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76,402,788 results
Mining and Metallurgical Residue Database
<p><span>The dataset includes 44 relevant data attributes from 64 mining and metallurgical sites in 27 countries. </span></p>
Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests
<p><strong>Data description</strong></p> <p>These datasets were generated for the Geostory "Forest disturbances in Europe mapped at high spatial detail and in near-real-time: Logging in protected Estonian forests" in the context of the Open Earth Monitor Cyberinfrastructure project.</p> <p>We used open source high-resolution Sentinel-1 satellite data to develop a wall-to-wall map of forest disturbances in the four-year period between the start of 2020 and end of 2023 in Estonia. First results are presented. The methodology is based on RADD-alerts developed for the pan-tropics (Reiche et al. 2021). Three years (2017-2019) of imagery was used as a historical period, and detections were generated for ~4 years (2020-2023). Winter images from November through March were not included as frozen conditions can introduce false detections. This will be addressed in the next version. Disclaimer: Disturbance maps have not been validated.</p> <p>Two additional layers are provided for visualization: a forest baseline layer (<em>forestcover</em>), masking out non-forest disturbance detections, was derived from Copernicus 10m 2018 forest cover density and GLAD 30m 2019 tree removal datasets, and a protected areas layer (<em>natura</em>), which displays the extent of Natura 2000 coverage in Estonia.</p> <p>'.SLD' files are provided for visualization (note: the <em>disturbance</em> .SLC file must be adjusted to contain appropriate time reference fields).</p> <p><strong>Naming Convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For instance:</p> <ul> <li>disturbance_radd_c_10m_s_20200101_20200131_eu_epsg.3035_v20240222.tif</li> </ul> <p>with the following fields:</p> <ul> <li>Generic variable name: <strong>disturbance</strong></li> <li>Variable procedure combination i.e. method standard: <strong>radd</strong></li> <li>Position in the probability distribution / variable type: <strong>c</strong></li> <li>Spatial support: <strong>10m</strong></li> <li>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): <strong>s</strong></li> <li>Time reference begin time (YYYYMMDD): <strong>20200101</strong></li> <li>Time reference end time: <strong>20200131</strong></li> <li>Bounding box (2 letters max): <strong>eu </strong></li> <li>EPSG code: <strong>epsg.3035</strong></li> <li>Version code i.e. creation date: <strong>v20240222</strong></li> </ul> <p><strong>Source Data</strong></p> <p>Disturbance maps:</p> <p>Contains modified Copernicus Sentinel data [2017-2023] and Generated using European Union's EEA-10 Copernicus DEM; https://doi.org/10.5270/ESA-c5d3d65</p> <p>Forest baseline:</p> <p>Generated using European Union's Copernicus Land Monitoring Service information; https://doi.org/10.2909/486f77da-d605-423e-93a9-680760ab6791 and GLAD tree removal; https://doi.org/10.1016/j.rse.2023.113797</p> <p>Natura 2000: </p> <p>Generated using European Environmental Agency's Natura 2000 layers; https://sdi.eea.europa.eu/data/dae737fd-7ee1-4b0a-9eb7-1954eec00c65</p>
EATRIS-Plus multi-omics data of a human reference cohort
<p>In this reference study, blood samples of 127 healthy individuals were analyzed with a wide range of -omics technologies, resulting in the most comprehensive -omics <br>profiling data set that is publicly available. The molecular measurements that are available here, can be used as reference values for any future (multi-)omics studyies. Along with phenotypic information (Sex, Age, BMI etc. and measured cell types levels) on the healthy subjects, the following data types are included:</p> <ul> <li>Targeted metabolomics (acylcarnitines, amino acids and very long chain fatty acids)</li> <li>Lipidomics (negative and positive ionization modes)</li> <li>Proteomics</li> <li>mRNA-seq</li> <li>miRNA-seq</li> <li>miRNA qRT-PCR</li> <li>Enzymation Methylation sequencing</li> </ul> <p>The pre-processed mult-omics data can be accessed here in the shape of a MultiAssayExperiment object (<a href="https://doi.org/10.1158/0008-5472.can-17-0344">Ramos et al. 2017</a>). Instructions on how to read the object into R can be found here: <a href="https://github.com/EATRIS/Read_MultiAssayExperiment">Read_MultiAssayExperiment</a>.</p> <p>A similar object for Python (MuData) including the same data will be added later. </p> <p> </p> <p>DATA AVAILABILITY STATEMENT:</p> <p>Full data related to the EATRIS-Plus multiomic cohort are available in the ClinData repository (<a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclindata.imtm.cz%2F&data=05%7C02%7CCasper.deVisser%40radboudumc.nl%7C347853c763954a30b82208dc3ebe2571%7Cb208fe69471e48c48d87025e9b9a157f%7C0%7C0%7C638454233596610669%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C0%7C%7C%7C&sdata=Jp7u%2BXblry9QNg4EQkJE4CKxMZZfMK9U84Ob7E6up90%3D&reserved=0">https://clindata.imtm.cz</a>) and include full phenotypic information, physical and laboratory examinations, multiomic data from white blood cells (whole genome sequencing, enzymatic methylation DNA sequencing, mRNA sequencing, miRNA sequencing) or plasma (miRNA qPCR profiling, proteomics, targeted metabolomics, untargeted lipidomics, Raman spectroscopy profiling). However, access is restricted due to legal, ethical, scientific and/or commercial reasons. Access to the data is subject to approval and a data sharing transfer agreement. For data access please contact <a href="mailto:data.access@imtm.upol.cz">data.access@imtm.cz</a>. </p>
XAlkeneDB: A database illuminating the electronic ground and excited state quantum chemical features of ethene, propene and butene
<div> <div> <div> <p>The dataset associated with this research has been published in <a href="https://pubs.rsc.org/en/content/articlelanding/2024/sc/d4sc04164j" target="_blank" rel="noopener"> Chem. Sci., 2024,15, 15880-15890.</a> Please cite this journal article when using the data.</p> </div> </div> </div>
Row data for the experiment: "Clinical, psychosocial and demographic factors affect decisions in SLE people".
<p>These datasets correspond to the article titled: “Clinical, psychosocial and demographic factors affect decisions in SLE people”, which can be found at <a href="https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf">https://www.medrxiv.org/content/10.1101/2024.03.25.24304643v1.full.pdf</a></p> <p>Analysis scripts, and an explanation of variables, can be found at: <a href="https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE">https://github.com/NeuroGenomicsMX/Factors_affecting_decisions_in_SLE</a></p> <p>Abstract</p> <p><span>Neurological and psychiatric manifestations affect most lupus individuals and include depression, anxiety, mood disorders, and cognitive dysfunction. Although there is evidence supporting suboptimal decision-making in lupus and its association with glucocorticoids consumption, it is not clear what variables impact such decisions. The aim of this study is to explore how social, clinical, psychological, and demographic factors impact social and temporal decision-making in people with lupus. Through a within-subjects experimental-design, our participants responded to social, clinical, psychological, and demographic electronic questionnaires. Then, they participated in two behavioral economics experiments: the third-party dictator game, and the delay discounting task. Our results show that hostility, and age are essential predictors of social decisions, whereas obsessive-compulsiveness and anxiety better predict temporal decisions. These variables behave as expected, but anxiety shows unexpected results: most anxious people act patiently and prefer delayed but bigger rewards. Finally, clinical factors are critical decision predictors for social and temporal decisions. When people are in remission, they tend to impose higher punishment on those who violate the social norm, and they also tend to prefer immediate rewards. When taking glucocorticoids, they also prefer immediate rewards, and as the dosage of glucocorticoids intake increases, they tend to impose higher punishment on norm violators. Clinicians, researchers, and practitioners must consider the side effects of glucocorticoids on decision-making.</span></p>
Dataset for algorithmic thinking skills assessment: Results from the virtual CAT large-scale study in Swiss compulsory education
<p><strong>Overview</strong><br>This dataset was collected during a main study that evaluated the virtual Cross Array Task (CAT) platform as an assessment tool for algorithmic thinking (AT) skills among K-12 students in Swiss compulsory education.<br>As algorithmic thinking becomes increasingly vital in our digital age, this study bridges the gap between traditional assessments and the needs of today's learners by introducing a digital platform. The virtual CAT, a digital adaptation of an unplugged assessment activity, offers scalable, automated assessments with reduced human intervention.</p> <p><strong>Study Context, Location and Participants</strong><br>To comprehensively investigate algorithmic competencies within compulsory education, exploring their variations and determining the factors influencing them, in Spring 2023 we conducted an experimental study with the virtual CAT's.<br>The sample comprises 129 students (65 girls and 64 boys), selected from nine classes across five public schools in Ticino and Solothurn cantons.</p> <p><strong>Data Collection</strong><br>During the data collection process, session and participant details were manually recorded by the administrator. <br>Each session has been assigned a unique identifier, and specific details, such as the date, canton, school name and type, and the students’ HarmoS grade (HG) level, have been recorded. <br>Student information are limited to sex and date of birth, with birth dates used to calculate ages, a significant factor in our demographic analysis. <br>To protect student privacy, unique identifiers have been assigned to each participant, keeping the data anonymous and secure. <br>The assessment tool automatically tracked all user interaction within the platform.<br>All data collected have been pseudonymised, aligning with prevailing open science practices in Switzerland (SNSF, 2021). <br>Data collection was integrated into a validation module of the app. </p> <p><strong>Data Features</strong><br>The dataset comprises the following files:</p> <ul> <li>STUDENTS_SESSIONS.csv</li> <li>RESULTS.csv</li> <li>LOGS.csv</li> <li>CANTONS.csv</li> <li>ALGORITHMS.csv</li> </ul> <p>These files collectively provide insights into the algorithmic actions of the students, demographic details, session logs, results, and more.</p> <p><strong>Usage & Ethics</strong><br>In the spirit of open science, this dataset is made available to the public after meticulous anonymisation to ensure all participants' privacy and ethical treatment. <br>Initial authorisations were secured from school administrators, teachers, and parents. <br>Detailed communication regarding the study's nature, data handling, and objectives was transparently shared with all stakeholders.</p> <p><strong>REFERENCES</strong></p> <p><strong>[1]</strong> A. Piatti, G. Adorni, L. El-Hamamsy, L. Negrini, D. Assaf, L. Gambardella & F. Mondada. (2022). The CT-cube: A framework for the design and the assessment of computational thinking activities. Computers in Human Behavior Reports, 5, 100166. <a href="https://doi.org/10.1016/j.chbr.2021.100166">https://doi.org/10.1016/j.chbr.2021.100166</a></p> <p><strong>[2]</strong> Adorni, G., & Piatti, S., & Karpenko, V. (2023). virtual CAT: An app for algorithmic thinking assessment within Swiss compulsory education. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10027851">https://doi.org/10.5281/zenodo.10027851</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-app/</a></p> <p><strong>[3]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT programming language interpreter. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10016535">https://doi.org/10.5281/zenodo.10016535</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-programming-language-interpreter/</a></p> <p><strong>[4]</strong> Adorni, G., & Karpenko, V. (2023). virtual CAT data infrastructure. Zenodo Software. <a href="https://doi.org/10.5281/zenodo.10015011">https://doi.org/10.5281/zenodo.10015011</a> On GitHub: <a href="https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure">https://github.com/GiorgiaAuroraAdorni/virtual-CAT-data-infrastructure</a></p> <p> </p>
Short example of Biceps Brachii muscle surface HDEMG decomposition using the DEMUSE Tool
<p>This dataset contains 4 examples of synthetic high density surface EMG signals of the Biceps Brachii muscle and results of their decomposition into separate motor unit activity. It is intended as a demonstration of the DEMUSE Tool software for sEMG decomposition and as a basis for practical example of dataset preparation for the HybridNeuro project webinar on Data management and ethics (<a href="https://www.hybridneuro.feri.um.si/results.html#webinars">https://www.hybridneuro.feri.um.si/results.html#webinars</a>). Two sets of data are included: the raw simulated sEMG signals and the results of decomposition of those signals with the DEMUSE Tool.</p>
Long-term Agricultural Experiments: Data Management Survey
<p>Results of an online survey used to guage views of researchers within the LTE community on data management issues and knowledge. The survey was broken down in to 4 main questions and can be found at the following link - further responses are still welcome: <a href="https://forms.office.com/e/8DmapwLRr8" target="_blank" rel="noopener">https://forms.office.com/e/8DmapwLRr8</a>.</p> <ul> <li>About your role</li> <li>Data management & sharing</li> <li>Describing LTEs and their data</li> <li>Challenges for data management & sharing </li> </ul> <p>At the time of publication, 55 responses had been recieved.</p> <p>The survey was developed in response to an LTE Conference Workshop held at Rothamsted Research, UK in June 2023.</p>
Database of 3D Concrete Printed Buildings
<p>This dataset contains all 3D concrete printed buildings known to the authors built between 2013 and 2023. This dataset is part of a publication and was used to research different fabrication strategies. The Excel database developed for this purpose is divided into 22 categories and filled in as far as possible. The sources are also indicated in the database. For a more detailed description of the categories and the results of the study, please refer to the corresponding publication. We would be happy if the data are used and expanded for future research into 3D concrete printing.</p>
Multi-Class Depression Detection Dataset
<p>This dataset was created as part of the Master's thesis titled "Multi-Class Depression Detection Through Tweets Using Artificial Intelligence." It contains tweets labeled for five types of depression (Bipolar, Major, Psychotic, Atypical, and Postpartum) using lexicons verified by psychiatrists. </p> <p>Purpose: Designed for multi-class classification of depression using AI, focusing on Explainable AI for highlighting key words in the tweets influencing the predictions.<br>Applications: The dataset is suitable for research in natural language processing, sentiment analysis, mental health prediction, and Explainable AI.</p> <p>This dataset is shared under the Creative Commons Attribution 4.0 International (CC BY) license, requiring proper attribution for any use or modification.</p>
Size scalability of Monte Carlo simulations applied to oxidized polypyrrole systems: Data and Codes
<p>This work generalizes our recently proposed coarse grained force field (CGFF) for halogen oxidized PPy in the condensed phases and introduces a novel implementation of the Nettropolis Monte Carlo (MMC) simulation based on the CGFF that enables simulations of polymer systems with more than<br>100000 particles. The MMC implementation utilizes a combination of CPU and GPUs and exploits a numerical approximation based on polynomial piecewise interpolation for the calculation of the CGFF pairwise additive terms. Our simulations evidence that the oxidized PPy thermodynamic and structural properties are consistent as the system size is scaled up. Predicted properties include density, enthalpy, potential energy, heat capacity, coefficient of thermal expansion, caloric curve, glass transition temperature range, compressibility, bulk modulus, radial distribution functions, and polymer chain characteristics.</p>
Historical and future irrigation water demand for the STARS4Water river basins
<p>Dataset contains data on historical and future irrigation water demand for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average summer net irrigation requirement [mm/year] for each combination GCM model (5 models)/time window (2 windows) was calculated within the boundaries of the project river basin hubs. The difference between the future and historical period was also calculated for each GCM. In addition, ensemble mean values for both horizons and ensemble mean differences were calculated. This dataset was prepared based on the data available in the "Net irrigation requirement under different climate scenarios using AquaCrop over Europe" repository (Busschaert et al., 2022, DOI: 10.5281/zendo.6760976).</p>
Historical and future land use and land cover data for the STARS4Water river basins
<p>Dataset contains data on historical and future land use and land cover for seven European river basins (Danube, Drammen, Duero, East Anglia, Messara, Rhine and Seine) being case study basin in the STARS4Water, and a shapefile with river basin boundaries. The average area fraction of five general land use classes (crop, forest, grass, urban and other) within the project river basins was calculated at five-year intervals starting in 2016 and ending in 2051. This dataset was prepared based on the data available in the "LUCAS LUC future land use and land cover change dataset for Europe (Version 1.1)" repository (Hoffmann et al., 2022, DOI: 10.26050/WDCC/LUC_future_EU_v1.1).</p>
Cuneiform Inscriptions Geographical Site - Assemblage Estimates (CIGS-AE)
<p>The <em>Cuneiform Inscriptions Geographical Site - Assemblage Estimates </em>(CIGS-AE) provides basic overall estimates of and bibliographical references for the approximate number of cuneiform inscriptions derived from individual archaeological locations. In use across the wider Middle East from c. 3,400 BCE until 100 CE, cuneiform is one of the earliest and most extensively documented ancient scripts in world history. The CIGS-AE is a component of the <a href="https://doi.org/10.5281/zenodo.4960710">Cuneiform Inscriptions Geographial Site (CIGS)</a> index, a digital geospatial register of archaeological sites with finds of cuneiform inscriptions across Europe, Asia, and Africa.</p> <p>CIGS-AE provides a first comprehensive quantitative overview of the approximate number of cuneiform inscriptions unearthed from known archaeological locations. It does not provide an overview of the entire corpus of cuneiform inscriptions known, as the index disregards all inscriptions with no verifiable archaeological origin, estimated to be between fifteen and twenty per cent of the overall corpus according to the catalogue of the <a href="https://cdli.mpiwg-berlin.mpg.de/heatmap">Cuneiform Digital Library Initiative</a>. The present resource then offers a lower threshold for the size of the full cuneiform corpus and a fairly reliable overview of its general distribution. The accompanying bibliography offers a basic set of references for all known archaeological sites with finds of cuneiform inscriptions. This information is intended as a starting point for further study, and should not be considered an exhaustive nor authoritative bibliography.</p> <p>This resource has been prepared by researchers of the <a href="http://lingfil.uu.se/">Department of Linguistics and Philology</a> of <a href="http://uu.se/">Uppsala University</a>. The index is intended as a tool for students and researchers in cuneiform studies and related areas and as an aid to cultural heritage managers and educators in communicating and safeguarding this unique body of world written heritage. The index remains under development and is regularly updated. The authors will very much appreciate notices of any omissions, errors, or inaccuracies. For any inquiries, please contact <a href="https://www.katalog.uu.se/profile/?id=N18-1120">Rune Rattenborg</a> (<a href="mailto:rune.rattenborg@lingfil.uu.se?subject=%5BCIGS-AE%5D%3A">rune.rattenborg@lingfil.uu.se</a>).</p> <p>The version 1.1 index contains 603 entries with a total five fields, including one primary ID, two integer fields for assemblage size and grouping, and two string fields for bibliographical references and notes. Record identifiers are matched with primary IDs in <a href="https://doi.org/10.5281/zenodo.4960710">Cuneiform Inscriptions Geographial Site (CIGS)</a> index to allow for geospatial visualisation of quantitative data. Reference short titles are matched with short titles contained in the accompanying .bibtex.</p>
Data from: Satellite-based Lagrangian model reveals how upwelling and oceanic circulation shape krill hotspots in the California Current System [updated]
<p><strong>Abstract</strong></p> <p>In the California Current System, wind-driven nutrient supply and primary production, computed from satellite data, provide a synoptic view of how phytoplankton production is coupled to upwelling. In contrast, linking upwelling to zooplankton populations is difficult due to relatively scarce observations and the inherent patchiness of zooplankton. While phytoplankton respond quickly to environmental forcing, zooplankton grow slower and tend to aggregate into mesoscale “hotspot” regions spatially decoupled from upwelling centers. To better understand mechanisms controlling the formation of zooplankton hotspots, we use a satellite-based Lagrangian method where variables from a plankton model, forced by wind-driven nutrient supply, are advected by near-surface currents following upwelling events. Modeled zooplankton distribution reproduces published accounts of euphausiid (krill) hotspots, including the location of major hotspots and their interannual variability. This satellite-based modeling tool is used to analyze the variability and drivers of krill hotspots in the California Current System, and to investigate how water masses of different origin and history converge to form predictable biological hotspots. The Lagrangian framework suggests that two conditions are necessary for a hotspot to form: a convergence of coastal water masses, and above average nutrient supply where these water masses originated from. The results highlight the role of upwelling, oceanic circulation, and plankton temporal dynamics in shaping krill mesoscale distribution, seasonal northward propagation, and interannual variability.</p> <p><strong>Data set description</strong></p> <p>This data set includes 2 files:</p> <ul> <li>a satellite-based 1993-2023 monthly retrospective of krill concentrations (Zbig) modeled using the growth-advection method in the California Current upwelling system. Inputs include the nitrate supply product described below and GlobCurrent 15 m oceanic currents. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/krill-hotspots-in-the-california-current/.</li> <li>a satellite-based 1993-2023 monthly retrospective of wind-driven nitrate supply estimated in a 150 km coastal band at 0.125° latitudinal resolution. Nitrate supply was calculated based primarily on CCMP v3.1 winds, AVISO geostrophic currents, and a climatology of in situ nitrate at 60m. This dataset is updated monthly (using NRT data) at https://www.mbari.org/science/upper-ocean-systems/biological-oceanography/nitrate-supply-estimates-in-upwelling-systems/.</li> </ul> <p>See details regarding data sources and calculations in <a href="https://doi.org/10.3389/fmars.2022.835813">Messié et al. (2022)</a>.</p> <p>[IMPORTANT NOTE:] There is an error in the Ekman pumping fields (trans_pump, Nsupply_pump, Nsupply_total) that will be corrected soon (those fields are not used in publications where only coastal transport was considered). Please contact me if you need Ekman pumping fields before this is fixed.</p>
glenglat: Global englacial temperature database
<p>Open-access database of englacial temperature measurements compiled from data submissions and published literature. It is developed on <a href="https://github.com/mjacqu/glenglat">GitHub</a> and published to <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>. This version (1.0.0) of the dataset is described in the following publication:</p> <blockquote> <p>Mylène Jacquemart, Ethan Welty, Marcus Gastaldello, and Guillem Carcanade (2025). glenglat: A database of global englacial temperatures. Earth System Science Data 17(4): 1627–1666. <a href="https://doi.org/10.5194/essd-17-1627-2025">https://doi.org/10.5194/essd-17-1627-2025</a></p> </blockquote> <h2>Dataset structure</h2> <p>The dataset adheres to the Frictionless Data <a href="https://specs.frictionlessdata.io/tabular-data-package">Tabular Data Package</a> specification. The metadata in <code>datapackage.json</code> describes, in detail, the contents of the tabular data files in the <code>data</code> folder:</p> <ul> <li><code>source.csv</code>: Description of each data source (either a personal communication or the reference to a published study).</li> <li><code>borehole.csv</code>: Description of each borehole (location, elevation, etc), linked to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>profile.csv</code>: Description of each profile (date, etc), linked to <code>borehole.csv</code> via <code>borehole_id</code> and to <code>source.csv</code> via <code>source_id</code> and less formally via source identifiers in <code>notes</code>.</li> <li><code>measurement.csv</code>: Description of each measurement (depth and temperature), linked to <code>profile.csv</code> via <code>borehole_id</code> and <code>profile_id</code>.</li> </ul> <p>For boreholes with many profiles (e.g. from automated loggers), pairs of <code>profile.csv</code> and <code>measurement.csv</code> are stored separately in subfolders of <code>data</code> named <code>{source.id}-{glacier}</code>, where <code>glacier</code> is a simplified and kebab-cased version of the glacier name (e.g. <code>flowers2022-little-kluane</code>).</p> <h3>Supporting information</h3> <p>The folder <code>sources</code>, available on <a href="https://github.com/mjacqu/glenglat">GitHub</a> but omitted from dataset releases on <a href="https://doi.org/10.5281/zenodo.11516611">Zenodo</a>, contains subfolders (with names matching column <code>source.id</code>) with files that document how and from where the data was extracted.</p> <h2>Tables</h2> <p>Jump to: <a href="#source"><code>source</code></a> · <a href="#borehole"><code>borehole</code></a> · <a href="#profile"><code>profile</code></a> · <a href="#measurement"><code>measurement</code></a></p> <h3><a name="source"></a><code>source</code></h3> <p>Sources of information considered in the compilation of this database. Column names and categorical values closely follow the Citation Style Language (CSL) 1.0.2 specification. Names of people in non-Latin scripts are followed by a latinization in square brackets (e.g. В. С. Загороднов [V. S. Zagorodnov]) and non-English titles are followed by a translation in square brackets. The family name of Latin-script names is wrapped in curly braces when it is not the last word of the name (e.g. Emmanuel {Le Meur}, e.g. {Duan} Keqin) or the name ends in two or more unabbreviated words (e.g. Jon Ove {Hagen}). The family name of a Chinese name (and of the latinization) is wrapped in curly braces when it is not the first character.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>string</td> <td>Unique identifier constructed from the first author's lowercase, latinized, family name and the publication year, followed as needed by a lowercase letter to ensure uniqueness (e.g. Загороднов 1981 → zagorodnov1981a).</td> </tr> <tr> <td><code>author</code></td> <td>string</td> <td>Author names (optionally followed by their ORCID or contact email in parentheses) as a pipe-delimited list.</td> </tr> <tr> <td><code>year</code> (required)</td> <td>year</td> <td>Year of publication.</td> </tr> <tr> <td><code>type</code> (required)</td> <td>string</td> <td>Item type.<br>- article-journal: Journal article<br>- book: Book (if the entire book is relevant)<br>- chapter: Book section<br>- document: Document not fitting into any other category<br>- dataset: Collection of data<br>- map: Geographic map<br>- paper-conference: Paper published in conference proceedings<br>- personal-communication: Personal communication between individuals<br>- speech: Presentation (talk, poster) at a conference<br>- report: Report distributed by an institution<br>- thesis-phd: Doctor of Philosophy (PhD) thesis<br>- thesis-msc: Master of Science (MSc) thesis<br>- webpage: Website or page on a website</td> </tr> <tr> <td><code>title</code> (required)</td> <td>string</td> <td>Item title.</td> </tr> <tr> <td><code>url</code></td> <td>string</td> <td>URL (DOI if available).</td> </tr> <tr> <td><code>language</code> (required)</td> <td>string</td> <td>Language as ISO 639-1 two-letter language code.<br>- da: Danish<br>- de: German<br>- en: English<br>- es: Spanish<br>- fr: French<br>- ja: Japanese<br>- ko: Korean<br>- ru: Russian<br>- sv: Swedish<br>- zh: Chinese</td> </tr> <tr> <td><code>container_title</code></td> <td>string</td> <td>Title of the container (e.g. journal, book).</td> </tr> <tr> <td><code>volume</code></td> <td>integer</td> <td>Volume number of the item or container.</td> </tr> <tr> <td><code>issue</code></td> <td>string</td> <td>Issue number (e.g. 1) or range (e.g. 1-2) of the item or container, with an optional letter prefix (e.g. F1) or part number (e.g. 75pt2).</td> </tr> <tr> <td><code>page</code></td> <td>string</td> <td>Page number (e.g. 1) or range (e.g. 1-2) of the item in the container, with an optional letter prefix (e.g. S1).</td> </tr> <tr> <td><code>version</code></td> <td>string</td> <td>Version number (e.g. 1.0) of the item.</td> </tr> <tr> <td><code>editor</code></td> <td>string</td> <td>Editor names (e.g. of the containing book) as a pipe-delimited list.</td> </tr> <tr> <td><code>collection_title</code></td> <td>string</td> <td>Title of the collection (e.g. book series).</td> </tr> <tr> <td><code>collection_number</code></td> <td>string</td> <td>Number (e.g. 1) or range (e.g. 1-2) in the collection (e.g. book series volume).</td> </tr> <tr> <td><code>publisher</code></td> <td>string</td> <td>Publisher name.</td> </tr> </tbody> </table> <h3><a name="borehole"></a><code>borehole</code></h3> <p>Metadata about each borehole.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Unique identifier.</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Identifier of the source of the earliest temperature measurements. This is also the source of the borehole attributes unless otherwise stated in <code>notes</code>.</td> </tr> <tr> <td><code>glacier_name</code> (required)</td> <td>string</td> <td>Glacier or ice cap name (as reported).</td> </tr> <tr> <td><code>glims_id</code></td> <td>string</td> <td>Global Land Ice Measurements from Space (GLIMS) glacier identifier.</td> </tr> <tr> <td><code>location_origin</code> (required)</td> <td>string</td> <td>Origin of location (<code>latitude</code>, <code>longitude</code>).<br>- submitted: Provided in data submission<br>- published: Reported as coordinates in original publication<br>- digitized: Digitized from published map with complete axes<br>- estimated: Estimated from published plot by comparing to a map (e.g. Google Maps, CalTopo)<br>- guessed: Estimated with difficulty, for example by comparing <code>elevation</code> to a map (e.g. Google Maps, CalTopo)</td> </tr> <tr> <td><code>latitude</code> (required)</td> <td>number [degree]</td> <td>Latitude (EPSG 4326).</td> </tr> <tr> <td><code>longitude</code> (required)</td> <td>number [degree]</td> <td>Longitude (EPSG 4326).</td> </tr> <tr> <td><code>elevation_origin</code> (required)</td> <td>string</td> <td>Origin of elevation (<code>elevation</code>).<br>- submitted: Provided in data submission<br>- published: Reported as number in original publication<br>- digitized: Digitized from published plot with complete axes<br>- estimated: Estimated from elevation contours in published map<br>- guessed: Estimated with difficulty, for example by comparing location (<code>latitude</code>, <code>longitude</code>) to a map of contemporary elevations (e.g. CalTopo, Google Maps)</td> </tr> <tr> <td><code>elevation</code> (required)</td> <td>number [m]</td> <td>Elevation above sea level.</td> </tr> <tr> <td><code>mass_balance_area</code></td> <td>string</td> <td>Mass balance area.<br>- ablation: Ablation area<br>- equilibrium: Near the equilibrium line<br>- accumulation: Accumulation area</td> </tr> <tr> <td><code>label</code></td> <td>string</td> <td>Borehole name (e.g. as labeled on a plot).</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Begin date of drilling, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code></td> <td>date (%Y-%m-%d)</td> <td>End date of drilling, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>drill_method</code></td> <td>string</td> <td>Drilling method.<br>- mechanical: Push, percussion, rotary<br>- thermal: Hot point, electrothermal, steam<br>- combined: Mechanical and thermal</td> </tr> <tr> <td><code>ice_depth</code></td> <td>number [m]</td> <td>Starting depth of continuous ice. Infinity (INF) indicates that only snow, firn, or intermittent ice was reached.</td> </tr> <tr> <td><code>depth</code></td> <td>number [m]</td> <td>Total borehole depth (not including drilling in the underlying bed).</td> </tr> <tr> <td><code>to_bed</code></td> <td>boolean</td> <td>Whether the borehole reached the glacier bed.</td> </tr> <tr> <td><code>temperature_uncertainty</code></td> <td>number [°C]</td> <td>Estimated temperature uncertainty (as reported).</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the study site, the borehole, or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> <tr> <td><code>curator</code></td> <td>string</td> <td>Names of people who added the data to the database, as a pipe-delimited list.</td> </tr> <tr> <td><code>investigators</code></td> <td>string</td> <td>Names of people and/or agencies who performed the work, as a pipe-delimited list. Each entry is in the format 'person (agency; ...) {notes}', where only person or one agency is required. Person and agency may contain a latinized form in square brackets.</td> </tr> <tr> <td><code>funding</code></td> <td>string</td> <td>Funding sources as a pipe-delimited list. Each entry is in the format 'funder [rorid] > award [number] url', where only funder is required and rorid is the funder's ROR (https://ror.org) ID (e.g. 01jtrvx49).</td> </tr> </tbody> </table> <h3><a name="profile"></a><code>profile</code></h3> <p>Date and time of each measurement profile.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier (starting from 1 for each borehole).</td> </tr> <tr> <td><code>source_id</code> (required)</td> <td>string</td> <td>Source identifier.</td> </tr> <tr> <td><code>measurement_origin</code> (required)</td> <td>string</td> <td>Origin of measurements (<code>measurement.depth</code>, <code>measurement.temperature</code>).<br>- submitted: Provided as numbers in data submission<br>- published: Numbers read from original publication<br>- digitized-discrete: Digitized with Plot Digitizer from discrete points of depth versus temperature<br>- digitized-continuous: Digitized with Plot Digitizer from a continuous data source (e.g. line plot of depth versus temperature)</td> </tr> <tr> <td><code>date_min</code></td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).</td> </tr> <tr> <td><code>date_max</code> (required)</td> <td>date (%Y-%m-%d)</td> <td>Measurement date, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).</td> </tr> <tr> <td><code>time</code></td> <td>time (%H:%M:%S)</td> <td>Measurement time.</td> </tr> <tr> <td><code>utc_offset</code></td> <td>number [h]</td> <td>Time offset relative to Coordinated Universal Time (UTC).</td> </tr> <tr> <td><code>equilibrium</code></td> <td>string</td> <td>Whether and how reported temperatures equilibrated following drilling.<br>- true: Equilibrium was measured<br>- estimated: Equilibrium was estimated (typically by extrapolation)<br>- false: Equilibrium was not reached</td> </tr> <tr> <td><code>notes</code></td> <td>string</td> <td>Additional remarks about the profile or the measurements therein as a pipe-delimited list. Sources are referenced by <code>source.id</code>. Quality concerns are prefixed with '[flag]'.</td> </tr> </tbody> </table> <h3><a name="measurement"></a><code>measurement</code></h3> <p>Temperature measurements with depth.</p> <table> <tbody> <tr> <th>name</th> <th>type</th> <th>description</th> </tr> </tbody> <tbody> <tr> <td><code>borehole_id</code> (required)</td> <td>integer</td> <td>Borehole identifier.</td> </tr> <tr> <td><code>profile_id</code> (required)</td> <td>integer</td> <td>Borehole profile identifier.</td> </tr> <tr> <td><code>depth</code> (required)</td> <td>number [m]</td> <td>Depth below the glacier surface.</td> </tr> <tr> <td><code>temperature</code> (required)</td> <td>number [°C]</td> <td>Temperature.</td> </tr> </tbody> </table>
Monthly precipitation in mm at 1 km resolution (multisource average) based on SM2RAIN-ASCAT 2007-2021, CHELSA Climate and WorldClim
<p>Monthly precipitation in mm at 1 km resolution based on SM2RAIN-ASCAT 2007-2021 (<a href="https://doi.org/10.5281/zenodo.2615278">https://doi.org/10.5281/zenodo.2615278</a>). <a href="https://github.com/Envirometrix/LandGISmaps/tree/master/input_layers/clim1km">Downscaled to 1 km resolution using gdalwarp</a> (cubic splines) and combined with WorldClim (<a href="https://worldclim.org/data/worldclim21.html">https://worldclim.org/data/worldclim21.html</a>) and CHELSA Climate (<a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a>) monthly values. Final values are estimated as a simple average between the three precipitation data sources; a more objective approach would be to use training points e.g. meteo-station monthly values, then train an ensemble model using the 3 data sources as independent variables. Another global data source of precipitation images is the <a href="https://gpm.nasa.gov/data/imerg">monthly IMERGE dataset</a>, however this requires transformation and is available only for limited span of years.</p> <p>Processing steps are available <a href="https://gitlab.com/openlandmap/global-layers/-/tree/master/input_layers/SM2RAIN"><strong>here</strong></a>. Antarctica is not included. Standard deviation (sd) indicates a difference between the 3 data sources. To access and visualize maps use: <a href="https://openlandmap.org"><strong>https://openlandmap.org</strong></a>.<strong> </strong>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>clm = theme: climate,</li> <li>precipitation = variable: precipitation,</li> <li>wc.v2.1.chelsa.v2.1.sm2rain.oct = determination method: long-term average values for October,</li> <li>m = mean value,</li> <li>1km = spatial resolution / block support: 1 km,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>1980..2020 = time reference: from 1980 to 2020,</li> <li>v0.3 = version number: 0.3,</li> </ul>
Chemical data accompanying the manuscript "Chromium cycling in redox-stratified basins challenges δ53Cr paleoredox proxy applications" in Geophysical research Letters
<p>Water column and sediment chromium concentration and stable isotope data and ancillary metal data from Lake Cadagno, Switzerland. These data accompany a manuscript by the same authors in Geophysical Research Letters (doi: 10.1029/2022GL099154).</p> <p> </p> <p>The associated CTD data are available in the following Zenodo dataset: Sepúlveda Steiner, O., Carlino, C., Haizmann, E., Roman, S., Wüest, A., & Bouffard, D. (2022). Lake Cadagno 2017 CTD and water quality monitoring [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.7127882">http://doi.org/10.5281/zenodo.7127882</a></p>
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
Dataset of reports about MOF-based SERS substrates since 2011 until March 2023. Structure, characteristics, analytes, and performances.
<p>This dataset was generated to aid the creation of a review article addressing the use of Metal-Organic Frameworks (MOF)-based Surface Enhanced Raman Spectroscopy (SERS) platforms for the detection of Volatile Organic Compounds (VOCs).</p> <p>This dataset was generated employing the Web of Science database, encompassing manuscripts published up to March 2023. A literature search was initially conducted using a combination of keywords, including "MOF," "Metal-Organic Framework," "SERS," "Surface Enhanced Raman Spectroscopy," and "Surface Enhanced Raman Scattering." This search spanned the "Topic" category, enabling exploration across title, abstract, author keywords, and keyword-plus fields.</p> <p>From the initial pool of 238 documents, review articles and duplicates were systematically excluded, resulting in a refined collection of 182 articles. Subsequently, articles not concurrently addressing MOF and SERS or those utilizing MOF as sacrificial templates were further excluded, resulting in a final subset of 72 articles. From this curated set, relevant parameters were extracted, resulting in 229 entries for the dataset. </p> <p>Characteristics about the structure (in terms of MOF type and configuration; Plasmonic element type and configuration), target analyte (including type, phase, and incubation time), measurement specifications (in terms of laser, laser power, exposure time), and performance of the MOF-based SERS substrates were collected.</p> <p>Listed references 1-72 correspond with the manuscript number in the dataset.</p> <p>Listed references 73-80 correspond with references for selected examples of MOF pore diameters.</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.