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210 results for “maximum temperature”
Marcell Experimental Forest daily maximum and minimum air temperature, 1961 - ongoing
This data publication contains daily maximum and minimum air temperature collected from 1961-ongoing at the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. The data come from three long-term meteorological monitoring stations.
Maximum temperature at El Verde Field Station, Rio Grande, Puerto Rico since October 1992
Daily maximum air temperature at the El Verde Field Station since October 1992. Temperature is measured daily using a manual Min-Max thermometer placed in a wooded box in the understory. The box is mostly shaded by surrounding trees, but it is more exposed to sunlight after hurricanes defoliate the forest canopy (e.g., hurricane Maria in 2017). Measurements are available for workdays only, as a technician at the station collects measurements. The thermometer is reset after each reading. Notes: • An automatic Hobo pendant sensor complements reading (see El Verde Field Station Air temperature from automatic sensor). • Daily minimum air temperature available in a separated data set. • Data from before 1992 is available in dataset #181 (1975 -August 1992). Data-missing gaps were filled in by extrapolated data from other sources, making the subsequent manipulations less valuable for interpreting long term trends. Daily emperature has been measured at the El Verde Field Station since 1975 (see methods). These data was divided into two data set: #16, having data from 1992 to current and #181 which has data from1975 till August 1992. Data-missing gaps where filled in by extrapolated data from other sources, making the subsequent manipulations less valuable for interpreting long term trends. Monthly averages have been calculated. Maximum values for maximum temperature were recorded from May to October with a range from 29 to 30 and peaks of 29.7 Centigrade in October. The months of October through December show the most dramatic increase, specially December (see chart). Highest average maximum temperatures during these years were recorded in 1998 and 1999 (See chart). Max monthly temperatures appear to be increasing from this years on. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as pa
Maximum temperature at El Verde Field Station, Rio Grande, Puerto Rico from January 1975 to August 1992
Daily emperature has been measured at the El Verde Field Station since 1975 (see methods). Average record show that maximum values for maximum temperature recorded from May to October with a range from 29 to 30 and peaks of 29.7 Centigrade in October. The months of October through December show the most dramatic increase, specially December. Highest average maximum temperatures during these years were recorded in 1998 and 1999. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
NOAA's National Climatic Data Center including maximum temperature and USFS RDA datasets
This dataset was originally established as a subset of relevant NOAA monthly average maximum air temperature data. This has been replaced with links to NOAA station websites which contain this data, please visit these links in the dataset file here. Previously, maximum air temperature at two stations in or near the LEF were compiled from the NOAA National Climate Data Center and posted here. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018
<p>This map shows the maximum land surface temperature for the wet season of the Greater Paramaribo Region, Suriname, 2016-2018. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite and were obtained from the United States Geological Survey (USGS). A detailed description is provided in the metadata document.</p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Wet season standardised maximum land surface temperature of the Greater Paramaribo Region 2016-2018, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696837,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></p>
Dry season standardised maximum land surface temperature of the Greater Paramaribo Region 2015-2019
<p>This map shows the maximum land surface temperature for the dry season of the Greater Paramaribo Region 2015-2019. The satellite images stem from the Landsat 8 OLI/TIRS (Operational Landsat Imager/Thermal Infrared Sensor) satellite. See details in the metadata document.</p><p>This map was made for the Tropenbos Suriname and the University of Twente-Faculty Geo-information Science and Earth Observation (ITC) project "Naar een groen en leefbaarder Paramaribo" and must be accredited as follows: </p><p><i>Tom Remijn, Lisa Best, Rudi van Kanten, Nina Schwarz , Louise Willemen, 2020, Dry season standardised maximum land surface temperature of the Greater Paramaribo Region 2015-2019, product of 'Naar een groen en leefbaarder Paramaribo' by Tropenbos Suriname and University of Twente-ITC.</i> DOI: 10.5281/zenodo.7696767,<i> licensed under the </i><a href="https://creativecommons.org/licenses/by-nc-sa/4.0/"><i>Creative Commons License CC BY-NC-SA 4.0</i></a><i>.</i></p><p> </p>
seNorge/TX: daily maximum temperature over Norway
<p>seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TX) fields for the 66-year time period 1957-2022. TX is the maximum temperature consistent with TG/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018</p>
seNorge/TXa: daily maximum temperature over Norway
<p>seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXa) fields for the 66-year time period 1957-2022. TXa is the maximum temperature without checking for consistency with TG/TM/TN. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018</p>
ChinaHighTEMmax: Daily Seamless 1 km Maximum Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>maximum air temperature </strong>(TEMmax) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.98 and a root-mean-square error (RMSE) of 1.49 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMmax dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
GSDM-WBT: Global station-based daily maximum wet-bulb temperature data for 1981-2020
<p>The wet-bulb temperature integrates the temperature and humidity to comprehensively describe the thermal environment and the energy regulation of human bodies. Daily maximum wet-bulb temperature is an important indicator to be used for research on extreme humid heat. GSDM-WBT is a new dataset of global station-based daily maximum wet-bulb temperature, which was produced through calculating wet-bulb temperature, data quality control, infilling missing values and homogenisation based on the HadISD station-based observations and the NCEP-DOE reanalysis data. GSDM-WBT covers the complete daily series of 1834 stations around the world from 1981 to 2020. We provide the NetCDF files of GSDM-WBT for each station and one compressed file containing all data.</p>
seNorge/TXb: daily maximum temperature over Norway
<p>seNorge_2018 is a collection of observational gridded datasets for the Norwegian mainland. This dataset contains the daily maximum temperature (TXb) fields for the 65-year time period 1957-2021. TXb is the maximum temperature consistent with TM/TN (yesterday at 18 UTC / today at 18 UTC). The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018</p>
Maximum temperature data from thermal safety assessment of type 21700 lithium-ion batteries with NMC, NCA and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)
<p>Data of safety investigation and thermal abuse behavior of commercial type 21700 LIB cells is provided.</p> <p>It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC.</p> <p>Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at different states of charge (SOC) from 0 to 100.</p> <p>Different cathode chemistries are compared (NMC, NCA and LFP), as well as for NCA chemistry, the high energy (HE) and high power (HP) cell design.</p> <p>For each cell, data includes the maximum temperature measured during thermal abuse at the surface on the center of the cell. Additionally, the mean value and standard deviation for each cell type and state of charge is provided.</p> <p>This data is supporting this article in the journal Batteries:</p> <p><a href="https://doi.org/10.3390/batteries9050237">https://doi.org/10.3390/batteries9050237</a></p> <p>Additional supporting material to this article are the exothermal data for thermal abuse, that are published here:</p> <p><a href="https://doi.org/10.5281/zenodo.7707929">https://doi.org/10.5281/zenodo.7707929</a></p> <p> </p>
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), Andrews Experimental Forest
Mean monthly maximum and minimum air temperature spatial grids (1971-2000), adjusted for the effects of solar radiation and sky view factors, Andrews Experimental Forest. Maps were created using PRISM (Parameter-elevation Regressions on Independent Slopes Model), developed by Dr. Christopher Daly at Oregon State University’s PRISM Climate Group in 2010 (prism.oregonstate.edu). Grids were exported into ASCII format from GRASS GIS software; values are in degrees C x 100. Spatial resolution is 50 meters. Two sets of temperature values are available: (1) values derived from an interpolation of point station temperature values accounting for elevation; and (2) values from (1), adjusted for effects of solar radiation exposure and sky view factors. Radiation exposure and sky view factors were calculated from a two-stream solar radiation model that accounts for elevation, slope, aspect, and shading from adjacent pixels on a 50-m digital elevation model. Temperature data were obtained from selected benchmark and reference stand climate stations within the HJ Andrews, as well as National Weather Service Cooperative (COOP) and USDA NRCS Snow Telemetry (SNOTEL) stations in the vicinity. Due to the sparseness of the station data outside the Andrews, values outside the Andrews are considered to have high uncertainty. Temperature values assume an open site with no canopy cover, so are not appropriate for describing temperatures within the forest canopy. See MS033 for radiation grids used to make the radiation adjustments.
Supplement A. Wolf et al: 'Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum'
<p>This repository contains all proxy data presented in A. Wolf et al, "Western Caucasus regional hydroclimate controlled by cold-season temperature variability since the Last Glacial Maximum". The data can be used to replicate figures and analyses presented in the main text. Additionally, data can be accessed in the supplement material and in the data availability statement. </p>
Data and analysis from: Body mass, temperature, and depth shape the maximum intrinsic rate of population increase in sharks and rays
<p>An important challenge in ecology is to understand variation in species' maximum intrinsic rate of population increase, 𝑟<sub>𝑚𝑎𝑥</sub>, not least because 𝑟<sub>𝑚𝑎𝑥</sub> underpins our understanding of the limits of fishing, recovery potential, and ultimately extinction risk. Across many vertebrate species, terrestrial and aquatic, body mass and environmental temperature are important correlates of 𝑟<sub>𝑚𝑎𝑥</sub>. In sharks and rays, specifically, 𝑟<sub>𝑚𝑎𝑥</sub> is known be lower in larger species, but also in deep-sea ones.</p> <p>We use an information-theoretic approach that accounts for phylogenetic relatedness to evaluate the relative importance of body mass, temperature and depth on 𝑟<sub>𝑚𝑎𝑥</sub>. We show that both temperature and depth have separate effects on shark and ray 𝑟<sub>𝑚𝑎𝑥</sub> estimates, such that species living in deeper waters have lower 𝑟<sub>𝑚𝑎𝑥</sub>. Furthermore, temperature also correlates with changes in the mass scaling coefficient, suggesting that as body size increases, decreases in 𝑟<sub>𝑚𝑎𝑥</sub> are much steeper for species in warmer waters.</p> <p>These findings suggest that there are (as-yet understood) depth-related processes that limit the maximum rate at which populations can grow in deep sea sharks and rays. While the deep ocean is associated with colder temperatures, other factors that are independent of temperature, such as food availability and physiological constraints, may influence the low 𝑟<sub>𝑚𝑎𝑥</sub> observed in deep sea sharks and rays. Our study lays the foundation for predicting the intrinsic limit of fishing, recovery potential, and extinction risk species based on easily accessible environmental information such as temperature and depth, particularly for data-poor species.</p> <p>This repository contains the data and a minimum working example of the model-fitting process used for the article "Body mass, temperature, and depth shape productivity in sharks and rays", which is currently in press at <em>Ecology and Evolution</em>.</p>
TreeGOER Global Zones: Global atlas for the Climatic Moisture Index (CMI), Maximum Climatological Water Deficit (MCWD) and the number of months with average temperature > 10 degrees C (Tmo10)
<p>The <strong>TreeGOER (Tree Globally Observed Environmental Ranges)</strong> database documents the environmental ranges for 48,129 tree species and is available from files archived at <a href="https://doi.org/10.5281/zenodo.7922927">https://doi.org/10.5281/zenodo.7922927</a>. Details on the preparation of this database from 30 arc-second global grid layers are provided by Kindt, R. (2023). <strong>TreeGOER: A database with globally observed environmental ranges for 48,129 tree species</strong>. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>. The atlas from this archive was designed to be used together with TreeGOER and possibly also with the <a href="https://worldagroforestry.org/output/globalusefulnativetrees">GlobalUsefulNativeTrees</a> database (Kindt et al. <a href="https://www.nature.com/articles/s41598-023-39552-1">2023</a>) to allow users to filter suitable tree species based on environmental conditions of the planting site.</p> <p><strong>TreeGOER</strong> includes a file (<em>TreeGOER_Tmo10_classes.txt</em>) that documents the distribution of species in zones defined by the number of months with average temperature > 10 degrees C. <strong>TreeGOER</strong> also includes a file (<em>TreeGOER_CMI_classes.txt</em>) that documents the distribution of species in zones defined by the Climatic Moisture Index (CMI). The atlas provided here shows the global distribution of the Tmo10 zones and CMI zones at high resolution on six sheets each, including three sheets in the northern hemisphere and three sheets in the southern hemisphere.</p> <p>The atlas also includes six sheets that show the global distribution of the Maximum Climatological Water Deficit (MCWD), another environmental variable covered by the <strong>TreeGOER</strong> database.</p> <p> </p> <table> <tbody> <tr> <td><strong>Zone</strong></td> <td><strong>Classes</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>Tmo10</td> <td> Tmo10 = 12 + Bio06 >= 18</td> <td>tropical (minimum temperature of coldest month 18 degrees C or higher)</td> </tr> <tr> <td> </td> <td>Tmo10 = 12 + Bio06< 18</td> <td>tropical (minimum temperature of coldest month less than 18 degrees C)</td> </tr> <tr> <td> </td> <td>8 ≤ Tmo10 < 12</td> <td>subtropical</td> </tr> <tr> <td> </td> <td>4 ≤ Tmo10 < 8</td> <td>temperate</td> </tr> <tr> <td> </td> <td>1 ≤ Tmo10 < 4</td> <td>boreal</td> </tr> <tr> <td> </td> <td>Tmo10 < 1</td> <td>polar</td> </tr> <tr> <td>CMI</td> <td>CMI ≥ 0.5</td> <td>P >= 2 * PET</td> </tr> <tr> <td> </td> <td>0 ≤ CMI < 0.5</td> <td>PET <= P < 2 * PET</td> </tr> <tr> <td> </td> <td>−0.35 ≤ CMI < 0</td> <td>0.65 <= P/PET < 1</td> </tr> <tr> <td> </td> <td>−0.5 ≤ CMI < −0.35</td> <td>dry sub-humid</td> </tr> <tr> <td> </td> <td>−0.8 ≤ CMI < −0.5</td> <td>semi-arid</td> </tr> <tr> <td> </td> <td>−0.95 ≤ CMI < −0.8</td> <td>arid</td> </tr> <tr> <td> </td> <td>CMI < −0.95</td> <td> hyper-arid</td> </tr> <tr> <td>MCWD</td> <td>MCWD ≤ -100</td> <td> </td> </tr> <tr> <td> </td> <td>−200 ≤ MCWD < −100</td> <td> </td> </tr> <tr> <td> </td> <td>−400 ≤ MCWD < −200</td> <td> </td> </tr> <tr> <td> </td> <td>−600 ≤ MCWD < −400 </td> <td> </td> </tr> <tr> <td> </td> <td>−800 ≤ MCWD < −600</td> <td> </td> </tr> <tr> <td> </td> <td>−1000 ≤ MCWD < −800</td> <td> </td> </tr> <tr> <td> </td> <td>−1250 ≤ MCWD < −1000</td> <td> </td> </tr> <tr> <td> </td> <td>−1500 ≤ MCWD < −1250</td> <td> </td> </tr> <tr> <td> </td> <td>−1750 ≤ MCWD < −1500 </td> <td> </td> </tr> <tr> <td> </td> <td>−2000 ≤ MCWD < −1750 </td> <td> </td> </tr> <tr> <td> </td> <td>−2500 ≤ MCWD < −2000 </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>A fourth map series in the atlas combines information from the Climatic Moisture Index with the distribution of 52,602 cities that were included in the CitiesGOER database, available from <a href="https://doi.org/10.5281/zenodo.8175429">https://doi.org/10.5281/zenodo.8175429</a><a name="_Hlk141002106"></a><br></p> <p>Maps were created from the environmental raster layers used to create the TreeGOER via the <a href="https://cran.r-project.org/web/packages/terra/">terra package</a> (Hijmans et al. 2022, version 1.7-46) in the <a href="https://cran.r-project.org/">R 4.2.1 environment</a>.</p> <p>Added country boundaries were obtained from <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/">Natural Earth</a> as <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip">Admin 0 – countries vector layers</a> (version 5.1.1). Also added after obtaining them from Natural Earth were <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_boundary_lines_disputed_areas.zip">Admin 0 – Breakaway, Disputed areas</a> (version 5.1.0, coloured yellow in the atlas), <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/cultural/ne_10m_roads.zip">Roads</a> (version 5.0.0, coloured red in the atlas) and <a href="https://www.naturalearthdata.com/http/www.naturalearthdata.com/download/10m/physical/ne_10m_lakes.zip">Lakes</a> (version 5.0.0, coloured darkblue in the atlas).</p> <p>For countries where the GlobalUsefulNativeTrees database included subnational levels, boundaries were added and depicted as dot-dash lines. These subnational levels correspond to level 3 boundaries in the World Geographical Scheme for Recording Plant Distributions. These were obtained from <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a>. Check <a href="https://github.com/tdwg/wgsrpd/blob/master/109-488-1-ED/2nd%20Edition/TDWG_geo2.pdf">Brummit 2001</a> for details such as the maps shown at the end of this document.</p> <p>When using the TreeGOER Global Zones atlas in your work, cite this depository and the following:</p> <ul> <li>Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1‐km spatial resolution climate surfaces for global land areas. <em>International Journal of Climatology</em>, <em>37</em>(12), 4302–4315. <a href="https://doi.org/10.1002/joc.5086">https://doi.org/10.1002/joc.5086</a></li> <li>Title, P. O., & Bemmels, J. B. (2018). ENVIREM: An expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. <em>Ecography</em>, <em>41</em>(2), 291–307. <a href="https://doi.org/10.1111/ecog.02880">https://doi.org/10.1111/ecog.02880</a></li> <li>Kindt, R. (2023). TreeGOER: A database with globally observed environmental ranges for 48,129 tree species. Global Change Biology, 00, 1–16. <a href="https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914">https://onlinelibrary.wiley.com/doi/10.1111/gcb.16914</a>.</li> </ul> <p> </p> <p>The development of the TreeGOER Global Zones atlas (including development of version 2024.06) was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>.</p> <p> </p>
August–September temperature reconstruction over the period 1792–2020 based on a tree-ring maximum latewood density
<p>We present a late summer (August–September) temperature reconstruction over the period<br> 1792–2020 based on a tree-ring maximum latewood density (MXD) chronology for the southern Tibetan Plateau (TP).<br> The reconstruction explained 66.2% of the variance in the instrumental temperature records during the calibration period<br> 1960–2020.</p>
Data and analysis from: Body mass, temperature, and depth shape the maximum intrinsic rate of population increase in sharks and rays
Open the record for dataset details and reuse information.
1 km Monthly Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_timeseries)
<p>ChinaClim_timeseries is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
The effect of midnight temperature maximum winds on post-midnight equatorial spread F: data used for this study
<p>Supporting Information for “The effect of midnight temperature maximum winds on post-midnight equatorial spread F”</p> <p>J. Krall 1 , D. Hickey 2 , J. D. Huba 3 , and P. B. Dandenault 4<br> 1 Plasma Physics Division, Naval Research Laboratory, Washington, District of Columbia, USA<br> 2 Space Science Division, Naval Research Laboratory, Washington, District of Columbia, USA<br> 3 Syntek Technologies, Fairfax, VA, USA<br> 4 Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, USA</p> <p>Contents</p> <p>1. Text-formatted data for Figure 1; this data also appears in Figures 2, 3 and 6.<br> 2. Text-formatted data for Figure 3; this data also appears in Figure 6.<br> 3. Text-formatted data for Figure 4.<br> 4. Text-formatted data for Figure 5.<br> 5. Text-formatted data for Figure 7.<br> 6. Text-formatted data for Figure 8.<br> 7. Text-formatted data for Figure 9.<br> 8. nation_2013_361_windfield_fit.txt, wind data from NATION<br> 9. MENTAT_WINDS_LON-85LAT30_TO_30.zip, MENTAT output</p> <p>This supplemental information is included to satisfy data-availability requirements.<br> Each file is formatted as ASCII text. Each file contains an explanatory header.<br> In all cases save ds01.txt and nation_2013_361_windfield_fit.txt, the “data” is model output as described in the main article.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.