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766 results for “Baseline”
Harmonic Baseline Experiments for Landsat-Based Forest Condition Monitoring in Southern New England 2017
This dataset was developed as part of a study of harmonic baseline model parameterization for forest condition monitoring using Landsat time series. We implemented a previously published harmonic modeling approach for forest condition monitoring in Google Earth Engine and systematically assessed the relative ability of condition change products generated using various model parameterizations for predicting pest abundances and defoliation during the 2016-2018 Lymantria dispar outbreak in southern New England. We ran a series of 32 experiments that considered a variety of parameter choices for establishing multi-year “baseline” models representing relatively stable forest conditions for each Landsat pixel in our study area. We tested a full set of factors including (a) spectral vegetation index used for model fitting, (b) baseline-modeling period, (c) frequencies of harmonic regression terms, and (d) differences in Landsat time series input imagery. We generated average condition score estimates for each of these 32 baseline parameterizations for a May 1 to September 30, 2017 monitoring period, then used Generalized Linear Mixed Models to test the relationships between ground-based observations of defoliation and defoliator abundance (larva and egg masses). This archived dataset includes the full set of experimental raster results, as well as a “reanalysis” product from a previous implementation of our condition monitoring workflow. More information on model parameterization rankings can be found in the associated publication (Pasquarella et al. 2021).
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
Baseline Plant Abundances on Garlic Mustard Experiment Plots at Harvard Forest 2013
This is a five year project that investigates the reassembly of soil fungi and native plants in forests affected by biological invasion. The experiment brings together regional land managers to create comparative eradications of garlic mustard at nine distinct sites along a natural climate and nitrogen deposition gradient in New England. This dataset represents baseline plant abundances from 2013 at nine study plots in Harvard Forest. Species richness, Shannon diversity, and Pielou’s evenness were not different between invaded and non-invaded plots at Harvard Forest.
City of Seattle, Seattle Public Utilities, Marbled Murrelet Baseline Surveys 2005-2007, Cedar River Municipal Watershed, King County, WA
From 2005 to 2007, Seattle Public Utilities (SPU) hired consultants to use ornithological radar and protocol audio-visual (AV) surveys to establish baseline information on the distribution and abundance of Marbled Murrelets (Brachyramphus marmoratus) in the Cedar River Municipal Watershed (CRMW), Washington. Across the three years, radar surveys were conducted on 100 mornings at 18 sites, and AV surveys on 92 mornings at 12 sites. The study recorded murrelet targets via radar in all years, with 65 detections in 2005, 89 in 2006, and 25 in 2007. Mean daily landward radar counts ranged between 0 and 4 per morning, and did not differ significantly across years. Although overall radar counts were low, high day-to-day variability was observed (coefficients of variation from 130% to 173%). Monte Carlo simulations showed that with repeated surveys using the same methods, the monitoring program could detect an annual increase of 2–3% in Marbled Murrelet abundance over approximately 25–50 years. AV surveys confirmed murrelet occupancy at two sites: the Rex River in 2005 and the South Fork Cedar River in 2006. No murrelets were detected by AV in 2007, though radar data suggested low-level murrelet presence at several additional sub-basins. The findings indicate that Marbled Murrelets occur at low densities in the CRMW, likely limited by both high inland distance and relatively sparse old-growth forest structure suitable for nesting. The four radar stations established and resampled during the study provide an opportunity for long-term monitoring, offering sufficient power to detect changes in murrelet activity as ongoing forest restoration efforts in the CRMW progress.
FAO and SAGARPA. (2012). Baseline of the Natural Resources Sustainability Program. Sustainable Land Use Subindex - Calculation Methodology. Mexico City (53 pp.) (FAO y SAGARPA. (2012). Línea de Base del Programa de Sustentabilidad de los Recursos Naturales. Subíndice de Uso Sustentable del Suelo - Metodología de Cálculo (53 pág.). Ciudad de México)
The lack of soil data is a complication that most soil scientists will encounter throughout their career; this critical aspect is exacerbated due to the excessive cost of soil surveying. Consequently, it is essential to develop strategies that guarantee the permanent accessibility of past soil sampling efforts. The main objective of this contribution is to release an entire dataset of soil samples surveyed by the Secretary of Agriculture, Livestock, Rural Development, Fisheries, and Food (SAGARPA) in collaboration with the Food and Agriculture Organization of the United Nations (FAO) in the year 2012, the dataset consists of more that 4000 compound samples surveyed on managed cropland. SAGARPAS's main objective was to generate a new index to assess and monitor the current and future state of soil when sustainable soil practices take place. A complete set of physicochemical properties were determined via laboratory analysis for all record in the dataset, namely: pH, EC, OM, BD, P, Sand, Silt, Clay, Texture, N, K, Ca, Mg, Na, CEC, Sodium Adsorption Ratio (SAR), Exchangeable Sodium Percentage (ESP), including the calculation of the Sustainable Soil Land Use Subindex (SSLUS). We presented a reviewed dataset with the potential to contribute to a large variety of studies, ranging from: agricultural pinpointing of the best conditions for crop production to digital soil mapping and modeling and agronomical studies. We also provide the original report on the developement of the dataset, indicating the names of creators and colaborators, as well as the methods of analysis and interpretation of the results. The new information is appealing for a wide diversity of users interested in soil traits across agricultural systems of Mexico.
MRI data of 40 adult participants in response to a cue induced craving task following food fasting, social isolation and baseline (within-subject design)
Open the record for dataset details and reuse information.
MESSAGEix-GLOBIOM 1.1 R11 no-policy baseline
<p>This dataset contains the parameterization of a no-policy baseline scenario of the global 11-regional <a href="https://docs.messageix.org/projects/global/en/">MESSAGEix-GLOBIOM</a> integrated assessment model. <a href="https://docs.messageix.org/projects/models/en/latest/pkg-data/node.html#region-aggregation-r11">Regions</a>, <a href="https://docs.messageix.org/projects/models/en/latest/pkg-data/year.html">time periods</a>, <a href="https://docs.messageix.org/projects/models/en/latest/pkg-data/codelists.html#commodities-commodity-yaml">commodities</a>, <a href="https://docs.messageix.org/projects/models/en/latest/pkg-data/codelists.html#commodities-commodity-yaml">technologies</a> and <a href="https://docs.messageix.org/projects/models/en/latest/pkg-data/relation.html">relations</a> included in this model are described in a separate <a href="https://docs.messageix.org/projects/models/">repository</a>. The dataset relies on the <a href="https://docs.messageix.org/en/stable/">MESSAGEix modeling framework</a> (<a href="https://doi.org/10.1016/j.envsoft.2018.11.012">Huppmann et al. 2019</a>) and can be imported into MESSAGEix via the <a href="https://docs.messageix.org/en/stable/api.html?highlight=read_xls#message_ix.Scenario.read_excel">read_excel()</a> functionality, for which a <a href="https://github.com/iiasa/message_ix/blob/main/tutorial/westeros/westeros_baseline_using_xlsx_import_part1.ipynb">tutorial</a> is available, or via <a href="https://docs.messageix.org/projects/models/en/latest/api/model-snapshot.html#message_ix_models.model.snapshot.load">snapshot.load()</a> as described <a href="https://docs.messageix.org/projects/models/en/latest/api/model-snapshot.html">here</a>. After the import the scenario can be solved and modified to create new scenarios. Note that the published scenario as included in the <a href="../record/5553976">ENGAGE global scenarios dataset</a> has been run with a release candidate of <a href="https://docs.messageix.org/en/stable/whatsnew.html#v3-4-0-2022-01-27">version 3.4.0</a> of MESSAGEix.</p>
WCRP Baseline Variables - MIP Prioritisation raw data
<p>Supplementary material for the publication: Juckes et al. (2024) Baseline Climate Variables for Earth System Modelling, accepted in GMD. Preprint: https://doi.org/10.5194/egusphere-2024-2363. </p> <p>This data summarises the WCRP Baseline Variables list, and includes the raw data from throughout the prioritisation process.</p>
Global Mangrove Watch 2010 Baseline (v2.5)
<p>This dataset is an updated version of the Global Mangrove Watch (Bunting et al. 2018) 2010 global mangrove baseline. A number of regions have been remapped to improve quality and map regions missed in the older version 2.0 product published in 2018. </p>
H2020 Platone German Demonstrator - Baseline Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are computed values for the active power exchange at the medium (MV)/low voltage grid connecting feeder (active power). The data are provided as 15-minutes mean values in kilowatt. The computed indicate the power exchange that would have been measured, in case no use case would have been applied in the field (control of batteries).</p> <p><strong>Data Description:</strong></p> <ul> <li>p_tei_c_mean = arithmetic mean of p_tei computed in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_c_min = the minimum value (1-minute mean) computed within the period of p_tei_mean (15-minutes)</li> <li>p_tei_c_max = the maximum value (1-minute mean) computed within the period of p_tei_mean period (15-minutes)</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The field test setup of the demonstrator consists of a MV/LV substation, 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh capacity. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
Baseline soil chemistry data measurements from the GCE-LTER Seawater Addition Long-Term Experiment (SALTEx)
SALTEx (Seawater Addition Long-Term Experiment) is a field experiment designed to simulate saltwater intrusion in a tidal freshwater wetland to predict how chronic (Press) and acute (Pulse) salinization will affect this and other tidal freshwater ecosystems. The SALTEx experiment was initiated in 2012 and consists of 31 field plots, each 2.5 m on a side. There are three treatments (Press, Pulse, and Fresh) and two types of controls (with and without sides), each consisting of six replicates. The Press treatment plots receive regular (4 times each week) additions of a mixture of seawater and fresh river water. Pulse plots receive the same mixture of seawater and river water during September and October, which is historically a time of low flow in the river when natural saltwater intrusion occurs. The Fresh treatment plots receive regular additions of fresh river water. Treatment water is added during low tide to facilitate its infiltration into the soil, and all plots are inundated by astronomical tides at high tide. Soils were destructively sampled before the beginning of the experiment (March 2014) and after approximately 3 years of treatments (December 2016) and analyzed for bulk density, percent carbon, percent nitrogen, total phosphorus, available phosphorus, available nitrate, and available ammonium.
Baseline survey for beef cattle producers in the Southwest and Southern Plains
This data package includes survey questions from beef cattle producers collectively operating in at least 31 counties in at least 7 states (California, Illinois, Missouri, Nebraska, New Mexico, Oklahoma, Texas) - "at least" because there were some respondents who chose not to provide the location of their operation. Responses were collected between January 22, 2020 and May 31, 2021. Most of the surveys were administered in person at the 2020 Southwest Beef Symposium in Amarillo, TX. The survey was also placed online and an additional few responses were collected through the online survey. These data represent a sample of convenience as no formal sampling scheme was employed in soliciting responses. Survey responses are summarized in the publication, Snapshot of Rancher Perspectives on Creative Cattle Management Options (Elias et. al, 2020). The purpose of gathering these data was to learn more about the characteristics of beef cattle producers in the region and to gauge producer interest in precision livestock ranching technologies and heritage cattle – both strategies being researched by the Sustainable Southwest Beef Project to support sustainability of ranching operations in the Southwest and Southern Plains regions of the US.
Baseline data for SDM of the SIM4NEXUS case of the Netherlands
<p>This dataset consists of the baseline data for the SDM of the SIM4NEXUS case of the Netherlands. Most data is based on year data that has been equally distributed over 12 months per year. For optional extension to monthly data, the data display already monthly data starting at December 2009 until December 2050 i.e. 493 points in time. The data include time series for socio-economic indicators, land use, food production, energy use, climate and water. The socio-economic system includes data on population and gross domestic product (GDP) per capita. The land system includes data on four main land uses, namely built-up areas, agriculture (with areas for food, energy crops and fodder crops production) , nature areas (non-forest, forest not for biomass production, forest for biomass production) , and areas for renewable energy production like wind mills and solar power fields. The food system includes plant-based (food crops like cereals and vegetables and fruit) and animal-based food production (based on the herds of cattle, pigs and poultry) in terms of protein. In addition, the animal- and plant-based protein requirements of Dutch consumers are also estimated. The energy system has an energy production and an energy demand part. Energy demand is determined for the domestic sector (i.e. households) based upon population and households’ demands for renewable and non-renewable energy. For the other economic sectors (agriculture, manufacturing industry, transportation and services sector) the demands for renewable and non-renewable energy are determined by GDP per sector and the energy intensity of the sectors. Energy supply is divided into non-renewable energy production and renewable energy production. Non-renewable energy sources include energy from coal, natural gas, oil and nuclear. The renewable energy consist of energy from wind (onshore and offshore), solar (on buildings and solar power fields), biomass and other sources (innovations like hydrogen or geothermic power). The energy of biomass there are 7 sources of biomass: energy crops, crop residues, manure, organic household waste, organic waste from public areas, waste water and timber residues. Timber residues are largely imported for large-scale use of co-firing in coal power plants and bio-based activities in the manufacturing industries. The water system has two parts: water quality which are the agricultural emissions of nitrogen and phosphorus to water, and water quantity i.e. agricultural water demand for irrigation and livestock drinking water. Finally, the climate system is divided into non-agricultural GHG emissions, and agricultural emissions. The non-agricultural GHG emissions are based on the GHG emissions from non-renewable energy production and non-energy related GHG emissions per economic sector except for agriculture. The agricultural GHG emissions relate to GHG emissions from livestock production, crop production and wetlands.</p>
Travellers baseline, pre and post-questionnaires of the 1st iteration phase
<p>The dataset contains the travellers’ baseline-, pre- and post-questionnaires of the first evaluation phase of the MyCorridor project. The column pre-evaluation indicates whether the respondent took part in the baseline- or pre-questionnaires (Column B-FF). Whereas all respondents were asked to participate in the same post-questionnaire survey (Column FG-MI). The questions in the pre-questionnaires are related to the background of the respondents, mobility wants & needs, online consumer experience, MaaS awareness, MyCorridor platform pre-acceptance, computer literacy, online consumer attitude and behaviour, online shopping needs and wishes and MyCorridor platform pre-acceptance. The questions in the post-questionnaires are related to the evaluation of the app, the interaction experience, the value, usability and acceptance.</p>
PUDL Raw NREL Annual Technology Baseline (ATB) for Electricity and Transportation
<p>The NREL Annual Technology Baseline (ATB) for Electricity publishes annual projections of operational and capital expenditures (by technology and vintage), as well as operating characteristics (by technology). Archived from <a href="https://atb.nrel.gov/">https://atb.nrel.gov/</a></p> <p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources: </p><ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li> <li><a href="https://zenodo.org/communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li> </ul> <p></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>
[DCASE2022 Task 3] Synthetic SELD mixtures for baseline training
<p><strong>DESCRIPTION:</strong><br> <br> This audio dataset serves serves as supplementary material for the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">DCASE2022 Challenge Task 3: Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes</a>. The dataset consists of synthetic spatial audio mixtures of sound events spatialized for two different spatial formats using real measured room impulse responses (RIRs) measured in various spaces of Tampere University (TAU). The mixtures are generated using the same process as the one used to generate the recordings of the <a href="https://zenodo.org/record/5476980">TAU-NIGENS Spatial Sound Scenes 2021</a> dataset for the <a href="https://dcase.community/challenge2021/task-sound-event-localization-and-detection-results">DCASE2021 Challenge Task 3</a>. </p> <p>The SELD task setup in DCASE2022 is based on spatial recordings of real scenes, captured in the <a href="https://zenodo.org/record/6387880">STARS22</a> dataset. Since the task setup allows use of external data, these synthetic mixtures serve as additional training material for the <a href="https://github.com/sharathadavanne/seld-dcase2022">baseline model</a>, and they are shared for reasons of reproducibility. For more details on the task setup, please refer to the <a href="http://Sound Event Localization and Detection Evaluated in Real Spatial Sound Scenes">task description</a>.</p> <p>Note that the generator code and the collection of room responses used to spatialize sound samples will be also be made available soon. For more details on the recording of RIRs, spatialization, and generation, see:</p> <ul> <li>Archontis Politis, Sharath Adavanne, Daniel Krause, Antoine Deleforge, Prerak Srivastava, Tuomas Virtanen (2021). A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection. In <em>Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2021)</em>, Barcelona, Spain.</li> </ul> <p>available <a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Politis_43.pdf">here</a>.</p> <p><strong>SPECIFICATIONS:</strong></p> <ul> <li><strong>13 target sound classes</strong> (see task description for details)</li> <li>The sound event samples are sources from the <strong><a href="https://zenodo.org/record/4060432">FSD50K</a></strong> dataset, based on affinity of the labels in that dataset to the target classes. The selection on distinguishing which labels in FSD50K corresponded to the target ones, then selecting samples that were tagged with only those labels, and additionally that they had annotator rating of Present and Predominant (see FSD50K for more details). The list of the selected files is included here.</li> <li><strong>1200</strong> 1-minute long spatial recordings</li> <li>Sampling rate of<strong> 24kHz</strong></li> <li>Two 4-channel recording formats, first-order Ambisonics (<strong>FOA</strong>) and tetrahedral microphone array (<strong>MIC</strong>)</li> <li>Spatial events spatialized in <strong>9 unique rooms</strong>, using measured RIRs for the two formats</li> <li>Maximum <strong>polyphony of 2</strong> (with possible same-class events overlapping)</li> <li>Even though the whole set is used for training of the baseline without distinction between the mixtures, we have included a <strong>separation into a training and testing split</strong>, in case on one needs to test the performance purely on those synthetic conditions (for example for comparisons with training on mixed synthetic-real data, fine-tuning on real data, or training on real data only).</li> <li>The training split is indicated as <strong>fold1</strong> in the dataset, contains 900 recordings spatialized on 6 rooms (150 recordings/room) and it is based on samples from the development set of FSD50K.</li> <li>The testing split is indicated as <strong>fold2</strong> in the dataset, contains 300 recordings spatialized on 3 rooms (100 recordings/room) and it is based on samples from the evaluation set of FSD50K.</li> <li>Common metadata files for both formats are provided. For the file naming and the metadata format, refer to the task setup.</li> </ul> <p><strong>FSD50K SELECTION:</strong></p> <p>The list of selected sound event recordings is included along the recordings and metadata, as <strong>FSD50K_selected.txt</strong>. Each line in the text file has the following structure:</p> <pre><code>[target_label]/[train/test]/[FSD50K_label]/filename.wav</code></pre> <p>with an example:</p> <pre><code>domesticSounds/train/Boiling/16584.wav</code></pre> <p>meaning that the file 16584.wav from FSD50K, with the <em>Boiling</em> label of FSD50K, is included in the samples for the training split of those synthetic recordings, and it is mapped to the target class of <em>domestic sounds. </em>Note that there can be multiple FSD50K labels mapped the same target class. Also note that if these are downloaded from FSD50K, and a folder structure is created that replicates the structure in the list, the resulting folder can be used out-of-the-box with the scene generator to generate new mixtures with the same or different parameters.</p> <p>Note that no sounds form FSD50K have been selected for the <em>Music</em> target class. Background and pop music tracks from the public domain have been cropped and used instead.</p> <p><strong>DOWNLOAD INSTRUCTIONS:</strong></p> <p>Download the zip files and use your preferred compression tool to unzip these split zip files. To extract a split zip archive (named as zip, z01, z02, ...), you could use, for example, the following syntax in Linux or OSX terminal:</p> <ol> <li>Combine the split archive to a single archive: <pre>zip -s 0 split.zip --out single.zip</pre> </li> <li>Extract the single archive using unzip: <pre>unzip single.zip</pre> </li> </ol>
Global Surface Temperature Changes Datasets Converted to 1850-1900 Baseline
<p>Global warming datasets converted to the uniform baseline. NASA, NOAA and Berkeley Earth datasets of global surface temperature changes in the period 1850-2021 for land+ocean, 1750-2021 for land only and 1880-2021 for ocean only, converted to the 1850-1900 baseline.</p>
Baseline map of 137Cs inventories in reference soil sites at the continental scales of South America
<p>This dataset contains the baseline map of <sup>137</sup>Cs inventories in reference soil sites (Bq m<sup>-2</sup>, decay-corrected to 2020) estimated by Partial Least Square Regression (PLSR) with a spatial resolution of 2 km at the continental scale of South America, as well as the prediction uncertainties of the baseline map (coefficient of variation, %).<br> Details information regarding this dataset can be found in the original publication:<br> Mapping the spatial distribution of global <sup>137</sup>Cs fallout in soils of South America as a baseline for Earth Science studies, Earth-Science Reviews, Volume 214, 2021, 103542, ISSN 0012-8252, https://doi.org/10.1016/j.earscirev.2021.103542.</p>
Risk and symptoms of COVID-19 in health professionals according to baseline immune status and booster vaccination during the Delta and Omicron waves in Switzerland – a multicentre cohort study
<p>For details, see publication</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.