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4,243 results for “seasonality”
Net community production (NCP) and gross oxygen production (GOP), based on oxygen-argon ratios and triple oxygen isotopes, from seasonal NES-LTER Transect cruises in 2020
This data package provides net community production (NCP) and gross oxygen production (GOP, a measure of gross primary production) for the winter, spring, and summer Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises in 2020. Two tables are provided: a high-frequency table with NCP rates calculated from measurements of O2/Ar made continuously by an at-sea equilibrator inlet mass spectrometer (EIMS), and a low-frequency table with both NCP and GOP rates calculated for discrete samples measured post-cruise. The GOP rates were calculated from triple O2 isotopic (TOI) ratios. These data are derived from the EIMS and TOI data for the NES-LTER Transect cruises in EDI data package knb-lter-nes.6.3.
Net community production (NCP) and gross oxygen production (GOP), based on oxygen-argon ratios and triple oxygen isotopes, from seasonal NES-LTER Transect cruises in 2021
This data package provides net community production (NCP) and gross oxygen production (GOP, a measure of gross primary production) for the winter and summer Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises in 2021. Two tables are provided: a high-frequency table with NCP rates calculated from measurements of O2/Ar made continuously by an at-sea equilibrator inlet mass spectrometer (EIMS), and a low-frequency table with both NCP and GOP rates calculated for discrete samples measured post-cruise. The GOP rates were calculated from triple O2 isotopic (TOI) ratios. These data are derived from the EIMS and TOI data for the NES-LTER Transect cruises in EDI data package knb-lter-nes.6.3.
Net community production (NCP) and gross oxygen production (GOP), based on oxygen-argon ratios and triple oxygen isotopes, from seasonal NES-LTER Transect cruises in 2022
This data package provides net community production (NCP) and gross oxygen production (GOP, a measure of gross primary production) for the winter and summer Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises in 2022. Two tables are provided: a high-frequency table with NCP rates calculated from measurements of O2/Ar made continuously by an at-sea equilibrator inlet mass spectrometer (EIMS), and a low-frequency table with both NCP and GOP rates calculated for discrete samples measured post-cruise. The GOP rates were calculated from triple O2 isotopic (TOI) ratios. These data are derived from the EIMS and TOI data for the NES-LTER Transect cruises in EDI data package knb-lter-nes.6.3.
Chlorophyll and phaeopigments from water column samples, collected at selected depths at Palmer Station Antarctica, during the Palmer LTER field seasons, 1991-2025.
Phytoplankton chlorophyll sampling was led by Smith from the 1991-1992 season through the 2001-2002 season, and then by Vernet from the 2002-2003 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Phaeopigments are non-photosynthetic pigments that are degradation products of phytoplankton chlorophylls which form during and after phytoplankton blooms. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Beginning in the 2020-2021 season, Station B is no longer sampled. Chlorophyll and phaeopigment concentrations are determined by filtration, extraction, and fluorometric detection of samples. The primary source of error for phaeopigment measurement is Chlorophyll b. If high amounts of Chlorophyll b are present in the sample, phaeopigments may be overestimated. There was no field season in 2021-2022.
Water column primary production from inorganic carbon uptake for 24h at simulated in situ light levels in deck incubators, collected at Palmer Station Antarctica during Palmer LTER field seasons, 1994-2025.
Primary Production experiments were led by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the current lead, beginning in the 2009-2010 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Primary production is the uptake of inorganic carbon and assimilation of it into organic matter by phytoplankton. Primary production rates, expressed as mgC per m3 per day were measured by the uptake of radioactive (14C) sodium bicarbonate. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Beginning in the 2020-2021 season, Station B is no longer sampled. Water is put into borosilicate bottles, inoculated with 1 uCi of NaH14CO3 per bottle, and incubated in an outdoor deck incubator. The incubator is plumbed to the Palmer Station sea water system to maintain ambient seawater temperature and bottles are screened to in situ light levels. The uptake of 14C-bicarbonate by the phytoplankton was measured in a scintillation counter after a 24-hour incubation period. Primary production experiments were not conducted during the 2020-2021 nor 2023-2024 field seasons. There was no field season in 2021-2022.
Photosynthetic pigments of water column samples analyzed using High Performance Liquid Chromatography (HPLC), sampled during the Palmer LTER field seasons at Palmer Station, Antarctica, 1991 – 2023.
Phytoplankton pigment sampling was led by Prezelin from the 1991-1992 season through the 1993-1994 season, and then by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll’s (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods. Data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, Palmer 2016-2017, Palmer 2019-2020, Palmer 2020-2021, and Palmer 2023-2024 seasons because those samples have not been analyzed yet.
Mesozooplankton taxonomic density collected using a 1-m diameter ring net with 200-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The numerical density of common mesozooplankton taxa was determined at Palmer LTER Stations B and E. Samples were collected with a 1-m diameter, 200-μm mesh ring net towed obliquely from the surface to a target depth of 50 m and back. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. The preserved samples were size-fractionated with nested sieves into five size classes (0.2−0.5, 0.5−1, 1−2, 2−5, and >5 mm) prior to microscopic enumeration. Data are provided for the following taxa: copepods Oithona spp., Calanoides acutus (>1 mm only), Calanus propinquus (>1 mm only), Rhincalanus gigas (>1 mm only), and small calanoids (0.2−1 mm), chaetognaths, asteroid larvae, nemertean larvae, and foraminifera (not quantified in all years). Individual size fractions were split and subsampled such that at least 100 individuals of the most abundant taxon were present. Density varies across taxa, seasonally, among years, and between sampling stations. Units of density are individuals per cubic meter.
Macrozooplankton taxonomic density collected using a 1 x 1 m square net with 700-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The numerical density of common macrozooplankton taxa was determined at Palmer LTER Stations B and E. Samples were collected with a 1 x 1 m square, 700-μm mesh Metro net towed obliquely from the surface to a target depth of 50 m and back. Duplicate tows typically were conducted at each sampling site. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. The catch was sorted and counted live. Data are provided for the following taxa, which dominated biomass: the euphausiids Euphausia superba and Thysanoessa macrura, the thecosome pteropod Limacina rangii, gymnosome pteropods, the salp Salpa thompsoni, amphipods, and larval fishes. Density varies across taxa, seasonally, among years, and between sampling stations. Units of density are individuals per cubic meter.
Size-fractionated zooplankton dry weight collected using a 1-m diameter ring net with 200-μm mesh at Palmer Station, Antarctica during Palmer LTER field seasons, 2017-2020
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. The density of zooplankton dry weight for five size fractions was determined at Palmer LTER Stations B and E. Samples were collected with a 1-m diameter, 200-μm mesh ring net towed obliquely from the surface to a target depth of 50 m and back. Tows were conducted during daytime, and sampling frequency was nominally twice weekly while personnel were at Palmer Station between the months of November and March. One-half of the catch was size-fractionated with nested sieves into the following five size classes for biomass analysis: 0.2−0.5, 0.5−1, 1−2, 2−5, and >5 mm. Individual size fractions were concentrated on preweighed 200 μm mesh filters and frozen at −20°C until analysis. Samples were thawed, weighed to determine wet biomass, dried at 60°C for at least 24 h, and weighed again to determine dry biomass. Zooplankton density varies across size groups, seasonally, among years, and between sampling stations. Units of biomass density are milligrams dry weight per cubic meter.
SEV-LTER Mean - Variance Experiment Seasonal Biomass Data at the Sevilleta National Wildlife Refuge, New Mexico
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean - Variance Climate Experiment (MVE) adds three novel elements to prior designs that have manipulated interannual variance in climate in the field (Gherardi & Sala, 2013) by (i) determining interactive effects of mean and variance with a factorial design that crosses reduced mean with increased variance, (ii) studying multiple dryland biomes to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. This data package includes species-level plant cover and biomass data from the Mean - Variance Experiment at five sites comprising the major ecosystems of the Sevilleta National Wildlife Refuge: Chihuahuan Desert shrubland, Chihuahuan Desert grassland, Great Plains grassland, Juniper savanna, and pinon-juniper woodland. Species cover and volume in one-meter-squared quadrats are assessed twice-yearly in spring and fall, and regressions correlating biomass and volume constructed using seasonal harvest weights from SEV157, "Net Primary Productivity (NPP) Weight Data."
Modal shift in North Atlantic seasonality during the last deglaciation
<p>Raw data for Brummer et al. (2020) published in Climate of the Past (https://doi.org/10.5194/cp-16-1-2020)</p>
The datasets used in the manuscript named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"
<p>The hindcast and real-time prediction output of FGOALS-f2 V1.0 used in the study named "Dynamical Seasonal Prediction of Tropical Cyclone Activity Using a Global Ensemble Prediction System FGOALS-f2 V1.0"</p>
North Atlantic jet stream clusters: daily and seasonal occurence
<p>This dataset contains the time series used in Madonna et al 2020 (Reconstructing winter climate anomalies in the Euro-Atlantic sector using circulation patterns, DOI: 10.5194/wcd-2021-6)</p> <p><br> Filenames:</p> <p>1) seasonal_timeseries.txt</p> <p>Time series of the occurrence (in % = days/season*100) of time during winter of each jet cluster, blocking and the NAO.<br> Winters are defined as December, January and February (DJF). The season name is given by the last month (i.e. 1980 is December 1979, January 1980 and February 1980). 29 February is removed from the data so that each winter season has 90 days.</p> <p>Jet clusters are calculated following Madonna et al 2017. The five clusters are named as in Madonna et al 2017: Northern (N), Central (C), Mixed (M), Southern (S) and Tilted (T).<br> Blocking are calculated following Scherrer et al. 2006 and averaged over Greenland (GB), offshore of the Iberian Peninsula also called Iberian wave breaking (IWB) and over Scandinavia (SBL). The exact definition of the regions can be found in Madonna et al 2020.</p> <p>The NAO index was downloaded from ftp://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.nao.index.b500101.current.ascii. Positive (NAO+) and negative (NAO-) days are defined as those that exceed 0.5 DJF standard deviation, corresponding to values greater than 0.613 and lower than -0.177, respectively.</p> <p>Example: during winter 1980, 7.78% of the days were in the North jet cluster. This is equivalent to 7 days -> 7.78 * 90 (days per season) /100</p> <p><br> 2) daily_inverse_distance_from_centroid.txt contains information about the similarity of the 2D zonal wind field to the cluster centroids which is used to determine the jet state.</p> <p>The file has 12 columns, labelled as follow:<br> date, lat, speed, N4, C4, M4, S4, N5, C5, M5, S5, T5</p> <p>The first column (date) shows the day in YYYYMMDD format, the second (lat) is the latitude (in °N) of the maximum zonal wind in the 60°W-0°W sector (i.e. the jet latitude index, see Woollings et al. 2010 or Madonna et al. 2017 for more details), and the third (speed) is the zonal averaged (60°W-0°) zonal wind speed (in m/s) at the latitude given by column 2.</p> <p>Columns 4-7 give the inverse distance from each cluster centroids using four (4) clusters: Northern (N4), Central (C4), Mixed (M4), Southern (S4) and is normalized from 0 to 1. Values close to 1 means that the clusters are similar to its centroid. The distances sum up to 1.</p> <p>Columns 8-12 show similar to 4-7 the inverse distance from the centroids using five (5) clusters: Northern (N5), Central (C5), Mixed (M5), Southern (S5) and Tilted (T5). Distances are also normalized and sum up to 1.</p> <p>In the study of Madonna et al 2020, a day has a defined cluster X (X=N, C, M, S, T), if the inverse distance from the cluster centroid X exceeds 0.5 and it clearly dominates over the other clusters.</p> <p><br> Example: 1 January 1979, the zonal mean zonal wind is maximum at 47°N and has a value of 15.61 m/s.<br> Considering 4 clusters, the jet resembles most the Mixed cluster (M4=0.36), followed by the Southern (S4=0.23), Northern (N4=0.22) and Central (C4=0.18). The sum of the distances (0.36 + 0.23 + 0.22 + 0.18 = 0.99 due to decimal approximation) is equal to 1. Using 4 clusters, this day would be assigned to cluster M4. The day is, however, not clearly identified as a Mixed jet, as the inverse distance (M4=0.36) is smaller than 0.5. The threshold of 0.5 is set to identify days where a centroid clearly leads over the others.<br> If we consider 5 clusters, the jet on 1 Jan 1979 resembles the tilted jet (T5 = 0.73) and has very little in common with the other centroids (values of 0.05-0.08). Thus, considering 5 clusters, this day is classified as a tilted jet. It is also clearly defined, as 0.73 > 0.5.</p> <p><br> References:</p> <p>Madonna, E., Li, C., Grams, C.M. and Woollings, T. (2017), The link between eddy‐driven jet variability and weather regimes in the North Atlantic‐European sector. Q.J.R. Meteorol. Soc, 143: 2960-2972. https://doi.org/10.1002/qj.3155</p> <p>Scherrer, S. C., Croci‐Maspoli, M., Schwierz, C., and Appenzeller, C. (2006). Two‐dimensional indices of atmospheric blocking and their statistical relationship with winter climate patterns in the Euro‐Atlantic region. International Journal of Climatology, 26(2), 233-249</p> <p>Woollings T, Hannachi A and Hoskins B. (2010). Variability of the North Atlantic eddy‐driven jet stream. Q. J. R. Meteorol. Soc. 136: 856– 868.</p> <p> </p> <p> </p> <p> </p>
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Raw data of "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion"
<p>The uploaded data here is the raw data of the manuscript "Seasonal fluctuations of ichthyoplankton assemblage in the northeastern South China Sea influenced by the Kuroshio intrusion" submitted to the Journal of Geophysical Research-Oceans. The CTD file (.cnv) is the data recorded by a Sea-Bird conductivity, temperature and depth (CTD) in the sampling stations. This data is used to analyze the water masses during the study period. It can be analyzed with free software of the Ocean Data View 4 (http://odv.awi.de/) or the MATLAB R2017 (http://www.mathworks.com/products/matlab/). The sequence data (.fasta) is used to evaluate the species composition. The data can be analyzed with free software of the BOLD Identification tool (http://www.boldsystems.org/), the basic local-alignment search tool (BLAST) (https://www.ncbi.nlm.nih.gov/), the Clustal X 2.1 (http://www.clustal.org/) and the MEGA 7 (http://www.megasoftware.net/). In additon, the .nc files are the data of surface temperature during the sampling periods. The data can be analyzed with the MATLAB R2017 (http://www.mathworks.com/products/matlab/).</p>
CLRD-GLPS: A Long-term Seasonal Dataset of Ruminant Livestock Distribution in China's Grazing Production Systems (2000-2021) Using Stacking-based Interpretable Machine Learning
<p>Advanced computational methods integrating ensemble learning with interpretable machine learning are essential for precision livestock management under increasing environmental constraints and food security pressures. This study develops a novel stacking-based interpretable machine learning (IML) framework that combines multiple algorithms with SHAP analysis techniques to generate the China's Long-term Ruminant Livestock Distribution in Grazing Livestock Production Systems (CLRD-GLPS) dataset. Our computational approach addresses critical challenges in livestock distribution modelling: livestock segmentation and spatial prediction accuracy. The framework integrates Random Forest, XGBoost, CatBoost, LightGBM, and Extra Trees through a two-layer stacking architecture, enhanced with SHAP (Shapley Additive Explanations) analysis for model interpretability. We also implemented interpretable machine learning for livestock production system segmentation to distinguish grazing from total livestock populations. The stacking ensemble demonstrated superior performance over individual algorithms, achieving R² values of 0.954-0.961 for cattle and 0.896-0.901 for sheep and goats, with improvements of up to 8.3% compared to best performance single-model approaches. Multi-scale validation confirmed computational robustness: livestock segmentation achieved R² = 0.80 at county level, while independent city-level validation of CLRD-GLPS datasets yielded R² = 0.76-0.80. SHAP interpretability analysis revealed distinct environmental drivers, with vegetation indices and topography primarily influencing cattle distribution, while snow conditions and elevation dominated sheep and goat patterns. This computational framework advances livestock distribution modelling through enhanced prediction accuracy, model stability, and interpretability, while the CLRD-GLPS dataset provides essential spatial-temporal information for rangeland sustainability assessments and evidence-based livestock management policies. This dataset is supported by the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, grant no. 2019QZKK0906).</p>
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>
Data accompanying the manuscript "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales"
<p>This dataset contains time series of wind energy production aggregated over France and Europe, obtained from a 1000-year climate simulation from the CESM model (version 1.2.2, Hurrel et al. 2013), coupled to a simple energy model to compute grid-point capacity factor from surface wind. Wind power is then computed by multiplying the capacity factor by the installed capacity, taken from 5 e-Highway scenarios (X5, X7, X10, X13 and X16), and integrated over the regions of interest. More details about the climate simulation, wind energy model and installed capacity scenarios can be found in the associated manuscript, "Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales" (Cozian et al. 2023).</p><p>The data is organized into 10 files for France and 10 files for Europe. In each case, the 10 files correspond to 10 batches of 100 years each, with 3-hourly output. Each file contains 5 time series corresponding to the 5 installed capacity scenarios.</p><h4>References</h4><ul><li>Hurrell J W, Holland M M, Gent P R, Ghan S, Kay J E, Kushner P J, Lamarque J F, Large W G, Lawrence D, Lindsay K, Lipscomb W H, Long M C, Mahowald N, Marsh D R, Neale R B, Rasch P, Vavrus S, Vertenstein M, Bader D, Collins W D, Hack J J, Kiehl J and Marshall S (2013). The community earth system model: A framework for collaborative research. Bulletin of the American Meteorological Society, 94, 1339–1360. <a href="https://doi.org/10.1175/BAMS-D-12-00121.1">https://doi.org/10.1175/BAMS-D-12-00121.1</a></li><li>e-Highway 2050 (2015). Europe's future secure and sustainable electricity infrastructure. <a href="https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results">https://docs.entsoe.eu/baltic-conf/bites/www.e-highway2050.eu/results</a></li><li>Cozian B, Herbert C and Bouchet F (2023). Assessing the Probability of Extremely Low Wind Energy Production in Europe at Sub-seasonal to Seasonal Time Scales. <a href="https://doi.org/10.48550/arXiv.2311.13526">https://doi.org/10.48550/arXiv.2311.13526</a></li></ul>
Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems
<p>Supplementary datasets for manuscript titled: Seasonal tissue-specific gene expression in wild crown-of-thorns starfish reveals reproductive and stress-related transcriptional systems</p>
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.