.g., nutrient fertilizer rates out of range), One Acre Fund personnel took in-situ measurements to determine field size. Input rates per hectare were calculated as the ratio of the farmer-reported input amount and field size. Data were subjected to quality control to remove unlikely values. Maize yield outliers were detected with a Bonferroni Outlier Test. Observations with plant densities and fertilizer rates higher than four standard deviations from the mean were excluded as well as those without geolocation, no N or P data, and atypical sowing dates. After quality control, the database contains a total of 14,773 field observations.</p> <p>Inorganic fertilizer rates were converted to nutrient rates (in elemental nutrients) following typical fertilizer nutrient contents. Organic fertilizers were encoded separately in two binary variables and one continuous variable, indicating whether compost was used, if that compost contained manure, and compost application rate. Likewise, cultivars were classified into hybrids or open pollination varieties (OPVs), which included local varieties, retained seed, and improved OPVs. For hybrids, we retrieved the associated crop cycle maturity (short, medium, and long), disease tolerance traits, and year of release from companies’ seed catalogs. Reported incidence of diseases and insect pests (e.g., anthracnose, aphids, blight, cutworms, drought, fall armyworm, stemborer, termites, and stalk or kernel rot) were simplified to two binary variables indicating whether the crop was affected by pests and/or diseases. Infestation by parasitic witchweeds (Striga hermonthica and S. asiatica) was considered as a separate variable. Fertilization methods were also simplified to whether the fertilizer was applied inside a hole or broadcasted in the surface. Number of weeding operations was simplified to zero, one or two or more weeding per season. Sowing dates were expressed as a deviation from the estimated average sowing date for each climate zone-season combination. Fields were grouped based on their location using the climate zone scheme developed by the Global Yield Gap Atlas Project (www.yieldgap.org). Isolated observations (more than three standard deviations from the median distance across sites within the climate zone) were excluded from their group. In the case of climate zones with two maize seasons, each crop season was considered as a separate group. Field elevation was retrieved from the Amazon Web Services Terrain Tiles. Total precipitation during the growing season, as well as for early, flowering, and grain filling phases, was retrieved from CHIRP. For observations with field-level coordinates data, root-zone plant-available water-holding capacity was retrieved from the World Soil Information database, and soil clay content, pH, organic carbon, and effective cation exchange capacity from iSDA. Lastly, the topography wetness index (TWI) was calculated from the elevation data. </p> <p>Table 1. List of survey-derived variables.</p> <div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Type</strong></td> <td><strong>Unit</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>plant_date_dev</td> <td>discrete</td> <td>days</td> <td>sowing date deviation from cluster average</td> </tr> <tr> <td>pl_m2</td> <td>continuous</td> <td># m2</td> <td>plant density (plants per area)</td> </tr> <tr> <td>row_spacing</td> <td>continuous</td> <td>cm</td> <td>distance between rows</td> </tr> <tr> <td>hybrid</td> <td>binary</td> <td>-</td> <td>Was a commercial hybrid seed used?</td> </tr> <tr> <td>hyb_mat</td> <td>ordinal</td> <td>-</td> <td>hybrid maturity (early, medium, late)</td> </tr> <tr> <td>hyb_yor</td> <td>continuous</td> <td>-</td> <td>Year of release of the cultivar</td> </tr> <tr> <td>hyb_tol_mln</td> <td>binary</td> <td>-</td> <td>Tolerance to maize lethal necrosis</td> </tr> <tr> <td>hyb_tol_msv</td> <td>binary</td> <td>-</td> <td>Tolerance to maize streak virus</td> </tr> <tr> <td>hyb_tol_gls</td> <td>binary</td> <td>-</td> <td>Tolerance to gray leaf spot</td> </tr> <tr> <td>hyb_tol_nclb</td> <td>binary</td> <td>-</td> <td>Tolerance to northern corn leaf blight</td> </tr> <tr> <td>hyb_tol_rust</td> <td>binary</td> <td>-</td> <td>Tolerance to rust</td> </tr> <tr> <td>hyb_tol_ear_rot</td> <td>binary</td> <td>-</td> <td>Tolerance to ear rot</td> </tr> <tr> <td>N_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>N fertilization rate</td> </tr> <tr> <td>P_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>P fertilization rate</td> </tr> <tr> <td>K_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>K fertilization rate</td> </tr> <tr> <td>compost</td> <td>binary</td> <td>-</td> <td>Was compost applied?</td> </tr> <tr> <td>comp_t_ha</td> <td>continuous</td> <td>t/ha</td> <td>compost rate</td> </tr> <tr> <td>manure</td> <td>binary</td> <td>-</td> <td>Did the compost contain manure?</td> </tr> <tr> <td>fert_in_hole</td> <td>binary</td> <td>-</td> <td>Was the fertilizer applied in a hole?</td> </tr> <tr> <td>lime_kg_ha</td> <td>continuous</td> <td>kg/ha</td> <td>lime rate</td> </tr> <tr> <td>weeding</td> <td>discrete</td> <td>#</td> <td>number of times the plot was weeded</td> </tr> <tr> <td>pesticide</td> <td>binary</td> <td>-</td> <td>Was any pesticide applied?</td> </tr> <tr> <td>disease</td> <td>binary</td> <td>-</td> <td>Was yield affected by diseases?</td> </tr> <tr> <td>pest</td> <td>binary</td> <td>-</td> <td>Was yield affected by pests?</td> </tr> <tr> <td>striga</td> <td>binary</td> <td>-</td> <td>Was yield affected by the Striga weed?</td> </tr> <tr> <td>water_excess</td> <td>binary</td> <td>-</td> <td>Was yield affected by water excess (heavy rain or flooding)?</td> </tr> </tbody> </table> <p> </p> <p><strong>Table 2. List of environmental variables. </strong></p> <div> </div> <div> <table> <tbody> <tr> <td><strong>Name</strong></td> <td><strong>Unit</strong></td> <td><strong>Spatial resolution</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>GDD</td> <td>°C days</td> <td>30 arc-sec (1km)</td> <td>Growing degree days</td> <td>www.worldclim.org</td> </tr> <tr> <td>AI</td> <td>unitless</td> <td>30 arc-sec (1km)</td> <td>Aridity Index (annual precipitation over potential evapotranspiration)</td> <td>www.worldclim.org</td> </tr> <tr> <td>TS</td> <td>°C</td> <td>30 arc-sec (1km)</td> <td>Temperature seasonality</td> <td>www.worldclim.org</td> </tr> <tr> <td>season_prec</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Total rainfall during the maize season (10% of planting to 50% of the harvest)</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_1</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the first third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_2</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the second third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>season_prec_3</td> <td>mm</td> <td>3 arc-min (5.6 km)</td> <td>Rainfall during the last third of the season</td> <td>www.chc.ucsb.edu/data/chirps</td> </tr> <tr> <td>elev</td> <td>m.a.s.l.</td> <td>75 meters</td> <td>Elevation (altitude) above sea level</td> <td>registry.opend26ata.aws/terrain-tiles</td> </tr> <tr> <td>soil_rzpawhc</td> <td>mm</td> <td>1 km</td> <td>Root zone plant-available water holding capacity</td> <td>www.isric.org</td> </tr> <tr> <td>soil_clay</td> <td>%</td> <td>30 meters</td> <td>Clay content at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_pH</td> <td>-</td> <td>30 meters</td> <td>pH (H2O) at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_orgC</td> <td>g/kg</td> <td>30 meters</td> <td>Organic carbon at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>soil_ECEC</td> <td>cmolc/kg</td> <td>30 meters</td> <td>Effective cation exchange capacity at 0-20cm soil depth</td> <td>www.isda-africa.com </td> </tr> <tr> <td>twi</td> <td>unitless</td> <td>75 meters</td> <td>Topographic Wetness Index</td> <td>calculated from elevation</td> </tr> </tbody> </table> </div> </div> </div>
Adaptive Behavior of Farmers Under Consecutive Droughts Results In More Vulnerable Farmers: A Large-Scale Agent-Based Modeling Analysis in the Bhima Basin, India
Open the record for dataset details and reuse information.
Dataset: Farmers National Banc Corp. (FMNB) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Farmers & Merchants Bancorp, Inc. (FMAO) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Farmer Bros. Co. (FARM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Sprouts Farmers Market, Inc. (SFM) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Data on the administrative workload and perceived administrative burden of farmers in Switzerland
<table> <tbody> <tr> <td> <p>We present data from a paper-and-pencil survey of Swiss farmers. The survey was mailed to 2,000 randomly selected Swiss farmers from the two largest Swiss language regions (German and French) in February 2019. A reminder was sent in April 2019. The response rate was around 40% (N = 808). In the main part of the survey, we collected quantitative data on farmers’ workload and perceived burden due to (1) overall farming activities, (2) administrative activities related to the application of direct payments, and (3) other office work related to farm planning, bookkeeping, purchasing, and sales. We also asked farmers to rate their current workload and perceived administrative burden compared to five years earlier. We also collected data on the perceived burden of using e-government services, the administrative workload of various voluntary direct payment schemes, and the workload of inspections and sanctions. We collected personal information about the farmers, such as age, education, work experience on the farm, work outside the farm, and their political activities (e.g. as a board or executive member in political or agricultural organisations). Finally, the farmers were asked to rate a series of statements regarding agricultural policy measures, the importance of inspection measures, the obligation to provide proof of eligibility for direct payments, information on current policy measures, and the justification of penalties for non-compliance with environmental or animal welfare standards. The survey results showed that, on average, Swiss farmers spent 3–5% of their total working time on administrative tasks. Based on a 60-hour working week, this means that, on average, farmers spent about 1.8–3 hours per working week on administrative activities. The farmers rated the perceived burden of administrative activities as higher than the burden of overall farming activities or other office work. The results also showed that the time spent on administrative activities and the associated perceived administrative burden had increased compared to five years earlier. Finally, the results showed that 28% of the Swiss farmers had received a penalty for non-compliance with direct payment regulations.</p> <p> </p> </td> </tr> </tbody> </table>
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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.