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Fig. 5 in Zooplankton Community Structure Of The Fish Farm Nagļi (Latvia)
Fig. 5. Mean (Zscore) of zooplankton taxa number, Shannon-Wiener index and total abundance in the ponds.
Рис. 1. Схема мониторинговых станций на озере Кенон: 1–1.6 — ТЭЦ; 2–2.1 — КСК; 3 — Нефтебаза; 4 — Центр озера; 5 — КаΑаΛинка Fig. 1. Diagram of monitoring stations on Kenon lake: 1–1.6 — TPP; 2–2.1 — KSK; 3 — Tank farm; 4 — Lake Center; 5 — Kadalinka in Toxic pollution assessment of Chita TPP-1 cooling reservoir by applying the method of head capsule morphological deformations in chironomid larvae
Рис. 1. Схема мониторинговых станций на озере Кенон: 1–1.6 — ТЭЦ; 2–2.1 — КСК; 3 — Нефтебаза; 4 — Центр озера; 5 — КаΑаΛинка Fig. 1. Diagram of monitoring stations on Kenon lake: 1–1.6 — TPP; 2–2.1 — KSK; 3 — Tank farm; 4 — Lake Center; 5 — Kadalinka
Figure 4 in Relationship between weedy rice (Oryzo sotivo) infestation level and agronomic practices in Italian rice farms
Figure 4. The three clusters identified in the two-step cluster analysis and the composition of each cluster for all the variables that contributed to clustering. The size of the circle and percentages close to each circle represent the proportion of farms pertaining to a certain category of each variable. The percentage of farms belonging to each cluster is reported in parentheses following the cluster name. (A) Cluster 1; (B) cluster 2; and (C) cluster 3.
Dataset of historical hourly information of four european wind farms for wind energy forecasting and maintenance
<p><strong>If you use this dataset please cite this paper: Sánchez-Soriano, J.; Paniagua-Falo, P.J.; Gómez Muñoz, C.Q. Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance. Data 2025, 10, 38. <a href="https://doi.org/10.3390/data10030038" target="_blank" rel="noopener">https://doi.org/10.3390/data10030038</a></strong></p> <p>For an electric company, having an accurate forecast of the expected electrical production and maintenance from its wind farms is crucial. This information is essential for operating in various existing markets such as Iberian Energy Market Operator - Spanish Hub (OMIE in its Spanish acronym), Portuguese Hub (OMIP in its Spanish acronym), and Iberian electricity market between the Kingdom of Spain and the Portuguese Republic (MIBEL in its Spanish acronym), among others. The accuracy of these forecasts is vital for estimating the costs and benefits of the handling of electricity. This article explains the process of creating the complete dataset, which includes the acquisition of the hourly information of four European wind farms as well as a description of the structure and content of the dataset which amounts to 2 years of hourly information. The wind farms are in three countries, two from Auvergne-Rhône-Alpes (France), Aragon (Spain) and the Piemonte region (Italy). The presented dataset is available and accessible to improve the forecasting and management of wind farms, especially for the detection of faults and the elaboration of a preventive maintenance plan.</p> <p>The full description of the characteristics of the dataset, as well as its components, format and methodology, can be found here: "Historical Hourly Information of Four European Wind Farms for Wind Energy Forecasting and Maintenance". Data 2025, 10, 38. <a href="https://doi.org/10.3390/data10030038" target="_blank" rel="noopener">https://doi.org/10.3390/data10030038</a></p>
Figure 1. A in Preliminary assessment of weed population in vegetable and fruit farms of Taif, Saudi Arabia
Figure 1. A map of Taif region, Kingdom of Saudi Arabia (https://images.app.goo.gl/SLRH8ep1mKncRHkm7).
Fig. 1. A in Monitoring coffee berry borer, Hypothenemus hampei (Coleoptera: Curculionidae), populations with alcoholbaited funnel traps in coffee farms in Colombia
Fig. 1. A red funnel trap used to capture CBB (Hypothenemus hampei) (A) and the methanol–ethanol lure (3: 1 ratio) enclosed in a semi-permeable membrane (B). CBB were collected in a plastic container containing 200 mL of soapy water. Photo Alex. E. Bustillo.
Fig. 2 in Monitoring coffee berry borer, Hypothenemus hampei (Coleoptera: Curculionidae), populations with alcoholbaited funnel traps in coffee farms in Colombia
Fig. 2. Number of CBB (Hypothenemus hampei) captured per trap per month (mean ± SEM) and corresponding berry infestation (%) on large coffee farms (≥ 20 ha) in Risaralda, Caldas, Colombia (A), and on small coffee farms (≤ 3 ha) in Viterbo, Caldas, Colombia (B) in 2003-2004. Note differences in axis scales.
Fig. 1. Sample completeness curve for a in Abundance and diversity of beneficial and pest arthropods in buckwheat on blueberry and vegetable farms in north Florida
Fig. 1. Sample completeness curve for a vegetable farm (dark shaded area, 38 samples) and blueberry farm (light shaded area, 19 samples) in Suwannee County, Florida. The solid line is the interpolated insect family-level richness and the dashed line is the extrapolated richness. The shaded areas represent 95% confidence intervals. Sampling effort is standardized based on the sample completeness (the proportion of sampled families to predicted families) at each sampling level, with the circle representing final sampling completeness of 38 samples taken at the vegetable farm and the triangle representing final sampling completeness of 19 samples taken at the blueberry farm.
Fig. 2 in Effects of Farming Systems on Insect Communities in the Paddy Fields of a Simplified Landscape During a Pest-control Intervention.
Fig. 2. Two-dimensional NMDS ordination of 40 insect communities sampled under different farming systems in northern Taiwan (stress = 0.18).
Above and below ground biomass and carbon content in the most common farm crops in Latvia
<p>Field measurements based assessment of above and below ground biomass of the most common farm crops in Latvia, including carbon and nitrogen stock and CO2 input into soil with plant residues under the most common management systems. Organic and integrated farmings are separated. Biomass is expressed as total biomass of all plants growing in the area, not only main species. Data for less common species are extrapolated assuming similarity with similar species or using default factors provided by the IPCC guidelines and other literature sources.</p> <p>Data area partially published (references are provided in the description). Uncertainty, which is not included in the public data set, can be provided separately.</p>
Data for a dairy farm microgrid solution
<p><strong>Load data </strong>(Farm_load_kW_data.txt)</p> <p>A pre-determined hourly data series of electricity consumption (one year). The consumption was considered independent from the microgrid operating state – network connected or islanded operation. Load was not controlled in order to obtain longer islanded operation capability, nor to minimize power exchange with the network in normal state. The dairy farm case was with about 180 cows and corresponding electricity consumption of approximately 261 MWh/a. The farm data series was created based on data from similar size farms. Daily consumption profile was based on diurnal consumption data of a large cowhouse in a winter day, and the variation from day to day throughout the year was approximated by creating sliding data series based on monthly electricity consumption. The dataset was then suitably scaled for the specified annual consumption.</p> <p><strong>PV generation data </strong>(PV_pu_data.txt)</p> <p>An hourly PV production data series for one year was created for a specific location (in Finland) based on MERRA-2 time series data on radiation [1] and air temperature [2]. The daily average radiation and temperature were scaled to match monthly values from PVGIS database [3,4]. PV panel generation (in per units) was calculated considering location and temperature, and selected panel tilt given by PVGIS ‘optimal inclination angle’. The PV generation data series was then scaled appropriately for the selected PV capacity in the case study.</p> <p> </p> <p><strong>References</strong></p> <ol> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_rad_Nx: 2d,1-Hourly, Time-Averaged, Single-Level, Assimilation, Radiation Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_flx_Nx: 2d,1-Hourly, Time- Averaged, Single-Level, Assimilation, Surface Flux Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC).</li> <li>Huld, T., Müller, R. & Gambardella, A. A new solar radition database for estimating PV performance in Europe and Africa. Sol. Energy 86, 1803–1815 (2012).</li> <li>European Communities (2012), PVGIS interactive application. Available: http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php#.</li> </ol>
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </p>
On Farm Demonstration Datasets - National Summaries
<p>This document brings together the national summaries and posters prepared on the basis of the inventory data collected, and 'supra-regional meetings' held in Northern, Southern and Eastern Europe to discuss the implications of the findings for on-farm demonstration in Europe. </p> <p> </p>
On-farm demonstration datasets in excel
<p>These files comprise 'Part A' of the questionnaire utilised to populate the inventory of on-farm demonstrations across Europe. The 'farms' file comprises the farmers who responded to the survey; the 'organisations' file the agricultural industry organisations which host or organise demonstrations (e.g. research institutions, agricultural advisory services, farming organisations). Part A identifies the topics and audiences of the demonstrations, as well as whether they are located. A search of the databases can also be conducted from the FarmDemo platform: <a href="https://farmdemo.eu/hub/">https://farmdemo.eu/hub/</a></p>
Data and R-code on a cross-sectional study of factors associated with lameness in dairy cows housed in freestall and compost-bedded pack dairy farms in southern Brazil
<p>The data correspond to a cross-sectional study designed to investigate factors associated with lameness in dairy cows on intensive farms in southern Brazil.<br> Farms: 38 freestall and 12 compost-bedded pack visited once in 2016. All lactating cows (n = 13,716) were examined and body condition score (BCS) and gait score were assessed. Additionally, some variables were collected through inspection of facilities and using data from an interview with farmers on routine herd management practices.<br> Additional information is provided in the published paper ("Factors associated with lameness prevalence in lactating cows housed in freestall and compost-bedded pack dairy farms in southern Brazil" https://doi.org/10.1016/j.prevetmed.2019.104773)</p>
Weather Data from Hull Springs Farm: 2019-09-29 to 2019-10-15
<p># General Metadata for Hull Springs Farm Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> HSF_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS)<br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Weather Data from Hull Springs Farm: 2019-09-10 to 2019-09-29
<p># General Metadata for Hull Springs Farm Atmospheric Sampling Station</p> <p>## Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <p> HSF_weather_YYYY-MM-DD_metadata.txt<br> <br> Where YYYY-MM-DD is the date that the sampling period ended.</p> <p>## File Created</p> <p> * 2019-10-27 by KF<br> <br> ## File Modified</p> <p>## Description</p> <p>These data are from a weather station installed at Hull Springs Farm near the "Yellow House" (38.121683, -76.666781) as part of the Longwood Environmental Observatory.</p> <p>All data are CC-BY and should be cited using the DOI available at https://zenodo.org/communities/leo/</p> <p>## Station Specifics</p> <p> The specific at each site are:</p> <p> * Barometric Pressure (mmHg) - Campbell Scientific CS100 Barometric Pressure Sensor<br> * Light Flux Density (kW/m^2) - Campbell Scientific Pyranometer CS300<br> * Light Total Flux (kJ/m^2) - Campbell Scientific Pyranometer CS300<br> * Rainfall (mm) - Texas Electronics TE525 Tipping Bucket<br> * Temperature (dC) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Relative Humidity (%) - Campbell Scientific CS215 Temperature and Relative Humidity Sensor<br> * Wind Speed (m/s) - RM Young 05103 Wind Speed and Direction Sensor<br> * Wind Direction (degrees from true N) - RM Young 05103 Wind Speed and Direction Sensor<br> * Data Collection - Campbell Scientific CR200 Data Logger<br> * The sensors are sampled every 15 minutes<br> <br> ## Measurement Parameters, units, and Variable Names</p> <p> * DATE - the date that the record was collected (YYYY-MM-DD)<br> * TIMESTAMP - the date and time that the datalogger assigned to the record that was collected (YYYY-MM-DD HH:MM:SS)</p> <p> * TIMESTAMP.corr - the date and time that the record was collected (YYYY-MM-DD HH:MM:SS). The date and time on the data logger was set incorrectly when the data collection began so the time stamps from the logger are incorrect. We used the file download time to identify the last actual time that data logger collected a sample and then offset the TIMESTAMP values by the difference between the recorded and actual time of the last sample collection. <br> * RECORD.x - a unique identifying number provided from the data logger for table 1 from the original downloaded data. <br> * BattV_Avg - The average battery voltage (Volts)<br> * BattV - The battery voltage at the time of the sampling (Volts)<br> * BP_mmHg_Avg - The average barometric pressure over the sampling interval (mmHg)<br> * BP_mmHg_Std - The standard deviation of the average barometric pressure (mmHg)<br> * BP_mmHg - The barometric pressure at the time of the sampling (mmHg)<br> * Rain_mm_Tot - The total rainfall during the sampling interval (mm)<br> * AirTC_Avg - The average air temperature during the sampling interval (dC) <br> * AirTC_Std - The standard deviation of the average air temperature (dC) <br> * AirTC - the air temperature at the time of the sampling (dC)<br> * RH - the relative humidity at the time of the sampling (%)<br> * RH_Min - the minimum relative humidity recorded (%)<br> * RH_TMn - the time that the minimum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS) <br> * RH_Max - the maximum relative humidity recorded (%)<br> * RH_TMx - the time that the maximum relative humidity measurement occurred (YYYY-MM-DD HH:MM:SS)<br> * RECORD.y - a unique identifying number provided from the data logger for table 1 from the original downloaded data.<br> * BattV_Min - the minimum battery voltage (Volts)<br> * SlrkW_Avg - the average light flux density during the sampling interval (kW/m^2) <br> * SlrkW_Std - the standard deviation of the average light flux density (kW/m^2)<br> * SlrkW - the light flux density at the time of the sampling (kW/m^2)<br> * SlrMJ_Tot - the total light flux (MJ/m^2) <br> * WS_ms_Avg - the average wind speed during the sampling interval (m/s) <br> * WS_ms_Std - the standard deviation of the average wind speed (m/s) <br> * WS_ms - the wind speed at the time of the sampling event (m/s)<br> * WindDir - the wind direction at the time of the sampling event (degrees from true N)<br> * WS_ms_S_WVT - the average wind speed over the previous 15 minutes (m/s)<br> * WindDir_D1_WVT - the average wind direction over the previous 15 minutes (degrees from true N)<br> * WindDir_SD1_WVT - the standard deviation of the average wind direction over the previous 15 minutes (degrees from true N)<br> </p>
Data Repository for: SOCIO-ENVIRONMENTAL IMPACTS OF OIL PALM CONTRACT FARMING SCHEMES IN THE BRAZILIAN AMAZON
<p>Contract farming is arguably a pro-poor strategy to promote rural development and minimize the social impacts of large-scale agricultural expansion. Yet, little attention has been paid to its environmental impacts, particularly in tropical landscapes. This article fills this gap by linking social and environmental analysis of oil palm contract farming in the Brazilian Amazon. The analysis presented used a mix of quantitative and qualitative methods, including household surveys, remote sensing techniques, and in-depth interviews, to assess whether the scheme managed to avoid deforestation and to contribute to livelihood improvements. The results show that the Brazilian model managed to prevent the deforestation of primary forests, but achieved limited and differentiated livelihood results. The analysis suggests that the Brazilian model is more likely to work for households with an agricultural vocation and a commercial spirit in areas with an abundant availability of degraded lands, but can hardly be a solution in frontier areas or for subsistence or more dependent households. The chapter concludes with some reflections on how contract farming schemes should be designed in order to maximize livelihood gains and minimize negative environmental impacts.</p>
Figure 3 in Organic farming and moderate tillage change the dominance and spatial structure of soil Collembola communities but have little effects on bulk abundance and species richness
Figure 3. Abundance, number of species and Berger-Parker index in samples in different management types and fields. Colors show fields. Boxplots show data distribution (n = 81 per field), horizontal lines represent the medians.
Figure 3 in Diceratocephala boschmai (Platyhelminthes: Temnocephalida) from crayfish farms in Thailand: investigation of the topographic surface and analysis of 18S ribosomal DNA sequences
Figure 3. The neighbor-joining phylogenetic tree based on the 18S rDNA gene, showing the relationships of D. boschmai with 29 other turbellarian species.
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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.