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6,498 results for “physics”
Physical Health of Adults during Covid-19
<h1>Background</h1> <p>This dataset is one of the studies of the <a href="https://www.corona-health.net/en/">Corona Health project</a>. It addresses how the physical health and habits of adults during the global pandemic changed over time. It consists of two questionnaires, a baseline questionnaire and a bi-weekly follow up questionnaire to track the behaviour in the last 14 days. The whole datasets contains more than <strong>1800 users</strong> (98 % of them German) and a total of <strong>7000 questionnaires</strong>. For some users, it also has GPS and app usage data.</p> <h1>Files</h1> <ul> <li>rki_heart_baseline.csv -> The baseline questionnaire, containing demographic data as well</li> <li>rki_heart_followup.csv -> The follow-up questionnaire</li> <li>answersheets.csv -> The unprocessed answersheets from both, baseline and follow-up questionnaires in a raw format.</li> <li>codebook.xlsx -> The Codebook that describes the three files in detail</li> </ul> <h1>More information</h1> <ul> <li> <p><strong>Key Facts</strong></p> <ul> <li>No. of questionnaires: 1805 Baseline + 5895 Follow-up</li> <li>n Tracking Consent GPS (ratio): 1366 (75%)</li> <li>n Tracking Consent App Usage (ratio): 101 (5.6%)</li> </ul> </li> <li> <p><strong>Sociodemographics</strong></p> <ul> <li>Age, mean (SD): 41.7 (15.1)</li> <li>Gender Ratio: <ul> <li>Male: 36%</li> <li>Female: 64%</li> <li>Diverse: 0%</li> </ul> </li> <li>Body Mass Index, mean (SD): 26.7 (6.23)</li> <li>Users located in Germany (ratio): 98.7%</li> </ul> </li> <li> <p><strong>Lifestyle habits at baseline</strong></p> <ul> <li>Daily Smokers (ratio): 292 (16.2%)</li> <li>Daily fruit consumers before lockdown (ratio): 533 (29.5%)</li> <li>Daily fruit consumers after lockdown (ratio): 538 (29.8%)</li> <li>Daily vegetable consumers before lockdown (ratio): 562 (31.1%)</li> <li>Daily vegetable consumers after lockdown (ratio): 559 (31.0%)</li> </ul> </li> <li> <p><strong>Cardiovascular Health at baseline</strong></p> <ul> <li>History of hypertension (ratio): 454 (25.2%)</li> <li>History of diabetes mellitus (ratio): 113 (6.3%)</li> <li>History of hyperlipidemia (ratio): 461 (25.5%)</li> </ul> </li> </ul> <p>For a more detailed description of the dataset, please go on <a href="https://github.com/joa24jm/CH-Heart" target="_blank" rel="noopener">GitHub/joa24jm/ch-heart.</a> There, you can also find a link to our publication.</p>
Long-term trends in pesticide residues and physical chemical parameters of superficial water samples with accompanying macro-benthic invertebrate community surveys from the Palo Verde National Park, Costa Rica: 1993-1994; 2001; 2004-2005; 2009-2011
During the years 1993-1994, 2001, 2003-2005 and 2009-2011, the Central American Institute for Studies on Toxic Substances (IRET-UNA) executed independent research projects which quantified the presence of pesticide residues on superficial water samples from the Palo Verde National Park (PVNP) and surrounding areas. The PVNP (5460 sq km) is a RAMSAR wetland of international importance, which has been subjected to pesticide pressure from agricultural fields (mainly rice and sugarcane) since the 1960s and 1970s. In 1993, the PVNP wetlands were placed on the RAMSAR Montreux Record, indicating that it was considered an “impaired ecosystem” due to ecotoxicology concerns. Water is the key component of all issues regarding the biodiversity, management, restoration, and economic development of this region. Therefore, water quality is a critical component of many social ecological discussions and research efforts. This data package contains uniform pesticide, biological and water quality data from all PVNP wetland projects (1993- 2011) in order to present long-term trends in the environmental water quality and accompanying biological patterns for this conservation area. Study sites were spatially determined to compare clean upstream waters with a gradient of pesticide-affected waters. Superficial water samples were collected at various sites for chemical (pesticide) analysis and water quality parameters were recorded in situ for environmental monitoring. Corresponding biological sampling was completed to survey benthic macroinvertebrate communities and compare with local eco-toxicological profiles. This data package contains information from four separate projects.
Biological and Physical Monitoring Data of Restored Oyster Reef in Savannah River, Savannah, GA from May 2023 - February 2025
For the purposes of this study, we constructed two oyster reefs in Savannah, GA, USA using standard spat-on-shell restoration methodology. Reefs were constructed 1-2 meters from the marsh edge to reduce wave energy as it approached the shoreline, similar to a breakwater. We then conducted monitoring on the biological function of the reef, including live juvenile oyster coverage, size, and abundance for approximately 18 months. We also quantified the energy flux of waves offshore and onshore of the reef using water pressure measurements to determine the capability of these reefs at reducing wave energy. The oyster reefs in this study decreased wave energy by up to 40% compared to paired, non-reef control sites. Constructed oyster reefs also experienced healthy oyster population growth throughout the study, with live juvenile coverage of 17-40% almost 18 months post-deployment. This study took place in an erosion-prone area due to recreational and commercial boating traffic at the nearby Port of Savannah. Our results indicate that using restored oyster reefs as living shorelines is a technique with high potential for preventing shoreline loss in coastal areas vulnerable to anthropogenically-caused erosion. Restored Reef Site 1: 32.067957°, -80.985005° Control Site 1: 32.0675194°, -80.986369° Restored Reef Site 2: 32.062663°, -80.965147° Control Site 2: 32.063261°, -80.965889°
Consumer Stocks: Fish, Vegetation, and other Non-physical Data from Everglades National Park (FCE LTER), South Florida, USA from February 2000 to April 2005
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the numeric count data of fish, plants, and other fauna.
Consumer Stocks: Physical Data from Everglades National Park (FCE), South Florida from February 1996 to April 2008
We hypothesize that standing crops of consumers reflect patterns of allochthonous nutrient transport along the estuarine interface at the Florida Coastal Everglades (FCE) LTER. Our goal is to investigate how variation in hydrology, water quality, and disturbance influence secondary production. This data set represents the physical data of the sampled plots.
Physical and microbial processing of dissolved organic nitrogen (DON) (Salinity Experiment) along an oligotrophic marsh/mangrove/estuary ecotone (Taylor Slough and Florida Bay) for August 2003 in Everglades National Park (FCE), South Florida, USA
A better understanding of the biogeochemical cycling of nutrients entering Florida Bay is a key issue regarding the restoration of the Everglades. In addition to precipitation, the other major source of freshwater to Florida Bay is from Taylor Slough and the C-111 Basin in the northeast section of the Bay. While it is known that these areas deliver significant amounts of N to the Bay, a significant portion of this is in the form of dissolved organic N (DON). The sources, environmental fate and bioavailability to microorganisms of this DON are however, not known. Should this DON be readily available, any increased load as a function of restoration changes might have an impact on internal phytoplankton bloom dynamics. No significant flocculation or precipitation of DOM occurred with increase in salinity, meaning that terrestrial DOM does not get trapped in the sediments but stays in the water column where it subjected to photolysis and advective transport. Sunlight has a significant effect on the chemical characteristics of DOM. While the DOC levels did not change significantly during photo-exposure, the optical characteristics of the DOM were modified. The environmental implications of this are conflicting: photo-induced polymerization may stabilize the DOM by reducing its bioavailability while photolysis may make the DOM more labile. Overall, DON bioavailability was relatively low in this region. Even though the amount of DON loaded to the bay may be significant, the fraction of DON available for microbial cycling is much smaller. The amount of N supplied by recycling may be a significant portion of the total DIN pool. All this must be considered in context with the proposed CERP modifications to flows. As of the latest initial Comprehensive Everglades Restoration Project (CERP) update, the flows to Taylor Slough and C-111/Panhandle Basis are not predicted to change very much from base conditions. Therefore we do not expect any great increases in TN loading in this
Physical and microbial processing of dissolved organic nitrogen (DON) (Photodegradation Experiment) along an oligotrophic marsh/mangrove/estuary ecotone (Taylor Slough and Florida Bay) for August 2003 in Everglades National Park (FCE), South Florida, USA
A better understanding of the biogeochemical cycling of nutrients entering Florida Bay is a key issue regarding the restoration of the Everglades. In addition to precipitation, the other major source of freshwater to Florida Bay is from Taylor Slough and the C-111 Basin in the northeast section of the Bay. While it is known that these areas deliver significant amounts of N to the Bay, a significant portion of this is in the form of dissolved organic N (DON). The sources, environmental fate and bioavailability to microorganisms of this DON are however, not known. Should this DON be readily available, any increased load as a function of restoration changes might have an impact on internal phytoplankton bloom dynamics. No significant flocculation or precipitation of DOM occurred with increase in salinity, meaning that terrestrial DOM does not get trapped in the sediments but stays in the water column where it subjected to photolysis and advective transport. Sunlight has a significant effect on the chemical characteristics of DOM. While the DOC levels did not change significantly during photo-exposure, the optical characteristics of the DOM were modified. The environmental implications of this are conflicting: photo-induced polymerization may stabilize the DOM by reducing its bioavailability while photolysis may make the DOM more labile. Overall, DON bioavailability was relatively low in this region. Even though the amount of DON loaded to the bay may be significant, the fraction of DON available for microbial cycling is much smaller. The amount of N supplied by recycling may be a significant portion of the total DIN pool. All this must be considered in context with the proposed CERP modifications to flows. As of the latest initial Comprehensive Everglades Restoration Project (CERP) update, the flows to Taylor Slough and C-111/Panhandle Basis are not predicted to change very much from base conditions. Therefore we do not expect any great increases in TN loading in this
Florida Bay Physical Data, Everglades National Park (FCE), South Florida from January 2001 to February 2002
Florida Bay physical data that includes surface temperature and salinity at Duck Key, Bob Allen Keys, and Sprigger Bank, Florida Bay in Everglades National Park, South Florida.
Physical Characteristics and Stratigraphy of Deep Soil Sediments from Shark River Slough, Everglades National Park (FCE) from 2005 and 2006
These data represent the results of piston-coring deep (around 1m) soil cores from Shark Slough sites, including FCE LTER site SRS3 and FCE related site NE-SRS1 from November 18, 2005 to February 26, 2006. Soils from 1-cm depth increments were analyzed for bulk density and stratigraphy. These analyses contribute to a paleoecological study to quantify past changes in vegetation and soil accumulation in relation to past climate variation, fire occurrences and water management.
Physical and Chemical Characteristics of Soil Sediments from the Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, August 2004 - ongoing
These data represent the results of annual soil sampling and analysis from all 17 FCE LTER transect locations from Year 2004 thru ongoing. Surface soils from 0-10 cm have been homogenized and analyzed from Sawgrass and mangrove sites and Florida Bay sites. Soils and sediments were analyzed for a suite of physical/chemical variables, including bulk density, organic matter content, extractable iron, AVS and CRS sulfur, and various forms of extractable phosphorus. These analyses are completed to document the differences in soil structure among transect sites, and to provide a baseline dataset against which long-term changes in the physical/chemical properties of the soils can be detected.
Florida Bay Physical Data, Everglades National Park (FCE LTER), Florida, USA, September 2000 - ongoing
Point measurements of Salinity, temperature and turbidity collected during visits to TS/Ph 7a, TS/Ph8, TS/Ph9, TS/Ph10, TS/Ph11, and Rabbit Key. Graphic representation of seagrass status and trends monitoring data and other related information can be located at http://serc.fiu.edu/seagrass/!CDreport/DataHome.htm
Physical and chemical properties of soils on Watershed 5 of Hubbard Brook Experimental Forest, before and after whole-tree harvest
We sampled soils on watershed 5 at the Hubbard Brook Experimental Forest in 1983, prior to a whole-tree harvest conducted in the winter of 1983-84. We resampled in 1986, 1991, and 1998. All sampling was performed using a quantitative soil pit method. Samples of the combined Oi and Oe horizons; the Oa horizon; 0-10 cm, 10-20 cm, and >20 cm layers of mineral soil; and the C horizon were collected. Grab samples of pedogenic mineral horizons were also taken from the sides of a subset of pits in each year. Here we report soil chemistry, mass of soil, percent rock, bulk density, and organic matter. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Soil physical and chemical properties of gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023
This dataset contains data for soil physical and chemical properties of gypsum and non-gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. Site info and characteristics data can be accessed at knb-lter-jrn.210616001. Soil physical properties included: percent gravel, percent < 2mm fraction, soil aggregate stability, and soil compaction. Soil chemical properties were: percent gypsum content, pH, EC, and soil soluble concentrations of calcium, magnesium, potassium, sulfur, and phosphorus. The resulting soil data was used to understand physical and chemical differences between gypsum and non-gypsum soils and to examine how biocrust community types and moss species abundance and composition were associated with the measured soil variables. This study and dataset are complete.
Advective nitrate fluxes, sea surface chlorophyll concentrations and other physical metrics in the Santa Barbara Channel (2012-2019)
This data package includes 6 files: (1 & 2) In-situ nitrate concentrations at the surface and mixed layer depth, and collocated remotely-sensed and reanalysis quantities of satellite sea surface temperature, 15-day cumulative wind stress, satellite sea surface chlorophyll with a 5-day lag, index of offshore position of the California Current, indices for along-channel and across-channel distance, and index for day of the year. (3) An R script for generating generalized additive models (GAMs) to predict nitrate concentrations at the surface and at the mixed layer depth using the collocated data in files 1 & 2. (4) Daily maps of satellite sea surface chlorophyll concentrations (SSChl), High-frequency radar (HFR) surface currents, weather research and forecasting (WRF) model wind-derived vertical velocities, estimated nitrate concentrations at the surface and mixed layer depth, horizontal advective nitrate fluxes at the surface and vertical advective nitrate fluxes. (5) Daily time series of spatial mean SSChl, principal component amplitude of the first mode of variability in surface currents estimated using complex empirical orthogonal function (EOF) analysis, alongshore pressure gradient, wind stress, spatial mean horizontal velocities at the western and eastern Santa Barbara Channel boundaries, spatial mean vertical velocities, spatial mean surface nitrate concentrations at the channel boundaries and across the entire channel, spatial mean mixed layer depth nitrate concentrations across the entire channel, spatial mean horizontal advective nitrate fluxes at the channel boundaries, and spatial mean vertical advective nitrate fluxes. (6) A MATLAB script for plotting examples of the daily maps and time series in files 4 & 5. These data were processed in order to investigate the impact of local nutrient delivery mechanisms on phytoplankton blooms in the Santa Barbara Channel, California, details of which are available in the study: Brokaw, R.J., D.A. Siegel, L. Washburn,
Benchmark Data for AI Safety for High Energy Physics
<p><strong>Datasets for the paper "AI Safety for High Energy Physics" by Ben Nachman and Chase Shimmin (<a href="https://arxiv.org/abs/1910.08606">arXiv:1910.08606</a>)</strong></p> <p>This record contains two files: particles_jj.npz and particles_yz.npz, which contain simulated events of dijet and Z+photon production, respectively, from proton-proton collisions at sqrt(s)=13 TeV.</p> <p>The parton-level events are generated with MadGraph5 aMC@NLO, which are then passed to Pythia 8 for parton showering and hardonization, and then finally to Delphes3 for ATLAS-like detector simulation. Reconstructed calorimeter towers are clustered using the anti-kT algorithm with radius parameter R=1.0. The highest-pT jet from each event is selected, and only events with jet pT > 300 GeV are saved.</p> <p>The Npz files contain three dictionary keys:</p> <ul> <li><strong>jets</strong><strong>:</strong> (N, 4)-shape array containing the pT, eta, phi, and mass of the leading R=1.0 jet for each event</li> <li><strong>constituents:</strong> (N, 128, 3)-shape array containing the pT, eta, phi of up to 128 highest-pT constituent momenta from the leading jet cluster. Jets with fewer than 128 constituents are padded with zero values.</li> <li><strong>photons:</strong> (N, 3)-shape array containing the pT, eta, phi of the leading reconstructed photon (if any) of the event. Events with no photon are filled with zeros.</li> </ul> <p>pT and mass values are stored in units of TeV.</p>
Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"
<p>This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (<a href="https://ui.adsabs.harvard.edu/abs/1992AcA....42..253U/abstract">OGLE</a>), while ancillary information was obtained from the Gaia Data Release 2 (<a href="https://ui.adsabs.harvard.edu/link_gateway/2016A&A...595A...1G/doi:10.1051/0004-6361/201629272">GAIA DR2</a>). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference).</p> <p>This dataset is realized in tandem with the corresponding <a href="https://github.com/jorgemarpa/PELS-VAE">GitHub</a> and <a href="https://arxiv.org/abs/2005.07773">article</a>.</p> <p> </p> <p> </p>
The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition
<p><strong>Dataset related to the paper on Response inhibition to physical inactivity stimuli using go/no-go tasks. </strong></p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_Go_noGo_Miller.xlsx"</p> <p><strong>2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task</strong></p> <p>--> "corrected.behavioral.data.csv"</p> <p>--> "correct_Order.csv"</p> <p><strong>3) Self-reported data </strong></p> <p>--> "Self_report_data.csv"</p> <p><strong>3) EEG data </strong></p> <p>--> "gng_data"</p> <p><strong>5) R script for the data management (i.e., from the raw data to data ready to be analyzed)</strong></p> <p>--> "Data_management_Self_report_go_no_go_Miller.R" for the self-reported data (return the file: "Data_SR_final.RData")</p> <p>--> "Data_management_behav_go_no_go_Miller.R" for the behavioral outcomes (return the file: "Data_GNG_behav.RData")</p> <p>--> Data ready to be analyzed "Data_GNG_final_all.RData"</p> <p><strong>6) Eprime script for the affective go/no-go task ("Go_no_go_task.zip")</strong></p> <p>--> Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)</p> <p><strong>7) R script for the models tested</strong></p> <p><strong>--> "</strong>Models_GoNogo_Miller_VZenodo.R" for behavioral data</p> <p>--> "Models_EEG_GoNogo.R" for EEG data</p>
Detailed abundances based on different nuclear physics for theoretical r-process scenarios
<p>This data set contains detailed abundances (at a time t=10^6 years after the event) for individual trajectories for seven different simulations of potential r-process sites, and based on nine different combinations of nuclear mass models and fission fragment distribution models. The data have been used and are discussed in Cote, Eichler, Yagüe, et al. (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) to determine the isotopic ratios of I129/Cm247 and compare them to meteoritic data.</p> <p>Furthermore, a code is included which samples a subset of trajectories reproducing the measured meteoritic I129/Cm247 abundance ratio of 438 +- 92. See the README file and the publication (https://ui.adsabs.harvard.edu/abs/2020arXiv200604833C/abstract) for more details.</p>
DebDaB: A database of supraglacial debris thickness and physical properties
<p><strong>DebDaB: A database of supraglacial debris thickness and physical properties</strong></p> <p>DebdaB is a database of measured and reported physical properties and thickness of supraglacial debris that is openly available and open to community submissions.</p> <p>The majority of the database (90%) is compiled from 172 sources in the literature, and the remaining 10% has not been published before. DebDaB contains 8,286 data entries for supraglacial debris thickness, of which 1,852 entries also include sub-debris ablation rates, 167 data entries of thermal conductivity of debris, 157 of aerodynamic surface roughness length, 77 of debris albedo, 56 of debris emissivity and 37 of debris porosity. The data are distributed over 83 glaciers in 13 regions in the Global Terrestrial Network for Glaciers. </p> <p>This is version 2 of the dataset, corresponding to the revised version of the database after peer-review of its accompanying "Data descriptor manuscript" submitted for publication to the scientific journal "Earth System Science Data (ESSD)" from Copernicus Publications. The preprint is available at <a href="https://doi.org/10.5194/essd-2024-559">https://doi.org/10.5194/essd-2024-559 </a></p> <p>DebDaB version 2 consists of the following files:</p> <ul> <li>DebDaB_v2.zip : The actual DebDaB database, provided as a navigable Open Document Spreadsheet (.ods) with spreadsheet tabs for each of the debris properties. Additionally, the database is also provided as separate .csv files for each debris property, and as a GeoPackage (.gpkg). </li> <li>Readme_files.zip: A .txt file for each of the debris property tabs, describing all the fields in each tab. </li> <li>Templates_for_data_submission.zip: Templates (.csv files and additionally .xlsx files) for data submission for each of the debris properties in DebDaB. Data submissiosn to DebDaB should be sent to debriscoveredglaciers@ista.ac.at. </li> <li>DebDaB_data_sources.pdf: List of DebDaB sources from published literature. </li> <li>DebDaB_data_sources.bib: BibTeX list of DebDaB sources from published literature. </li> <li>Manuscript_codes.zip: The codes to download and process the data to generate the figures for data descriptor manuscript on ESSD.</li> </ul> <p>The data descriptor manuscript is in open review stage at: <a href="https://essd.copernicus.org/preprints/essd-2024-559/">https://essd.copernicus.org/preprints/essd-2024-559/ </a></p> <p><strong>DebDaB is open to new data submissions</strong>, and therefore future data submissions of previously unpublished data to DebDaB will entail co-authorship on the DebDaB database on Zenodo. </p> <p>According to the authors’ understanding of FAIR principles, authors of published literature and published data, that:</p> <ul> <li>Correct existing data within DebDaB, in case of errors</li> <li>Send the raw data from digitised figures</li> <li>Submit additional data that was previously unavailable (for example, accurate coordinates or additional data or metadata which is not already available)</li> </ul> <div>will have the right to be added as co-authors on the database in Zenodo. The authors are working to reevaluate their policies to conform to changes or unusual circumstances in authorship contributions, and are happy to involve eager people in the core team.</div> <div> </div> <div><strong>How to submit data: </strong>Please use the templates provided in the database files for data submissions and send it to debriscoveredglaciers@ista.ac.at. Authors who submit data will be asked to fill in a form regarding authorship contributions. </div> <p><strong>Important note on citations:</strong> DebDaB data users must cite the data descriptor manuscript (Fontrodona-Bach et al. 2025), the DebDaB zenodo repository<br>(Groeneveld et al., 2025), <strong>and the original data sources</strong> when using the database, given that DebDaB is mostly<br>a compilation of previously published data. To facilitate the citations of original data sources, each of the data entries in DebDaB contains the corresponding<br>original reference and corresponding DOI.</p> <p><strong>Manuscript citation:</strong> Fontrodona-Bach, A., Groeneveld, L., Miles, E., McCarthy, M., Shaw, T., Melo Velasco, V., and Pellicciotti, F.: DebDaB: A database of supraglacial debris thickness and physical properties, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2024-559, in review, 2025.</p> <p><strong>Zenodo citation:</strong> Groeneveld, L., Fontrodona-Bach, A., Miles, E., McCarthy, M., Melo Velasco, V., Shaw, T., Pellicciotti, F., Bauder, A., Buri, P., Kneib, M., Kumar, A., Mishra, A., & Petersen, L. (2025). DebDaB: A database of supraglacial debris thickness and physical properties (Version v2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.14514803" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.</a><a href="https://doi.org/10.5281/zenodo.14224835" target="_blank" rel="noopener">14224835</a></p> <p><strong>Original data sources citation:</strong> See <em>DebDaB_data_sources.pdf</em> or <em>DebDaB_data_sources.bib</em></p> <p>The authors acknowledge the Debris-Covered Glaciers Working Group (DCGWG) from the International Association of Cryospheric Sciences (IACS) for setting the stage and drawing together the debris-covered glaciers community to focus on broader needs transcending a specific research topic, and starting the zenodo community on debris-covered glaciers, where this database is hosted. </p> <p><strong>Author contributions: </strong>The following spreadsheet states the contribution of each of the co-authors on the database: <br><a href="https://docs.google.com/spreadsheets/d/1nTieH_ZkwqnUpHQMYn7bygEcV5RzX4DJuqZ_qd-_PzE/edit?usp=sharing" target="_blank" rel="noopener">Author contributions statement (click here)</a></p> <p>A description of what each contribution field means is below:</p> <ul> <li><em>Conceptualisation:</em> This refers to the original idea and shaping of the database and is therefore closed.</li> <li><em>Data curation:</em> The data managers of DebDaB. Primarily the quality checks and curation done to all the collected published and unpublished data. It may also include authors who have compiled a lot of measurements from sources the authors did not have, and merged them into DebDaB, or if someone else takes on the role of ingesting/homogenizing data in the future.</li> <li><em>Data collection: </em>Field measurements as well as scouring past literature that the authors have missed, digitising sources, or advocating for old missing data sources to be entered into DebDaB.</li> <li><em>Formal analysis:</em> In the case of methods being applied to derive debris property values from other measurements, such as the case for surface roughness and thermal conductivity.</li> <li><em>Supervision/funding: </em>This refers to funding provided for the generation of DebDaB itself, but also funding for the data collection (measurements). </li> </ul>
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
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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.