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.
514
datasets available to search
ShareScore release 0.9.0
Dataset results
514 results for “meteorological data”
PIE LTER 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2024
Meteorological measurements for 2024 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds 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 15-minute meteorological data from the Marshview Farm weather station located in Newbury, MA, year 2025
Meteorological measurements for 2025 at MBL Marshview Farm, Newbury, MA. Sensors conduct measurements every 5 seconds 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.
Daily meteorological data (2000-2019) from PIE LTER weather stations located in Byfield/Newbury, MA
Meteorological data daily averages and daily fluxes for stations located at Governor's Academy and MBL Marshview Farm, Newbury, MA. Data includes air temeprature, precipitation, relative humidity, solar radiation, PAR, wind and air pressure measurements. Years 2000 to 2007 the station was located at Governor's Academy, Newbury, MA and was moved July 30, 2007 to the MBL Marshview Farm field station property where it is currently located.
Meteorology Data from the Sevilleta National Wildlife Refuge, New Mexico
These files contain hourly meteorological data that were collected from a network of permanent weather stations on the Sevilleta National Wildlife Refuge as part of the Sevilleta Long Term Ecological Research Program.
Meteorology, environment and surface flux data for grassland sites in Germany
<p>Observation and model data for locations Fendt (DE-Fen), Rottenbuch (DE-RbW) and Graswang (DE-Gwg), in conjunction with selected journal publications. These data have primarily been used for investigation of surface carbon fluxes (Net Ecosystem Exchange, Gross Primary Productivity), seasonal climatic trends and land management. </p> <p>The sites are part of TERENO, a network of observatories in Germany. The TERENO Data Portal should provide other and more up-to-date information. The time period includes the ScaleX intensive observation campaigns that took place in 2015 and 2016. The data format is NetCDF4. A Jupyter notebook is available (see Related identifiers, GitLab) with technical notes and examples. </p>
Rye microgrid load and generation data, and meteorological forecasts.
<p>This dataset contains timeseries for Rye Microgrid, Trondheim, Norway. The timeseries include solar and wind power generation, consumption and historical weather forecasts.</p> <p>From <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a>:</p> <p><em>"The Rye microgrid is a pilot within the EU research project REMOTE. It is a small microgrid placed at Langørgen, in the outskirts of Trondheim, and is a small energy system designed to supply electricity to a modern farm and three households. The REMOTE projects goal for Rye Microgrid is to run the system in islanded mode.</em></p> <p><em>The system has two sources of generation – a wind turbine and a rack of PV panels. In addition, the system has two storages – a battery with high charge and discharge response, but with limited storage and losses, and a hydrogen energy system, with lower charge and discharge rates, higher losses and storage capacity. When you want to charge the hydrogen system, electricity is used to run an electrolyser that makes hydrogen from water and stores the resulting hydrogen in a tank. The process can be reversed by producing electricity from hydrogen using a fuel cell. (...)</em></p> <p><em>Morover, when local production or discharges from storages are not sufficient to cover the demand, the microgrid can draw electricity from the grid at some costs."</em></p> <p> </p> <p>For further details, see: <a href="https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf">https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf</a> and <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a></p> <p>rye_generation_and_load.csv is a comma-separated csv-file with the following columns (all values in <em>kW </em>and time as UTC):</p> <ul> <li>Consumption: Consumption of loads in system (residential and agriculture).</li> <li>Solar: Total production from all solar PV racks.</li> <li>Wind: Power production from wind turbine.</li> </ul> <p>met_data.h5: Contains historical weather forecasts data from The Norwegian Meteorological Institute (met.no) updated every 6 hours for the given location. The file is in hdf5 format. The forecasts include the following parameters: air_pressure_at_sea_level [Pa], air_temperature_2m [K], cloud_area_fraction [pu], integral_of_surface_downwelling_shortwave_flux_in_air_wrt_time [J/m<sup>2</sup>s], wind_direction_10m [deg], wind_speed_10m [m/s]</p> <p>The structure of the file is as follows:</p> <ul> <li>lat63_41_lon10_11 (coordinates) <ul> <li>[forecasted parameter] <ul> <li>forecast <ul> <li>2020-01-01T00Z (time forecast was issued) <ul> <li>axis0 (columns, index where each represent a point in a geographical grid. For example if axis=0,1,2,3, the tables contains the forecasts for the four closes points to the microgrid.)</li> <li>axis1 (rows, timestamps)</li> <li>block0_items (equal to axis0)</li> <li>block0_values (matrix, forecast values)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
Deep learning to extract the meteorological by-catch of wildlife cameras: Supporting data, models and code
<p>This repository contains the data, models and code to train and deploy deep learning models related to the paper "Deep learning to extract the meteorological by-catch of wildlife cameras" published in the journal Global Change Biology (<a href="https://doi.org/10.1111/gcb.17078"><strong>https://doi.org/10.1111/gcb.17078</strong></a>).</p>
Meteorological Data from Chios: May 2024 Baseline Measurements for the MUSICA Project
<h2><strong>May 2024 – Chios (Chiostown)</strong></h2> <h3>Introduction</h3> <p>The present meteorological data is collected from the weather station in Chiostown, located in Chios, and is published on the Zenodo platform for open access. The station is positioned at an elevation of 32 meters, and the data includes measurements of temperature, rainfall, wind speed, and wind direction, covering the period from May 1st to May 31st, 2024.</p> <h3>Purpose</h3> <p>These measurements are conducted as part of the <strong>MUSICA</strong> project, which aims to monitor climate changes in the Chiostown area and the broader region of Chios. The data for May 2024 captures the transition from spring to early summer, offering insights into the warming trend and dry conditions typical for the region during this period.</p> <h3>Content</h3> <p>The files include:</p> <ul> <li><strong>Date and time of recording</strong>: For accurate time tracking of the data.</li> <li><strong>Temperature</strong>: Daily average, maximum, and minimum temperatures in degrees Celsius (°C).</li> <li><strong>Rainfall</strong>: Daily rainfall in millimeters (mm).</li> <li><strong>Wind speed</strong>: Average and maximum daily wind speed in kilometers per hour (km/h).</li> <li><strong>Wind direction</strong>: The prevailing wind direction of the day.</li> </ul> <h3>Data Highlights for May 2024</h3> <ul> <li><strong>Highest temperature</strong>: 28.8°C, recorded on May 19th, 2024, at 18:20.</li> <li><strong>Lowest temperature</strong>: 12.3°C, recorded on May 15th, 2024, at 05:00.</li> <li><strong>Total rainfall</strong>: 1.2 mm, with the highest daily rainfall of 1.19 mm recorded on May 11th, 2024.</li> <li><strong>Highest wind speed</strong>: 56.3 km/h, recorded on May 27th, 2024, at 10:40.</li> </ul> <h3>Data Usage</h3> <p>The data is free to use. Users are welcome to download, analyze, and utilize the data for personal, educational, or research purposes, as well as for developing applications and tools that contribute to understanding and addressing weather and climate phenomena</p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
Data files for: Meteorological factors in the production of Gigantic Jets by tropical thunderstorms in Colombia
<p>Data includes:</p> <ul> <li>Gigantic jet locations and times</li> <li>Vertical profiles for GJ and null cases</li> <li>CSV files with meteorological variables per GJ event and null case</li> </ul>
Air pollution, atmospheric and local meteorological data for Graz, Austria from 2014 to end of 2021
<p>The data covers a timeframe from January 2014 to November 2021 in a daily frequency, and covers two sources:</p> <ul> <li>The environmental and pollutant data was provided by the Austrian government under the following license: CC-BY-4.0: Land Steiermark - <a href="http://data.steiermark.gv.at">data.steiermark.gv.at</a> <ul> <li>Air quality (<em>Lovric_et_al_air_pollutants.csv</em>) by means of NO<sub>2</sub>, NO, NO<sub>x</sub>, PM<sub>10</sub> and O<sub>3</sub> was measured at five sites in Graz, Austria (Süd (<em>eng. South</em>) - S, Nord (<em>eng. North</em>) - N, West (<em>eng. West</em>) - W, Don Bosco – D, Ost (<em>eng. East</em>) – O). In addition weather conditions like temperature, percipitation, relative humidity, pressure, wind speed and direction are added (<em>Lovric_et_al_local_meteorology.csv</em>)</li> </ul> </li> <li>The ERA5-Land data (<em>Lovric_et_al_era5_recalculated.csv</em>) is subject to the Copernicus licence from following source <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcds.climate.copernicus.eu%2Fcdsapp%23!%2Fdataset%2F10.24381%2Fcds.e2161bac%3Ftab%3Doverview&data=05%7C01%7Cmlovric%40know-center.at%7C2ba06457329349623a5608da631632c9%7C0d3c92e977ae4f49bd126ff29e8f1c37%7C0%7C0%7C637931244242754711%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=lt5NcIfbIRGse01Naha8bolxEkdtLmyp2VNcrz38Rk8%3D&reserved=0">https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview</a> <ul> <li>it includes following variables : <ul> <li>Cloud_Cover_Mean</li> <li>Temperature_Air_2m_Max_Day_Time</li> <li>Temperature_Air_2m_Min_Night_Time</li> <li>Wind_Speed_10m_Mean</li> </ul> </li> </ul> </li> </ul>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
Meteorological data from the experimental period of the submersion test of photovoltaic cables
<p>Meteorological data recorded by the onsite weather station (coordinates: 38°31'50.0"N 8°00'40.3"W) regarding the study of submersion of photovoltaic cables (with two different insulation materials) in freshwater and artificial seawater. The metereological data is logged with 1minute time resolution for the period from 16/10/2020 to 25/01/2021.</p> <p>The meteorological station is composed by:</p> <p>Kipp and Zonen Solys2 Sun tracker</p> <p>Kipp and Zonen CMP6 Pyranometer (horizontal global solar radiation, data units W/m2)</p> <p>RH and Air temperature sensor (air relative humidity, data units % and ambient air temperature, data units ºC)</p> <p>Rain Gauge (precipitation, data units mm)</p>
On-Glacier Meteorological Data for Haut Glacier d'Arolla, Switzerland
<p>The compiled dataset is a series of summer meteorological observations on the Swiss Haut Glacier d'Arolla (45.97°N, 7.52°E) <br>to support the analysis presented in the manuscript:</p><p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%<br> "The Decaying Near-Surface Boundary Layer of a Retreating Alpine Glacier", <br>submitted to Geophysical Research Letters. </p><p>Thomas E. Shaw1, Pascal Buri1, Michael McCarthy1, Evan S. Miles1, Álvaro Ayala2, Francesca Pellicciotti1</p><p>1 Swiss Federal Institute, WSL, Birmensdorf, Switzerland<br>2 Centro de Estudios Avanzados en Zonas Áridas, La Serena, Chile</p><p>%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%</p><p>The following files are provided:<br>1) 6 x xlsx files "Arolla_Meteorological_Data_[YEAR].xlsx"<br> contains within are tabs for:<br> i) The station locations and elevations (tab "[YEAR]_Info").<br> ii) All AWS/Tlogger data in hourly format (tab "[YEAR]_Met_Data").<br> iii) Only the hourly air temperature data for on-glacier sites (tab "[YEAR]_Ta").</p><p>2) Glacier outlines (.shp) for years 1850 (GLAMOS), 1973 (GLAMOS), 1994 (Carenzo, 2012), 1999 (Carenzo et al., 2012), 2010 (GLAMOS), 2022 (Digitised from PlanetScope imagery).<br>3) Debris cover area (.shp) derived by applying an NDSI classification of cloud filtered, summer Landsat scenes in Google Earth Engine following the approach of Scherler et al. (2018).</p><p>For the meteorological data in 1), the following variables are provided:<br>"TA" = near surface air temperature (°C).<br>"RH" - relative humidity (%).<br>"SWIN" - Shortwave incoming radiation (Wm^-2).<br>"SWOUT" - Shortwave outgoing radiation (Wm^-2).<br>"LWIN" - Longwave incoming radiation (Wm^-2).<br>"LWOUT" - Longwave outgoing radiation (Wm^-2).<br>"FF" - Wind speed (m s-1).<br>"DIR" - Wind direction (°).<br>"PP" - precipitation (mm).<br>"DEW" - dew point temperature (°C).</p><p>The term "OG" refers to an off-glacier station, which are numbered accordingly. If no variable names are given as a header in the "Met_Data" tab, then the data are air temperature values. </p><p>Data are filtered for obvious errors and errors are then removed. Data are not gap-filled as this would affect the analysis presented about patterns in air temperature data. </p><p>Data were checked and compiled by Thomas Shaw (WSL) - thomas.shaw@wsl.ch<br>Data were measured by ETH (2001-2010) and WSL as part of the Marie-Curie Project 'TEMPEST' (2021-2022).</p><p>Details of data collection and analysis can be found in:<br>Strasser et al. (2004) - 2001.<br>Carenzo (2012) - 2001-2010.<br>Shaw et al. (N.D.) 2021-2022. </p><p>%% CITED WORK</p><p>Carenzo, M. (2012). Distributed modelling of changes in glacier mass balance and runoff (Issue 20616). ETH Zurich.</p><p>Scherler, D., Wulf, H., & Gorelick, N. (2018). Global Assessment of Supraglacial Debris-Cover Extents. Geophysical Research Letters, 45(21), 11,798-11,805. https://doi.org/10.1029/2018GL080158</p><p><strong>Shaw, T. E.,</strong> Buri, P., McCarthy, M., Miles, E. S., Ayala, Á., & Pellicciotti, F. (2023). The Decaying Near-Surface Boundary Layer of a Retreating Alpine Glacier. <i>Geophysical Research Letters</i>, <i>50</i>, 1–12. <a href="https://doi.org/10.1029/2023GL103043">https://doi.org/10.1029/2023GL103043</a></p><p>Strasser, U., Corripio, J. G., Pellicciotti, F., Burlando, P., Brock, B. W., & Funk, M. (2004). Spatial and temporal variability of meteorological variables at Haut Glacier d'Arolla (Switzerland) during the ablation season 2001: Measurements and simulations. Journal of Geophysical Research, 109, D03103. https://doi.org/10.1029/2003JD003973</p><p><br> -------------</p><p> </p><p>This work was funded by the EU Horizon 2020 Marie Skłodowska-Curie Actions Grant 101026058.</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) July 2021 - April 2022
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from July 2021 to April 2022 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2020 - July 2021
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2020 to July 2021 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) May 2017 - June 2018
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from May 2017 to June 2018 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu] </p>
Lake Sunapee Gloeotrichia echinulata density near-term hindcasts from 2015-2016 and meteorological model driver data, including shortwave radiation and precipitation from 2009-2016
Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.
Meteorological data for the Manitou Experimental Forest, Colorado, USA, 1936-1997
The Manitou Experimental Forest is an outdoor research laboratory in Colorado, USA, that has been run by the USDA Forest Service’s Rocky Mountain Research Station since 1936. This data publication contains meteorological data collected at the Manitou Experimental Forest from 1936-11-11 to 1997-06-24. Precipitation amount, current temperature, maximum temperature, and minimum temperature were collected at daily to weekly intervals over most of this period. Precipitation type, aboveground wind speed, ground wind speed, and wind direction were collected at daily to weekly intervals over a portion of the period.
Seasonality of in-lake and meteorological data from seven lakes, including daily measurements of water temperature, chlorophyll-a, dissolved oxygen, ice cover, air temperature, and solar radiation
This data product supports the manuscript "Seasons and seasonality in lakes: a synthesis amid global change" (Lewis et al. 2026; in review). Data were analyzed to understand how seasonality varies among diverse lakes and variables. Specifically, this data publication includes daily mean water temperature, chlorophyll-a, and dissolved oxygen at multiple depths, ice cover (binary), air temperature and solar radiation. Data availability and collection methods differ among lakes, as described in the Methods.
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.