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1,648 results for “farm”
Year 2009, meteorological data, 15 minute intervals, from the MBL Marshview Farm weather station located in Newbury, MA
Year 2009 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2010, meteorological data, 15 minute intervals, from the Marshview Farm weather station located in Newbury, MA
Year 2010 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2011, meteorological data, 15 minute intervals, from the Marshview Farm weather station located in Newbury, MA
Year 2011 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2013, meteorological data, 15 minute intervals, from the Marshview Farm weather station located in Newbury, MA
Year 2013 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
PIE LTER year 2018, meteorological data, 15 minute intervals, from the PIE LTER Marshview Farm weather station located in Newbury, MA
Year 2018 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
PIE LTER year 2019, meteorological data, 15 minute intervals, from the PIE LTER Marshview Farm weather station located in Newbury, MA.
Year 2019 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
PIE LTER year 2020, meteorological data, 15 minute intervals, from the PIE LTER Marshview Farm weather station located in Newbury, MA
Year 2020 meteorological measurements at MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
Year 2007, meteorological data, 15 minute intervals, from weather station located at the Governor's Academy, Byfield, MA then moved to MBL Marshview Farm, Newbury, MA.
Year 2007 meteorological measurements at Governor's Academy and MBL Marshview Farm of air temperature, humidity, precipitation, solar radiation, photosynthetically active radiation (PAR), wind speed and direction and barometric pressure. Sensors conduct measurements every 5 secs and measurements are reported as averages or totals for 15 minute intervals. 15 minute averages are reported for air temperature, humidity, solar radiation, PAR, wind speed and direction and barometric pressure. 15 minute totals are reported for precipitation.
'Wind theft' from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution
<p>Data and namelists from Pryor S.C., Shepherd T.J., Volker P., Hahmann A.N. and Barthelmie R.J.: ‘Wind theft’ from onshore wind turbine arrays: Sensitivity to wind farm parameterization and resolution. <em>Journal of Applied Meteorology and Climatology (doi:10.1175/JAMC-D-19-0235.1)</em></p>
Database on the Performance of Current Agro-Ecological Farming Systems (AEFS) as an Input to the Modelling in WP4
<p>This database contains farm data (e.g. yields) and results (indicators) from assessments with the three decision support tools in the UNISECO case studies: SMART (www.fibl.org/en/themes/smart-en.html), Cool Farm Tool (coolfarmtool.org) and COMPAS (www.thuenen.de). This version (2.0) was developed as a benchmark for the assessment of exemplary cases of how the core dilemmas of agro-ecological transitions may be overcome at farm level and as an input to the modelling at territorial level in WP4.</p> <p>This database was created in the course of the H2020 project UNISECO. The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 773901.</p>
Data from an Experiment in farming real conditions
<p>This file presents data from an experiment conducted in 2013/2014 and 2014/2015 with farmers in their fields. The experiment aimed at identifying sustainable agricultural management practices.</p>
Figure 1 in How extensive is the effect of modern farming on bird communities in a sand dune desert?
Figure 1. Dendrogram of F- and C-transects, using group-average clustering from Bray-Curtis similarities on log-transformed bird abundances. Similarity coefficient in percent.
Mehrabi et al. 2020. The global divide in data-driven farming. Supplementary Information.
<p>This repository contains data, data sources, and code for reproducing the results of the paper Mehrabi et al. 2020. The global divide in data-driven farming. Nature Sustainability. https://doi.org/10.1038/s41893-020-00631-0.</p> <p>There are five directories included here, listed in alphabetical order. Directory SI_A contains the script for reproducing the analysis presented in the main text of the paper. Directories SI_B to SI_E contain the scripts for creating the input data sets used in the analysis.</p> <p>This is a baseline study, that is intended to be built upon and improved as and when new data become available. For issues and suggestions, please contact zia.mehrabi@ubc.ca.</p>
Policy brief on intergenerational renewal in EU-farming systems. What can policy do?
<p>For a resilient farming system, smooth and sufficient intergenerational renewal is crucial, and it has been defined as one of nine goals for the CAP post-2020. Before implementing specific policy measures and instruments, however, policy makers must determine the degree and nature of the generational renewal problem that needs to be addressed. Also, policies need to focus on increasing the attractiveness of farming as an occupation and lifestyle, as many non-entry decisions are made before measures aimed at the young farmer start to play a role. Further policy directions include increasing the mobility of land and labour, supporting the management of extreme calamities as they involve a great risk of exit and non-entry and facilitating the provision of personal and farm-specific advice and coaching. The power and responsibility of national and regional governments to address these issues is often underestimated and overlooked.</p>
Policy options for resilience-enhancing farm demographics
<p>European agriculture as well as the wider economy face substantial demographic changes in the upcoming decades. The baby boomer generation will retire within the next 10 to 15 years and the cohorts of the young generations which will enter the labour market in the next decades are much smaller in size. Accordingly, the farming sector will have to compete with other sectors and urban areas which offer attractive career prospects. The SURE-Farm project seeks to better understand the sustainability and resilience of European farming systems, including the challenges related to farm demographics and what this means for structural change. This policy brief aims to tie the findings of SURE-Farm on farm demographics with policy options which may enable resilient farm structures.</p>
Six questions to shifting the focus from more to more successful generational renewal in farming
<p>One important longer-term building-block of a farming system’s resilience is generational renewal. Vice versa, farming systems are only attractive for the younger generation to enter if they offer long-term prospects. Within the EU, regional farming systems differ enormously with regard to their organisational and demographic characteristics, their production systems, and their local natural, institutional, and infrastructural conditions. Accordingly, generational renewal is multifaceted and related to many different issues, such as whether and how farms should organise farm succession, how farms can ensure the availability of a sufficient labour force, how they can adapt to and prepare for generational and demographic changes, and how the younger generation can benefit from the training and employment opportunities in the farming sector. Fuelled by key findings of the SURE-Farm Project, the aim of this business brief is to raise awareness among farmers and stakeholders of opportunities and challenges related to the various aspects of generational renewal.</p>
Description of tools for the sustainability assessment of farms and farming systems
<p><strong>The “summary” table shows the papers and names of the tools and the link to the main publication as well as the use of the paper in each part of the analysis. We presented the variables for all the methods in a table called “method” with the result of the classification. In the table “dimension”, we presented the extraction of the dimension of sustainability and classification of methods. We did the same for the “themes” with a colour that we used for providing the descriptive statistics on the number of themes per dimension. We used three tables for each indicator that present the name of the indicator as extracted in the publication.</strong></p>
Assessment of dairy cow welfare in small-scale farming systems dataset
<p>This database was created as preparatory work for the Scientific Opinion on the assessment of dairy cow welfare in small-scale farming systems (EFSA, 2015) to collect data for the description and the categorisation of European Small-Scale Dairy Farms (SSDF) based on size, farming system and husbandry practices and (ii) to analyse the feasibility in SSDF of animal-based measures usually used for intensive farming. The Scientific Opinion was necessary to address specific expectations of consumers on locally produced food and acceptable animal welfare conditions in the context of the EU Strategy for the protection and welfare of animals 2012-2015.</p> <p>The on-farm survey was run to collect data for welfare assessment covering Austria, France, Italy and Spain. A total of 124 farms with up to 75 cows were selected based on three criteria reflecting use of local resources or enrolment in a certification scheme: (1) the type of enterprise (ownership and workers), (2) the use of inputs in the production process, including the use of local feed and local breeds, and (3) the production type (certification schemes). From 124 dairy farms visited 119 were considered as SSDF. Among the 119 farms included in the survey as non-conventional, some of them had a very small herd size (44 had less than 25 cows and one had only 10 cows) and some of them had more animals (19 farms had between 51 and 75 dairy cows).</p> <p>The database includes 53 continuous and categorical farm descriptor variables, 23 continuous and categorical risk-factor variables and 47 animal-based measures in small-scale farms. The final data model used was based on data collection at farm/herd level, pen level and animal level.</p>
Dataset supplementing Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology
<p>This dataset contains data and scripts that supplement the publication</p> <p>Lichtenberg et al. (2017) A global synthesis of the effects of diversified farming systems on arthropod diversity within fields and across agricultural landscapes. Global Change Biology. DOI: 10.1111/gcb.13714</p> <p> </p> <p>Please cite the above article if you use any of the included data or code.</p> <p> </p> <p>Files are described in README.md.</p>
Dataset for article: Integer programming for optimal yaw control of wind farms
<div> <p>This is the dataset for the article "Integer programming for optimal yaw control of wind farms". We provide the integer programs (lp-files) and corresponding solver log files for each case of our series of experiments. Submission of manuscript: September 2024 (v1). Major revision of manuscript: February 2025 (v2). Minor revision of manuscript: April 2025 (v3).</p> </div>
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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.