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22,922 results for “collections as data”
Fig. 7 in "Something old, something new, something borrowed, and the oioxeny is true": description of Plectanocotyle jeanloujustinei n. sp. (Polyopisthocotylea, Plectanocotylidae) from the MNHN Helminthology collection with novel molecular and morphological data for P. gurnardi (Van Beneden & Hesse, 1863) (sensu stricto) from Sweden
Fig. 7. Plectanocotyle gurnardi (Van Beneden and Hesse, 1863) sensu stricto ex Eutrigla gurnardus from the North Sea, Sweden, disposition of clamps sclerites. A, Dorsal jaw. B, Ventral jaw. C, Clamp, ventral view (SMNH 216593). 3.2. Morphology
High-rate GNSS data collected during shake table experiment
<p>The dataset contains high-rate GNSS data collected during shake table experiment. A displacement-controlled experiment was designed and carried out at the UWM Olsztyn campus. The in-house developed shake table provided artificial dynamic displacements. The device induced forwardbackward uniaxial motion with dedicated speed and range, simulating a sin-wave displacement with constant frequency and amplitude. We have induced a few ~50-s long harmonic motions in the E-W direction of frequency in the 1-4 Hz range and amplitudes of 5-15 mm. A whole data collection period lasted approximately 1 hour, also including initial and inter excitations’ static periods.</p>
Data Workbook - Ex-Situ Geoheritage Case Study: Quantitative and Qualitative Analysis of the Uppsala University Museum of Evolution Collections
<p>Data Workbook for Thesis.</p> <p>Ex-Situ Geoheritage Case Study: Quantitative and Qualitative Analysis of the Uppsala University Museum of Evolution Collections. </p> <p>Includes; Images, Conservation Results, Inventory, Valuation Grades, RStudio Results</p>
Educational data collected from parents - regarding the analysis of online activities in schools in Romania (during the Covid-19 pandemic, March 2020 - April 2020)
<p>The responses of the 784 parents were collected through the questionnaire available at: <a href="https://forms.gle/Km8WE5QamrYYgXJi7" target="_new" rel="noopener"><strong>https://forms.gle/Km8WE5QamrYYgXJi7</strong></a></p> <p>It was designed with various types of responses, including binomial (yes/no), polynomial (multiple options), and open-ended responses, to capture a comprehensive range of data. This combined approach allows for both quantitative analysis of fixed-response questions and qualitative insights from open-ended questions. Patterns, correlations, and differences between various demographic groups and their experiences and attitudes toward online education can be identified.</p> <p>To protect the identity of the respondents and to obtain accurate responses, all data collected from teachers was anonymous. We did not collect any personal information whatsoever. This aspect was made clear to the respondents in the description of the questionnaire.</p>
Compilation of data collected in surveys on the WissKI-based 3D Repository with DFG 3D-Viewer project partners and architecture students from the Warsaw University of Technology and the Technical University of Łódź
<p>The dataset contain the compiliation of responses from users of WissKI-based 3D Repository (https://3d-repository.hs-mainz.de/)., which is the open platform for deposit of 3D models of cultural heritage. The beta version of the WissKI 3D Repository, initiated in June 2022, has been subjected to evaluation by two primary target groups since its launch. The initial group, composed of students in the cultural heritage domain, was tasked with showcasing the importance of documenting and publishing 3D models of digital reconstructions. The survey with sutdents was conducted for three different classes: </p> <p>1) In summer 2022 with bachelor architectrue students at Warsaw University of Technology during seminar of choice regarding digital reconstruction of wooden synagogues;</p> <p>2) In summer 2023 with bachelor architectrue students at Warsaw University of Technology, and master students from Technology University of Łódź during seminar of choice regarding digital reconstruction of wooden synagogues;</p> <p>3) In autumn 2023 during international workshop about digital 3D heritage of CoVHer project with studnets of architecture from Warsaw Univeristy of Technology, Alma Mater Studiorum – Universita di Bologna, Facoltà di Architettura di Porto and Hochschule Mainz - University of Applied Sciences, as well as archaeology studnets from Universitat Autònoma de Barcelona.</p> <p>The second group, comprising digital 3D cultural heritage professionals, predominantly focused on archiving digital assets. Participants were project partners of DFG 3D Viewer project, which were professionals from the Institute of Archaeology at University Cologne, the Institute of Art History at the Ludwig-Maximilians-Universität Munich, the Architecture, Civil Engineering and Urban Planning Department of BTU Cottbus Senftenberg, and the Detushce Museum. They were asked for evaluaton of system after three differetn stages of work: at the begging wihtout any introduction to the system, after proivision of intorudctionary materilas and finally at the end of work.</p> <p>All participants were requested to report their experiences across four categories: metadata form, 3D viewer, provided guidelines, and overall experience. A 5-point rating scale was employed to assess specific issues, with 1 being the most negative and 5 being the most positive. The form length question was an exception, where a median value of 3 was considered ideal, and extreme values indicated either excessive length or brevity.</p>
Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections? - Data Collected and Presented for the Experiments
<p>This dataset contains images collected (and processed) for the experiments of Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?" journal article, a work that defines a new path planning problem for UAVs - the Fast Inspection of Scattered Regions (FISR) - and introduces a novel method that deals with this problem - the multi-UAV Disjoint Areas Inspection (mUDAI) method. For the validation of the introduced methodology, two sets of real-world experiments were executed, one small-scale in Galatsi, Athens, were two mUDAI missions were depolyed, with two different optimization objectives for the data collection procedure (Mazimized Coverage Objective - MCO, and Balanced Coverage Objective - BCO), and one large scale in ZEP-Kissos, Thessaloniki, where a Coverage Path Planning (CPP) mission, and 2 mUDAI missions, one with a single and one with two UAVs, using both the MCO criterion for the data collection, were deployed. Regarding the CPP mission, both the collected images, and the processed results (to generate 2D, 3D, elevation, and plant health maps) are included.</p> <p>In this <a title="mUDAI - ChoosePath platform guide" href="https://sites.google.com/view/mudai-platform/" target="_blank" rel="noopener">page</a> you can find a guide for the on-line platform hosting demo instances of the algorithms used for the deployment of all experiments.</p> <p>In case you use this data, please cite the article:<br>(Article under review - more information to be included soon)</p>
Physiological Data Collected from smartwatch: EDA, Pulse Rate, and Skin Temperature for Stress and Fatigue Analysis
<p>The dataset contains multiple columns capturing both <strong>physiological and demographic data</strong>.<strong> Physiological data</strong>, collected using the <strong>Empatica EmbracePlus smartwatch,</strong> includes electrodermal activity (EDA), pulse rate, and skin temperature. These metrics provide insights into participants' stress and fatigue levels. Empatica's proprietary algorithms preprocess the raw data, extracting digital biomarkers and metrics that reflect the wearer's physiological and behavioral states. <strong>The processed data is aggregated on a per-minute basis.</strong></p> <p>Demographic information, such as age, gender, fitness level, and sleep duration from the previous night, is also included. Additionally, participants rated their perceived physical fatigue on the Borg scale (ranging from 6 to 20), offering a subjective measure of exertion during or after physical tasks.</p> <p>The dataset was collected during controlled simulations of industrial tasks in a fitness environment. These simulations involved repetitive activities, including weightlifting, resistance band exercises, and isometric tasks, designed to mimic the physical demands of industrial work. This approach allowed for the safe and effective study of physical fatigue. The resulting data provides valuable insights into the physiological responses associated with repetitive physical labor.</p>
Linked collectors and determiners for: Collections and observation data National Museum of Natural History Luxembourg.
Natural history specimen data linked to collectors and determiners held within, "Collections and observation data National Museum of Natural History Luxembourg". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/962f59bc-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/962f59bc-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/962f59bc-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/962f59bc-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence Data of Small Carnivores (Mammalia, Carnivora) in the National Museums of Kenya Zoology Collection.
Natural history specimen data linked to collectors and determiners held within, "Occurrence Data of Small Carnivores (Mammalia, Carnivora) in the National Museums of Kenya Zoology Collection". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/829ed5f2-eae7-4c81-98c1-6cb77ef0a17c">https://bionomia.net/dataset/829ed5f2-eae7-4c81-98c1-6cb77ef0a17c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/829ed5f2-eae7-4c81-98c1-6cb77ef0a17c">https://gbif.org/dataset/829ed5f2-eae7-4c81-98c1-6cb77ef0a17c</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Iberian Odonata distribution: data of the BOS Arthropod Collection (Univ. Oviedo, Spain).
Natural history specimen data linked to collectors and determiners held within, "Iberian Odonata distribution: data of the BOS Arthropod Collection (Univ. Oviedo, Spain)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7e31baf8-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/7e31baf8-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7e31baf8-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/7e31baf8-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence Data of Vascular Plants collected or compiled for the Flora of Bavaria.
Natural history specimen data linked to collectors and determiners held within, "Occurrence Data of Vascular Plants collected or compiled for the Flora of Bavaria". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/64dabd3c-4f34-4520-b9dd-d227a0bf1582">https://bionomia.net/dataset/64dabd3c-4f34-4520-b9dd-d227a0bf1582</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/64dabd3c-4f34-4520-b9dd-d227a0bf1582">https://gbif.org/dataset/64dabd3c-4f34-4520-b9dd-d227a0bf1582</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence data of Elateridae housed in the natural history museum collection in Nairobi, Kenya.
Natural history specimen data linked to collectors and determiners held within, "Occurrence data of Elateridae housed in the natural history museum collection in Nairobi, Kenya". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988">https://bionomia.net/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988">https://gbif.org/dataset/b8827d23-dc43-4022-b52e-c1f6eda0a988</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: National Museum of Namibia Scorpiones Collection Data 2022.
Natural history specimen data linked to collectors and determiners held within, "National Museum of Namibia Scorpiones Collection Data 2022". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7a5d39ef-1abc-442f-be56-5ccf3b27aedd">https://bionomia.net/dataset/7a5d39ef-1abc-442f-be56-5ccf3b27aedd</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7a5d39ef-1abc-442f-be56-5ccf3b27aedd">https://gbif.org/dataset/7a5d39ef-1abc-442f-be56-5ccf3b27aedd</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Mollusca collected by Agassiz trawl from the 2016 SO-AntEco Expedition to the South Orkney Islands, Antarctica - data.
Natural history specimen data linked to collectors and determiners held within, "Mollusca collected by Agassiz trawl from the 2016 SO-AntEco Expedition to the South Orkney Islands, Antarctica - data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/60f0304b-2b55-41c5-b23d-b3df565a2389">https://bionomia.net/dataset/60f0304b-2b55-41c5-b23d-b3df565a2389</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/60f0304b-2b55-41c5-b23d-b3df565a2389">https://gbif.org/dataset/60f0304b-2b55-41c5-b23d-b3df565a2389</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Updates to the checklist of the wild bee fauna of Luxembourg as inferred from revised natural history collection data and fieldwork.
Natural history specimen data linked to collectors and determiners held within, "Updates to the checklist of the wild bee fauna of Luxembourg as inferred from revised natural history collection data and fieldwork". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3f254e70-d5a0-4871-8c28-30a2004db17d">https://bionomia.net/dataset/3f254e70-d5a0-4871-8c28-30a2004db17d</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3f254e70-d5a0-4871-8c28-30a2004db17d">https://gbif.org/dataset/3f254e70-d5a0-4871-8c28-30a2004db17d</a>. Formatted as a Frictionless Data package.
Digital Collections Data and Tracking Disease Workshop 2: Introduction and Museum Perspectives Talks
<p>Recordings of talks by speakers in the Museum Perspectives section of our Digital Collections Data and Tracking Disease Workshop 2 at the American Society of Mammalogists Meeting in Boulder, Colorado in June of 2024.</p> <ul> <li><strong>Topic -- A Museum Perspective</strong> <ul> <li><strong>Museums and Emerging Pathogens in the Americas</strong>, Joe Cook, University of New Mexico<strong><br></strong></li> <li><strong>Museums and pathogens: Challenges, potential, and management</strong>, <a href="https://orcid.org/0000-0002-6931-6420" target="_blank" rel="noopener">Adam Ferguson</a>, Field Museum of Natural History</li> <li><strong>The UX Side of Extended Specimen Databases</strong>, <a href="https://orcid.org/0000-0002-8775-7254" target="_blank" rel="noopener">Kelly Speer</a></li> <li><strong>Preventing the next viral pandemic via elucidation of mammalian reservoirs</strong>, Rick White, University of North Carolina-Charlotte</li> <li><strong>Scientific collections and public health surveillance: institutional weaknesses and perspectives from Uruguay,</strong> <a href="https://orcid.org/0000-0002-4055-9277" target="_blank" rel="noopener">Germán Botto Nuñez</a>, Universidad de la República <ul> <li>(note talk sound did not work for this presentation)</li> </ul> </li> <li><strong>Building integrated archives for parasite and pathogen discovery</strong><br><a href="https://orcid.org/0000-0002-8065-0833" target="_blank" rel="noopener">Kurt Galbreath</a>, Northern Michigan University</li> <li><strong>The Dark Side of the Light: Fireflies, Snails, and Viromes</strong>, <a href="https://orcid.org/0000-0001-5067-3316" target="_blank" rel="noopener">Oliver Keller</a>, University of Michigan</li> </ul> </li> </ul>
Data for the manuscript: "Self-organization of collective escape in pigeon flocks"
<p>This repository contains all data (empirical and simulated) used and generated for the paper "Self-organization of collective escape in pigeon flocks" (2022) <em>PLoS Comput Biol 18(1): e1009772. <a href="https://doi.org/10.1371/journal.pcbi.1009772">https://doi.org/10.1371/journal.pcbi.1009772</a></em>. More information can be found in the README file and the connected GitHub repository: https://github.com/marinapapa/SelfOrg-ColEsc-Pigeons/</p>
Terretrial LiDAR data collected from St Pancras Old Church, Camden, UK
<p>Terrestrial LiDAR data collected by the team at University College London.</p><p>This is Version 2 containing data processed into 10 m x 10 m tiles, this has also been filtered to remove high "deviation" points.</p><p>Data is in .ply format containing xyz fields as well as reflectance, deviation, range, return number and scan position<\p></p><p><b>UCL project name</b>: 2017-07-18.001.riproject</p><p><b>Plot ID</b>: STP</p><p><b>State or region</b>: Camden</p><p><b>Date project started</b>: 7/18/2017</p><p><b>Area scanned</b>: 25,392 m2</p><p><b>Instrument</b>: UCL RIEGL VZ-400</p><p><b>Scan pattern</b>: 19 positions</p><p><b>Angular resolution</b>: 0.04</p><p><b>Images captured</b>: No</p><p><b>Links to media</b>: </p><p><b>Number of scans</b>: 38</p><p><b>Google Maps URL</b>: https://www.google.com/maps/place/The+Hardy+Tree/@51.5348275,-0.1302261,19.07z/data=!4m5!3m4!1s0x48761b22ae4a50ff:0x5ee5e6d9819cb888!8m2!3d51.5351276!4d-0.1297699</p><p><b>Publications</b>: https://doi.org/10.1186/s13021-018-0098-0, https://doi.org/10.1016/j.rse.2020.112102</p><p>For more information on the methods used to capture TLS data please refer to <a href="https://doi.org/10.1016/j.rse.2017.04.030">Wilkes et al. 2017</a></p><p>Please acknowldege the producers of this data set if using this data for publication.</p>
Research project on field data collection for honey bee colony model evaluation - datasets
<p><strong>Description of the datasets</strong></p> <p>The file 00_MUSTB_field_data_model.docx contains the data model according to which the data collected in the context of the MUSTB field data collection were reported to EFSA. The current data model description includes some modifications with respect to the specifications published before the beginning of the project (EFSA, 2017, https://doi.org/10.2903/sp.efsa.2017.EN-1234). All the tables included in the data model are published here in csv format. The underlying schemas are also published in xsd format.</p> <p>Sites: General information about the sites where the data collection took place;</p> <p>Polygons: General information about the polygons where the botanical survey took place.</p> <p>Table I: Pesticide application, reporting data on experimental spraying events;</p> <p>Table II: Resource providing unit and landscape fitness, reporting data on abundance of flowering plants in polygons mostly within 1.5 km, but in some cases up to 3 km of the experimental colony;</p> <p>Table III: Master list of all hives included in the study;</p> <p>Table IV: Colony management, reporting the log of the beekeeper regarding input (if material was added to the hive: e.g. empty frames, chemicals for varroa treatment, sugar), output (if material was removed from the hive, e.g. honey combs, supers), queen loss, swarming, or clinical signs observed in the experimental hives;</p> <p>Table V: Hive inspection, reporting data on in-hive measurements in the experimental colonies. This table contained several types of data, including:</p> <ul> <li>Data on brood development and food provision (“cell utilization”) obtained from image analysis of combs;</li> <li>Data on forager activity obtained from automatic video recordings and image analysis by a bee counter;</li> <li>Data on hive weight obtained from automatic logging by a hive scale;</li> <li>Data on adult bee strength, obtained by weight assessment of combs with and without adult bees (“bees per comb data”);</li> </ul> <p>Table VI: SSD2, reporting data on results of laboratory analyses of pollen, pesticide residues and parasites/pathogens. These four types of laboratory analyses involved different methods, and were reported according to different standards. Therefore, a number of the fields in the technical specifications for the SSD2 table (EFSA, 2017) were not applicable for records reporting results of some analyses, in particular palynological, parasite and pathogen analyses. These fields were left empty;</p> <p>Table VII: Colony observation, reporting observations of honey bee waggle dances from observation hives. Orientation denotes the angle of the waggling phase relative to the vertical axis on the comb. Direction denotes the actual direction in the landscape, as calculated from the orientation of the waggle dance.</p> <p>In all the csv files, columns with the suffix "_desc" have been included, where relevant, to include the name corresponding to the EFSA controlled terminology used in the previous column (e.g. resUnit contains EFSA term codes while resUnit_desc contains the term names).</p> <p><strong>Data storage</strong></p> <p>All data collected during the project was stored in a relational database. The database was developed in .NET Entity Framework Core, ran on a PostgreSQL, and was hosted by Amazon Web Service during the whole duration of the project development. Data could be imported or entered manually in the database through a web form. Administrators could create new users and administrators, new sites, and new colonies, i.e., administrators were allowed to enter or change data of all tables. Users were allowed to enter data, and could view, retrieve, and modify their own data of all tables, except for Table III (description of experimental colonies). Administrators could view and retrieve all data. Data was retrieved in CSV and XML formats, and were structured to secure a smooth transmission of data to the Data Collection Framework of EFSA. Furthermore, data flow from the field data collection to the development of ApisRAM was secured by direct communication between the field and modelling teams.</p> <p> </p> <p><strong>Version 2</strong> contains the UTM coordinates in tables Sites, Polygons and Resource providing unit.</p>
Codebook for the analysis of focus group interview data collected as part of the DETECT project.
<p>This is the codebook created for the analysis of focus group interview data collected as part of the <a href="http://detectproject.eu/">DETECT project</a>. In particular, nine interviews were conducted with primary and secondary school teachers in Finland, Italy, Spain and the UK in order to explore their perceptions of critical digital literacies and how these are manifested in their practices. The interviews were organised and conducted by the researchers in the respective local HEIs between February-June 2020. A total of 7 focus-groups interviews took place with a total number of 39 participating teachers (7 from Finland, 6 from the UK, 9 from Spain and 17 from Italy). The interviews were conducted both face to face (schools in Spain) and online (schools in Finland, Italy and the UK) due to pandemic-related restrictions imposed shortly after the start of the data collection period.</p> <p>The collected data were read and segmented and all interview excerpts relating to the Critical Digital Literacies framework sub-dimensions were marked and chosen for the analysis. The total number of marked segments was 666 in all nine focus group interviews. To ensure the reliability of the analyses, the researchers translated and collected a representative sample of codings (n=117 out of 666) for each sub-dimensions from each school and these were examined by all researchers in several consensus meetings and the final criteria for each sub-category were constructed together based on those discussions.</p> <p>This dataset accompanies the intellectual outputs of the DETECT project, including amongst others this report:</p> <p>Gouseti, A., Bruni, I., Ilomäki, L., Lakkala, M., Mundy, D., Raffaghelli, J., Ranieri, M., Roffi, A., Romero, M. and Romeu, T. (2021) <em>Schools’ perceptions and experiences of critical digital literacies across four European countries - DETECT Report 2. </em>Accessed at: <a href="http://doi.org/10.5281/zenodo.5070394">http://doi.org/10.5281/zenodo.5070394</a></p> <p><strong>Acknowledgement</strong></p> <p>This project was funded by Erasmus+, KA2. Project Reference: 2019-1-UK01-KA201-061508.</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.