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Pre-processed (in Detectron2 and YOLO format) planetary images and boulder labels collected during the BOULDERING Marie Skłodowska-Curie Global fellowship
<p>This database contains 4976 planetary images of boulder fields located on Earth, Mars and Moon. The data was collected during the BOULDERING Marie Skłodowska-Curie Global fellowship between October 2021 and 2024. The data was already splitted into train, validation and test datasets, but feel free to re-organize the labels at your convenience. </p> <p>For each image, all of the boulder outlines within the image were carefully mapped in QGIS. More information about the labelling procedure can be found in the following manuscript (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013">https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2023JE008013</a>). This dataset differs from the previous dataset included along with the manuscript <a href="https://zenodo.org/records/8171052">https://zenodo.org/records/8171052</a>, as it contains more mapped images, especially of boulder populations around young impact structures on the Moon (cold spots). In addition, the boulder outlines were also pre-processed so that it can be ingested directly in YOLOv8.</p> <p>A description of what is what is given in the README.txt file (in addition in how to load the custom datasets in Detectron2 and YOLO). Most of the other files are mostly self-explanatory. Please see previous dataset or manuscript for more information. If you want to have more information about specific lunar and martian planetary images, the IDs of the images are still available in the name of the file. Use this ID to find more information (e.g., M121118602_00875_image.png, ID M121118602 ca be used on https://pilot.wr.usgs.gov/). I will also upload the raw data from which this pre-processed dataset was generated (see <a href="https://zenodo.org/records/14250970">https://zenodo.org/records/14250970</a>).</p> <p>Thanks to this database, you can easily train a Detectron2 Mask R-CNN or YOLO instance segmentation models to automatically detect boulders. </p> <p><strong>How to cite:</strong></p> <p>Please refer to the "how to cite" section of the readme file of <a href="https://github.com/astroNils/YOLOv8-BeyondEarth" target="_blank" rel="noopener">https://github.com/astroNils/YOLOv8-BeyondEarth.</a></p> <p><strong>Structure:</strong></p> <pre><code>. └── boulder2024/ ├── jupyter-notebooks/ │ └── REGISTERING_BOULDER_DATASET_IN_DETECTRON2.ipynb ├── test/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── train/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── validation/ │ └── images/ │ ├── <image_name>_image.png │ ├── ... │ └── labels/ │ ├── <image_name>_image.txt │ ├── ... ├── detectron2_inst_seg_boulder_dataset.json ├── README.txt ├── yolo_inst_seg_boulder_dataset.yaml</code></pre> <p> </p> <pre><code>detectron2_inst_seg_boulder_dataset.json</code></pre> <p>is a json file containing the masks as expected by Detectron2 (see <a href="https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html">https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html</a> for more information on the format). In order to use this custom dataset, you need to register the dataset before using it in the training. There is an example how to do that in the jupyter-notebooks folder. You need to have detectron2, and all of its depedencies installed. </p> <pre><code>yolo_inst_seg_boulder_dataset.yaml</code></pre> <p>can be used as it is, however you need to update the paths in the .yaml file, to the test, train and validation folders. More information about the YOLO format can be found here (<a href="https://docs.ultralytics.com/datasets/segment/">https://docs.ultralytics.com/datasets/segment/</a>).</p>
Menschen afrikanischer, asiatischer sowie indigen- und afrikanisch-amerikanischer Herkunft im Hamburger Raum, 1760 bis 1840
<p>Dieser prosopographische Datensatz ist aus der Recherche zu einem Dissertationsprojekt der Autorin zu Hamburger Sklavereiverbindungen und nicht-weißen Menschen im Hamburger Raum hervorgegangen, die von 2017 bis 2024 an der Universität Bremen durchgeführt wurde. Es erfasst Personen afrikanischer, asiatischer sowie indigen- oder afrikanisch-amerikanischer Herkunft, die sich zwischen 1760 und 1840 im Hamburger Raum aufhielten und sich durch Sekundärliteratur oder Quellen nachweisen lassen. Die Datenbank stellt ein Teilergebnis der Forschung dar, die in der zugehörigen Dissertation ausgewertet, kontextualisiert und analysiert wird. In die Datenbank aufgenommen wurden Individuen oder – so sich keine Einzelangaben ausfindig machen ließen – kleine Gruppen, die sich nachweislich über kürzere oder längere Zeiträume im heutigen Hamburger Stadtgebiet sowie in angrenzende Gemeinden wie Ahrensburg und Pinneberg aufhielten. Da die Datenbank ein Resultat einer gezielten Recherche ist, kann sie keinen Anspruch auf Vollständigkeit oder Repräsentativität (etwa in Bezug auf Herkunftsregionen) erheben. Ergänzende Informationen zum Quellenkorpus finden sich im Dokument „Durchgesehene serielle Quellen“; weitere Informationen zu den in der Datenbank genutzten Quellen sind im Dokument „Erläuterungen zu Spalten und Angabeoptionen“ einzusehen.</p>
Product Images for Life Cycle Assessment Dataset For Peritoneal Dialysis and Haemodialysis in Modena
<p>The database contains a collection of images showcasing the individual components of peritoneal dialysis (PD) products, along with their corresponding weights. These images serve as a visual record for life cycle assessment (LCA) purposes, focusing on the material composition and environmental impact of each product.</p> <ol> <li> <p><strong>Patient Education Materials</strong>: Photographs of educational materials provided to patients, with accompanying data on the weight of the paper and packaging.</p> </li> <li> <p><strong>Catheters and Surgical Kits</strong>: Images display the disassembled components of PD catheters and surgical kits, including tubing, connectors, and packaging. Each image is annotated with the precise weight of the individual components.</p> </li> <li> <p><strong>Dialysis Solution Bags</strong>: The database includes images of both CAPD and APD solution bags, separated into their constituent parts (e.g., plastic bag, solution, and protective wrapping), with weights noted for each component.</p> </li> <li> <p><strong>Connection Devices and Consumables</strong>: Detailed images of connection devices, clamps, and other consumable items, with individual component weights clearly labeled.</p> </li> <li> <p><strong>Packaging and Transport Materials</strong>: Photographs of transport packaging, such as cardboard boxes and plastic wraps, alongside recorded weights for each element.</p> </li> <li> <p><strong>Maintenance Items</strong>: Visuals of terminal catheter sets, cleaning agents, and related products, each accompanied by their respective weight data.</p> </li> <li> <p><strong>Disposal Components</strong>: Images of used solution bags, syringes, and other single-use items, separated into recyclable and non-recyclable components, with weights specified for each.</p> </li> </ol> <p>This image-based database provides a clear and comprehensive reference for the material breakdown and weight distribution of PD product components, essential for conducting a thorough LCA and identifying areas for environmental improvement.</p>
Historical and future water demand for households and industry for the STARS4Water river basins
<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> <p>The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050.</p> <p>The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file:<br>1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip for years 2000-2020<br>2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5)<br><br>The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in <em>Towards a global high </em><em>resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe</em>, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others. </p>
Federation of Vehicular Data in Smart Villages with Socioeconomic Information
<p>We present a dataset tailored for monitoring vehicle activity in a rural environment, specifically the Barranco de Poqueira region, covering the municipalities of Pampaneira, Bubión, and Capileira within the Sierra Nevada National Park, Granada, Spain. The dataset is generated by four Hikvision License Plate Recognition (LPR) cameras, capturing vehicle entries and exits in each village. To enrich the dataset, we include additional contextual details such as vacation calendars, vehicle origins, and socio-demographic information. Spanning from February 2022 to August 2023, the dataset is organized into three files: one with raw data directly from the cameras, another aggregated at the visit level with contextual information, and a third aggregated by vehicles with context details. With potential applications in mobility studies, urban planning, tourism, and socio-demographic analysis, the dataset is structured into three distinct files, encompassing a total of 43 different variables.</p> <p>The <strong>RAW_SMART_POQUEIRA.csv</strong> file contains information about 4 variables: num_plate_ID, camera_ID, date, and direction.</p> <p>The file <strong>VEHICLES_SMART_POQUEIRA.csv</strong> contains information about 33 variables: num_plate_ID, visit_time, distance, num_holiday, num_workday, num_high_season, num_low_season, entry_in_high_season, entry_in_holiday, nights, visits_dif_weeks, visits_dif_months, total_entries, avg_visit, std_visit, avg_nights, std_nights, avg_holiday, std_holiday, avg_workday, std_workday, avg_high_season, std_high_season, avg_low_season, std_low_season, route, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p> <p>The file <strong>VISITS_SMART_POQUEIRA.csv</strong> contains information about 26 variables: num_plate_ID, entry_cam, entry_date, entry_time, exit_cam, exit_date, exit_time, visit_time, route, distance, num_holiday, num_workday, num_high_season, num_low_season, nights, visits_dif_weeks, visits_dif_months, entry_in_holiday, entry_in_high_season, country, km_to_dest, population, avg_gross_income, avg_disposable_income, autonomous_community, and province.</p>
Hochfrequente Mental Health Surveillance
<p>Im Rahmen der Mental Health Surveillance (MHS) am Robert Koch-Institut (RKI) werden für eine Auswahl an Indikatoren der psychischen Gesundheit von Erwachsenen basierend auf Surveydaten Zeitreihen bestehend aus gleitenden Drei-Monats-Schätzern und Glättungskurven berechnet. Dadurch sollen Entwicklungen in der psychischen Gesundheit der erwachsenen Bevölkerung in Deutschland mit möglichst geringem Zeitverzug beobachtet und insbesondere negative Entwicklungen frühzeitig erkannt werden. Diese hochfrequente Surveillance wurde ursprünglich vor dem Hintergrund neuer Informationsbedarfe zur Entwicklung der psychischen Gesundheit der Bevölkerung in der COVID-19-Pandemie entwickelt.</p>
Dataset of "Marcus cross relation in the space of H-atom abstraction reactions boosted through off-diagonal thermodynamics"
<p>Proton-coupled electron transfer (PCET) and hydrogen-atom transfer (HAT) reactions play critical roles in biological processes and modern organic synthesis. The kinetics of these processes can align with the principles described in the renowned Marcus cross relation (MCR), a framework initially formulated to describe electron transfer mechanisms. The MCR provides an outstanding link between the kinetics of PCET/HAT reaction involving two distinct reactants and two related auxiliary self-exchange reactions – each between a molecule of one of the reactants and its coupled radical. In this study, we investigate the applicability and limitations of the canonical MCR across over 300 PCET and HAT reactions, providing a comprehensive theoretical analysis. Our findings reveal the need for an enhanced framework that incorporates ‘off-diagonal’ thermodynamic factors—asynchronicity and frustration. Of these factors, asynchronicity, which quantifies the imbalance between the proton vs. electron transfer components of the reaction, is identified as the dominant contributor to the improved predictive accuracy of the MCR. Notably, the incorporation of off-diagonal thermodynamics yields a more pronounced enhancement for HAT reactions than for PCET reactions. This advancement offers a refined theoretical basis for understanding H-atom abstraction mechanisms and underscores the importance of off-diagonal effects in PCET/HAT chemistry.</p>
Dataset of "Neutron imaging and molecular simulation of systems from methane and p‑xylene"
<p>The dataset contains parameterizations, and input files for molecular dynamics simulations used in the study of methane dissolution in p-xylene. For selected conditions, full simulation data, i.e., trajectories and energetics are provided. All used simulation results data are provided in the table, along with the measured experimental data.</p>
Data for The UNCOVER Survey: A First-Look HST+JWST Catalog of Galaxy Redshifts and Stellar Populations Properties Spanning 0.2 ≲ z ≲ 15
<p>The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster Abell 2744 to create the deepest view of our universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ~ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 < z < 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-beta priors on masses and star formation histories to produce joint redshift and stellar populations posteriors, and additionally lensing magnification is performed on-the-fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with NMAD ~ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of the Treasury survey, we publicly release the stellar populations catalog with this paper, derived from the photometric catalog adapting aperture sizes based on source profiles. This release includes posterior moments, maximum-likelihood spectra, star-formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.</p>
Ten years (2013-2023) of fish assemblage data collected seasonally with underwater visual surveys on paired artificial and natural reefs
<p>The study of assembly patterns and dynamics of organisms has long remained a foundational theme in ecology. Further, the relationship between assemblages and different habitats can provide important insight on ecological processes and guide management and conservation efforts (e.g., restoration, protected areas). We conducted underwater visual surveys of reef fish assemblages at 14 sites in the eastern Gulf of Mexico, including eight that were paired artificial and natural reefs. By using a paired design, we controlled biotic (e.g., larval supply), abiotic (e.g., depth), and socio variables (e.g., fishing access) to isolate the effect of reef type. Trained scientific SCUBA divers with extensive experience with reef fishes from the broader tropical western Atlantic region conducted two to four 10-minute stationary surveys on the paired reefs each season (i.e., calendar quarters) for 10 years from spring 2013 to spring 2023. We also surveyed six additional artificial reefs from winter 2020 to spring 2023 that lacked natural reef pairs. During each survey, the divers identified and estimated the total lengths of all taxa<strong> </strong>observed within an imaginary cylinder around them. The imaginary cylinders had a radius up to 7.5 meters (depending on horizontal visibility) and extended from the seafloor to the highest visible water above the diver. During the period of study, we conducted a total of 1,349 surveys and counted 544,736 fish that represented 171 taxa (most at the species level). Analyses of these data have revealed habitat-specific heterogeneity of the fish assemblages at both taxonomic and functional trait levels, the importance of herbivory in structuring the benthos, and socio-ecological interactions in the system, among other findings. These data may be useful for other researchers interested in patterns and dynamics of populations and communities, functional traits, taxa-habitat relationships, and for parameterizing statistical, joint distribution, metacommunity, and ecosystem models. In addition, because many of the observed taxa<strong> </strong>are of management concern, they may be useful for researchers interested in fisheries science. The data are free to use, are not copyright restricted, and we ask users to cite this data paper.</p>
Phytogeographic regions of Ukraine according to the "Flora Fungorum Ucrainicae"
<p>Origin of the data</p> <p>This regionalization was originally published by Heluta (1989), to illustrate the distribution of powdery mildew fungi across Ukraine, and further was used in the series "Flora Fungorum Ucrainicae", as well as individual publications and thesis in Mycology. The regionalization was based mainly on the current at that time Geobotanical zonation of the URSR (Barbarych et. al, 1977).<br>Since both names and accepted abbreviations of regions originally were in Russian, we adopted the translation made by Akulov et al. (2003), with some additions from a later publication by Prylutskyi & Chvikov (2020):<br>CF – Carpathian Forests, DGMS – Donetsk Gramineous-Meadow Steppe, FSCr – Forest-Steppe Crimea, KFS – Kharkiv Forest-Steppe, LFS – Left Bank Forest-Steppe, LGS – Left Bank Gramineous Steppe, LGMS – Left Bank GramineousMeadow Steppe, LP – Left Bank Polissya, MRF – Middle-Russian Forests, MCr – Mountain Crimea, PF – Precarpathian Forests, RF – Roztocze Forests, RFS – Right Bank Forest-Steppe, RGS – Right Bank Gramineous Steppe, RGMS – Right Bank Gramineous-Meadow Steppe, RP – Right Bank Polissya, SP – Small Polissya, SSCr – South Seaside of Crimea, SGMS – Starobilsk Gramineous-Meadow Steppe, SCr – Steppe Crimea, TR – Transcarpathia, VFS – Volyn Forest-Steppe, WFS – Western Forest-Steppe, WP – Western Polissya, WUF – West-Ukrainian Forests, WS – Wormwood Steppe.</p> <p><strong>UPD:</strong> Ukrainian names and abbreviations, as well as English names of the regions, updated according to <a href="https://ukrbotj.co.ua/archive/80/3/199" rel="nofollow">Heluta, 2023</a>.</p> <p>Dataset description</p> <p>Dataset (zip-archive) contains GIS vector layers with the polygons of regions, in the following formats: Geopackage, KML, and Esri shapefile. Polygons have been drawn manually using QGIS software, following verbal descriptions of the borders of regions from Heluta (1989).<br>CRS: EPSG:3857 - WGS 84 / Pseudo-Mercator<br>Charset Encoding: UTF-8</p> <p>Attribute table's fields descriptions</p> <p>fid - Unique identifier for each polygon<br>Name - Accepted abbreviated name for the region in Ukrainian<br>NameEng - Abbreviated name for the region, translated into English<br>NameFullUA - Full name of a region, in Ukrainian<br>NameFul - Full name of a region translated into English<br>NatZone - Natural zone according to the source (Heluta, 1989), in Ukrainian<br>Ecoregions - Name of the Terrestrial Ecoregion (TEOW) (Olson et al., 2001), which covers most of the area of a given region<br>Note: KML file has additional system fields, not contain attribute information.</p> <p>References</p> <p>Heluta, V.P. (2023) A critical revision of the powdery mildew fungi (Erysiphaceae, Ascomycota) of Ukraine: Erysiphe sect. Microsphaera. Ukrainian Botanical Journal. 2023. 80 (3). <a href="https://doi.org/10.15407/ukrbotj80.03.199" rel="nofollow">https://doi.org/10.15407/ukrbotj80.03.199</a></p> <p>Heluta, V.P. (1989) Powdery Mildews. Flora Fungorum Ucrainicae. Kyiv: Naukova dumka [In Russian: Гелюта, В.П. (1989) Флора грибов Украины: Мучнисторосяные грибы. Киев: Наукова думка]</p> <p>Barbarych, A.I. (Ed.) (1977)Geobotanical zonation of the URSR. Kyiv: Naukova Dumka [in Ukrainian: Геоботанічне районування Української РСР. Київ: Наукова думка]</p> <p>Akulov, O.Yu.; Usichenko, A.S.; Leontyev, D.V.; Yurchenko, E.O.; Prydiuk, M.P. (2003) Annotated checklist of aphyllophoroid fungi of Ukraine. Mycena 2:1–76.</p> <p>Chvikov, V.; Prylutskyi, О. (2020) Annotated checklist of Hygrophoraceae (Agaricales, Basidiomycota) of Ukraine. Biodivers. Ecol. Exp. Biol. 22, 6–23. https://doi.org/10.34142/2708-5848.2020.22.2.01</p> <p>Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'Amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.</p>
Triaxial accelerometer gait dataset: foot and lower back motion during normal and metronome walking
<p><strong>This dataset contains accelerometric data collected from young and older individuals walking in a controlled environment. The data were recorded using two triaxial accelerometers, one attached to the participant's lower back and the other attached to the foot. Participants were instructed to walk back and forth along a 205-meter corridor under two different conditions:</strong></p> <p><strong>Normal walking: </strong>Participants walked at their preferred walking speed, reflecting their natural gait and pace.</p> <p><strong>Metronome walking: </strong>Participants synchronized their walking pace to a metronome set to their preferred walking cadence. This condition introduced a rhythmic element to the walking pattern, allowing for the study of gait changes when adhering to an external tempo.</p> <p><strong>Another condition was also measured to introduce a more variable and dynamic walking pattern that reflects everyday pedestrian movement in a real-world context.</strong></p> <p><strong>Free outdoor walking:</strong> Older participants engaged in approximately 5 minutes of free walking in an urban environment, navigating city streets. During this activity, only the lumbar accelerometer was used to record data. </p>
Historical Animal Observation Records by Bavarian Forestry Offices (1845)
<p>In 1845, under the scientific direction of Andreas Wagner, the Bavarian government recorded the occurrence of 44 selected vertebrate species across the entire country. To this end, Wagner had a survey questionnaire sent to all 119 forestry offices in the state. The foresters' responses were now systematically recorded and analyzed for the first time. This data set represents the result of this survey. Among other things, it contains 5,467 geo-coded animal observation data.</p> <p>The data is the result of an interdisciplinary collaboration between scientists from the Chair of Computational Humanities at the University of Passau, the Directorate General of the Bavarian State Archives Munich, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, the Center for Biodiversity Informatics and Collection Data Integration at the Botanical Garden Berlin, and the NFDI4Biodiversity consortium.</p>
UNIC Templates for uploading corpus metadata v1.11
<p>The UNIC platform (https://unic.dipintra.it) accepts a JSON file for uploading corpus metadata based on the template here. Alternatively, use the spreadsheet template to input the corpus metadata and convert the resulting .xlsm file to JSON using this application at https://huggingface.co/spaces/nannanliu/UNIC_metadata_conversion. When opening the Excel spreadsheet template, please enable Macros, which will automatically validate your input in the columns. Please do not change the order of the columns because they are embedded with code. To add elements and components not included by the UNIC schema, create new columns after the existing ones.</p>
Dataset of "Preparation of novel lithiated high-entropy spinel type oxyhalides and their electrochemical performance in Li-ion batteries "
<p>Electrochemical measurements carried out using the 2032-coin cells with the Li-metal anode have shown voltammetric charge capacities of 450, 694, and 593 mAh g-1 for HEOFe, LiHEOFeCl, and LiHEOFeF, respectively.<br>Galvanostatic chronopotentiometry at 1 C rate confirmed high initial charge capacities for all the samples but galvanostatic curves exhibited a capacity decay over 100 charging/discharging cycles. Raman spectroelectrochemistry measured on the LiHEOFeF sample proved the reversibility of the electrochemical process for initial charging/discharging cycles. Electrochemical impedance spectroscopy revealed the lowest initial charge transfer resistance for LiHEOFeCl and its gradual decrease both for LiHEOFeCl and LiHEOFeF during galvanostatic cycling, whereas the charge transfer resistance of HEOFe slightly increases over 100 galvanostatic cycles due to different mechanism of the electrochemical reduction. </p>
Dataset on Weather-related disasters in agriculture in Italy - WDA
<h1><strong>Abstract</strong></h1> <p>The dataset is the supplementary material for the following journal paper:</p> <p>Pontrandolfi A, Alilla R, De Natale F, Nuti R, Parisse B, Pepe AG, Dataset on Weather-related Disasters in Agriculture (WDA) in Italy 2005–2021, Data in Brief <br><a href="https://doi.org/10.1016/j.dib.2025.111323">https://doi.org/10.1016/j.dib.2025.111323</a></p> <p>The database on Weather-related disasters in agriculture (WDA) is a part of the cloud storage which hosts the materials of the <a href="https://agrometeo.crea.gov.it/">Observatory for agricultural meteorology and climatology</a> of the Research Center for Agriculture and Environment belonging to the Council for Agricultural Research and Economics (CREA). The Observatory website has a specific section devoted to <a href="https://agrometeo.crea.gov.it/dati-e-analisi__trashed/rischio-meteorologico-in-agricoltura/">weather-related risk in agriculture</a>.</p> <p>A specific relational SQL database has been created fo data entry information from the official decrees of WDA declaration in Italy.</p> <p>From this relational SQL database, a <strong>dataset </strong>of WDA has been extracted for the period from 2005 to 2021 and here published</p> <p>The WDA dataset aims to make available useful data for weather-related risk assessment and analysis in the Italian agricultural sector.</p> <h2>Attached content:</h2> <ul> <li>pdf file "A_Description_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>csv file "DiscoveryMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> <li>xlsx file "StructuralMD_Dataset_Weather_related_disasters_agriculture_v1.2"</li> </ul>
Uses of Biblical Hebrew כִּי kī in Genesis, Judges, Samuel, and Ruth
<p>This is the data set accompanying chapter 4 of Staps (2024). It contains all occurrences of the Biblical Hebrew word כִּי <em>kī</em> in Genesis, Judges, Samuel, and Ruth.</p> <p>The file is a CSV file. The first line is a heading. Each other line describes a single use of כִּי <em>kī</em>. Fields are separated by a comma character (','). When the field value contains a comma, it is enclosed by double quotation marks ("…").</p> <p>The data is annotated with the following columns:</p> <ul> <li>Node: the number of the node for כִּי <em>kī</em> in the 2021 version of the ETCBC data set (Roorda et al. 2021). Only needed when importing the data programmatically.</li> <li>Construction: indicates the larger construction כִּי <em>kī</em> is part of, if any (e.g. כִּי אִם <em>kī ʾim</em>).</li> <li>Type: <ul> <li>adversative: 'but'.</li> <li>causal: gives the reason for a state or event. Includes reasons for doing or saying something, as well as explanations based on natural laws.</li> <li>causal-adversative: like causal, but also adversative ('not like this, because/but like that').</li> <li>complementizer: introduces an object or subject clause.</li> <li>concessive: 'though'.</li> <li>conditional: protasis 'if'.</li> <li>exceptive: כִּי אִם <em>kī ʾim</em> followed by a DP, translated 'except'.</li> <li>resultative: gives the result or consequence of an event or state.</li> <li>standalone: does not connect two clauses but marks some property of the clause itself.</li> <li>temporal: protasis 'when(ever)', or in the past 'when'.</li> <li>-: excluded (e.g. because of poetry, repetition, or textual emendation; see Notes).</li> </ul> </li> <li>CompPred: gives the predicate for cases of Type “complementizer” (usually a verb).</li> <li>CommonGround: indicates whether the information in the כִּי <em>kī</em>-clause is in the Common Ground. In narrative portions the Common Ground is between author and reader. In quotations it is between Speaker and Addressee. Values can be: <ul> <li>no</li> <li>yes</li> <li>accommodated: the information is not in the Common Ground yet, but can be easily accommodated by the Addressee. For example, it may be not that relevant, or it is already partially CG, or it can be deduced from the rest of the CG. Typically not at-issue.</li> <li>imposed: the Speaker imposes the information on the Common Ground, even though it is new, to achieve some discursive effect.</li> </ul> </li> <li>BasedOn: the verse(s) the CommonGround categorization is based on.</li> <li>Notes: explanation for the choices made, cross-references, and other notes.</li> </ul> <p>Staps (2024) is based on v1 of this data set (https://doi.org/10.5281/zenodo.8314818).</p>
Invasion Biology WikiProject Scientific Papers: Text Data Mining and LLM-based Information Extraction of Species, Locations, Habitats, and Ecosystems
<p>This dataset contains the abstract and full-text for publication DOIs from the Invasion Biology WikiProject (DOI: <a href="https://www.doi.org/10.5281/zenodo.12518036">10.5281/zenodo.12518036</a>). The data was retrieved using the <a href="https://ask.orkg.org/">ask.orkg.org</a> <a href="https://api.ask.orkg.org/docs#tag/Semantic-Neural-Search/operation/explore_documents_index_explore_get">API</a>. For the <a href="https://github.com/jd-coderepos/invasion-biology-IE/blob/main/scripts/ask-doi-list-fulltext-search.py">script</a> used to obtain the data, refer to the accompanying GitHub repository: <a href="https://github.com/jd-coderepos/invasion-biology-IE/" target="_blank" rel="noopener">https://github.com/jd-coderepos/invasion-biology-IE/</a>.</p> <p>The resulting CSV file includes the following fields: <code>"ASK ID"</code>, <code>"DOI"</code>, <code>"Title"</code>, <code>"Abstract"</code>, and <code>"Full-text"</code>.</p> <p>Of the 49,438 queried DOIs, the ASK database provided:</p> <ul> <li><strong>Total DOIs processed:</strong> 12,636</li> <li><strong>DOIs with neither abstract nor full-text:</strong> 36 (abstract token count was less than 10)</li> <li><strong>DOIs with abstracts but no full-text:</strong> 12,636</li> <li><strong>DOIs with both abstract and full-text:</strong> 2,834</li> </ul> <p>The second part of the dataset contains structured information extracted from the publications using the GPT-4o Large Language Model. This structured data is included in the zipped folder <code>structured-publications.zip</code>.</p> <p>The accompanying GitHub repository provides access to the code and scripts used at various stages of the information extraction (IE) process.</p> <p><strong>Theme of the Study:</strong><br>"Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models."</p>
Catalog of NE Italy earthquakes Mw with related velocimetric time series
<p>Mw catalog (xlsx format) of earthquakes occurred in Norheastern Italy from 2016 to 2023; the catalog reports estimations for:</p> <ul> <li>ML (Bragato and Tento, 2005);</li> <li>Mw calculated from SA (Moratto et al., 2017);</li> <li>Mw calculated from MT (Moment Tensor; Saraò et al., 2021);</li> <li>The tgz file with the corrected velocimetric waveforms (SAC fomat with P and S arrival times used for the locations and units in m/s); tgz file can be found in Waveforms.tgz. EVDP SAC header is expressed in meters.</li> </ul> <p>Continuous raw time series can be dowloaded from Oasis website (Priolo et al., 2015).</p> <p> </p>
Cuneiform Inscriptions Geographical Site Index (CIGS)
<p>The <em>Cuneiform Inscriptions Geographial Site</em> (CIGS) index contains a basic set of primary spatial, toponym, attribute, and external link information on close to 600 archaeological locations where texts written in cuneiform and derived scripts have been found. 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. This resource has been prepared by researchers of the Department of Linguistics and Philology of Uppsala University. 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">rune.rattenborg@lingfil.uu.se</a>). For further details, see <a href="https://cdli.ucla.edu/pubs/cdlj/2021/cdlj2021_001.html">Rattenborg et al. 2021</a>.</p> <p>The version 1.7 index contains 598 entries with a total twenty-six fields, including one primary ID, one integer field for accuracy, twenty-two string fields with toponyms and links, and two spatial data fields. Coordinates given use the WGS 1984 geographic coordinate reference system (<a href="https://epsg.io/4326">EPSG 4326</a>) and have been truncated to four decimal digits. Site locations have been traced from archaeological gazetteers and web mapping services (e.g. <a href="https://pleiades.stoa.org/">Pleiades</a>, <a href="https://www.geonames.org/">GeoNames</a> and <a href="https://www.openstreetmap.org/">OpenStreetMap</a>) and digitally generated from optical recognition using current and legacy satellite imagery datasets in QGIS 3.x.</p> <p>The version 1.7 data set is updated to correlate with archaeological locations included in the <a href="https://cdli.mpiwg-berlin.mpg.de">Cuneiform Digital Library Initiative</a> table of proveniences (see <a href="https://cdli.mpiwg-berlin.mpg.de/proveniences">https://cdli.mpiwg-berlin.mpg.de/proveniences</a>), migrated July 2023. Please see this resource for later updates to individual records.</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
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DANDI Archive for NWB datasets
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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.