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Withdrawn: DZD Core Data Set - first Version published at DZD Website for internal use (obsoleted by DOI 10.21961/mdm:45923)
<p>The German Center for Diabetes Research (DZD) conducts large clinical multicenter studies in the field of diabetes and metabolic research. In this vein, a core data set (CDS) which contains a list of clinical parameters relevant for joint studies in diabetes research was established in 2021 and published for internal use at the DZD website (https://www.dzd-ev.de/en/). In 2022 a FAIRified version of the data set was published at MDM portal (https://medical-data-models.org/). This entry shows the very first version of the core data set, published as an excel file before the FAIRification. It is intended as a supplement for an article about the FAIRification process: "The Journey to a FAIR CORE DATA SET for Diabetes Research in Germany"</p>
Data set for: Thema and world needs
<p>In essence, bibliodiversity describes how different communities handle knowledge creation and dissemination. Consequently, the information needs of these communities will also vary. In this article, we will investigate if evidence for this can be found by looking at much downloaded open access books in 100 countries. The subjects of these books are described by the Thema classification, which aims to be global in scope. A clustering algorithm is deployed to find patterns in the combination of classification codes and countries. This will allow us to determine whether the residents of the same region also share an interest in the same topics.</p> <p>When looking at global information needs and open access books, it is obvious that these titles will not just be written in English. The set of books used in this investigation contains text in twenty different languages. The subject classification is however language independent, which allows us to group all of them based on shared subjects. In the next section, we will further explore the Thema classification and bibliodiversity.</p>
Data set in the form a relational database (sql) to denote a network of service providers, service clients and recommenders
<p>This data-set pertains to a network (i.e. graph) represented in the form of a relational data-base of service providers (nodes), service clients (nodes), service recommenders (nodes) and relationaships between then (i.e. a client used a provider, a recommender recommended a service to another client), along with some initial values of the QoS level perceived by any client whi have used a service and the reputation of a recommender. The data-set can be used for developing a reputation-based trust system. </p>
SGComp data sets
<p>Training data set for an expert finding system (SGComp_Train.csv), and Benchmarking data set for expert finding systems with adversarial data (SGComp_Adversarial.csv).</p> <p><br>Both data sets have the same field structure as relation triples (head: experts' full name, relation: relationship with key phrases, list of tail: []) in comma-separated values (.CSV) </p>
Supplementary File 8; The full data set used for the analysis presented herein
Open the record for dataset details and reuse information.
STILT footprints data set 2
<p>This repository contains the second batch of training data sets of measurement footprints.</p> <p>The footprints are used to train the deep learning model presented in our paper titled "FootNet v1.0: Development of a machine learning emulator of atmospheric transport".</p> <p>Preprint of the manuscript could be accessed at https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1526/.</p> <p>The footprints are provided in Numpy compressed array format, which could be decompressed with Python 3.10.6 and NumPy 1.23.4.</p>
Data set for "SARS-CoV-2 introductions to the island of Ireland: a phylogenetic and geospatiotemporal study of infection dynamics"
<p>Please see README.txt for detailed information about the contents of this data repository.</p>
PREPCLIM sample data set for publication in "Geoscientific Model Development" 2024
<p>Data set illustrating the functionality of software developed in the PREPCLIM project and used in the proposed paper:</p> <p>A Modeling System for Identification of Maize Ideotypes, optimal sowing dates and nitrogen<br>fertilization under climate change – PREPCLIM-v1</p> <p>https://doi.org/10.5194/gmd-2024-105<br>Preprint. Discussion started: 11 July 2024<br>c Author(s) 2024. CC BY 4.0 License.</p>
Data Set for "Analyzing Microbial Growth with R"
<p>Sample data set used in "<a href="http://bconnelly.net/2014/04/analyzing-microbial-growth-with-r/">Analyzing Microbial Growth with R</a>"</p>
Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year.
<p>Full Data Set of 16814 tweets from 17 different Twitter accounts of Catalonian Public Servants and Delegates collected between August 19 2013 and September 17 2017 in both original language and translated into Spanish later codified under para-diplomacy or nation branding macro-categories. Includes results for each year out of a significant sample of 1638 tweets.</p>
A multimodal data-set of a unidirectional glass fibre reinforced polymer composite
<p>Please cite the following article when using the data-sets hereby shared:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. A multimodal data-set of a unidirectional glass fibre reinforced polymer composite. <em>Data in brief</em>, <em>18</em>, pp.1388-1393.</p> <p>These data-sets were used for validating the use of X-ray tomography and our dictionary-based probabilistic method for detection of individual fibres, for more information see the following article:</p> <p>Emerson, M.J., Dahl, V.A., Conradsen, K., Mikkelsen, L.P. and Dahl, A.B., 2018. Statistical validation of individual fibre segmentation from tomograms and microscopy. <em>Composites Science and Technology</em>, <em>160</em>, pp.208-215.</p>
Data set for: Proposal for a micromagnetic standard problem for materials with Dzyaloshinskii-Moriya interaction
<p>Scripts and Jupyter notebooks for the data reproduction of the paper: "Proposal for a micromagnetic standard problem for materials with Dzyaloshinskii-Moriya interaction"</p> <p>Files include notebooks and scripts with simulations using the OOMMF, MuMax3 and Fidimag codes. Data analysis is performed using Python tools such as Numpy and Scipy.</p> <p>The latest version of this data set can be found in the Github repository:</p> <p><a href="https://github.com/fangohr/paper-supplement-standard-problem-dmi">https://github.com/fangohr/paper-supplement-standard-problem-dmi</a></p>
Data set for "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction"
<p>Scripts for the micromagnetic simulations of the publication "Chiral Magnonic Crystals: Unconventional Spin-Wave Phenomena Induced by a Periodic Dzyaloshinskii-Moriya Interaction", using the OOMMF software. These codes reproduce the result of magnonic waveguides with periodic Dzyaloshinskii-Moriya interactions. A Dockerfile and a Makefile are included for the reproducibility of the results.</p> <p>The repository containing these results, together with a explanatory README document can be found in:</p> <p>https://github.com/davidcortesortuno/paper-2018-chiral_magnonic_crystals</p> <p> </p> <p>The files included in this Zenodo release refer to the v1.0 version of the data set. For an updated version of the scripts refer to the Github repository.</p>
X-ray Holographic Data Set of 15 μm Spheres obtained at Petra III, P10, GINIX at DESY, Hamburg
<p>This is the data set presented in the article "<strong><em>Phase Retrieval for Near-field X-Ray Imaging beyond Linearisation or Compact Support</em></strong>".</p> <p>It is a matlab file containing 3 fields:</p> <p>geometry contains the following fields: all distances z_01 (focus-sample) z_02(sample-detector) and derived quantites M (magnification), z_eff (effective propagation distance from Fresnel scaling), F (Fresnel number) and dxeff (effective pixelsize)</p> <p>images is a cell array of the 4 flat field corrected (not aligned nor magnified) measurements.</p> <p>p is a parameter structure. p.measurements contains the fully corrected data used for the reconstruction. p.F contains the rescaled Fresnel numbers - use these for reconstruction, corresponding to the order of measurements. </p>
Bubble and ppt data set at CIEM large scale wave flume
<p>The present work was developed in work of an internal UPC project. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona.</p> <p>The data set aims to evaluate the false reading of Optical Backscatter Sensors produced by air bubbles at breaking conditions. In order to do that a set of different wave conditions (wave height ranging from 0.2 up to 0.85 and period from 2 to 7 s) have been tested at two different Still water conditions (2.5 and 2.65) with a barred profile in the wave flume.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p>
Data set Interoceptive Accuracy Scores from the Heartbeat Counting Task
<p>Interoception, the capacity to perceive internal bodily states, is thought to influence cognitive, affective and interpersonal functioning. It is frequently assessed using the heartbeat counting task, introduced recently in interoceptive research. In this task participants are requested to count their heartbeats without relying on external cues. Interoceptive Accuracy (i.e., IAcc) scores are then computed based on absolute comparisons between actual and reported heartbeats. We uploaded here a large data set that includes the reported and actual number of heartbeats of N = 572 participants who performed the heartbeat counting task for three time intervals of 25s, 35s, and 45s. </p>
Condition monitoring of hydraulic systems Data Set at ZeMA
<p><strong>Abstract:</strong></p> <p>The data set addresses the condition assessment of a hydraulic test rig based on multi sensor data. Four fault types are superimposed with several severity grades impeding selective quantification.</p> <p> </p> <p><strong>Source:</strong></p> <p>Creator: ZeMA gGmbH, Eschberger Weg 46, 66121 Saarbrücken<br> Contact: t.schneider <strong>'@'</strong> zema.de, s.klein <strong>'@'</strong> zema.de, m.bastuck <strong>'@'</strong> lmt.uni-saarland.de, info <strong>'@'</strong> lmt.uni-saarland.de</p> <p> </p> <p><strong>Data Set Information:</strong></p> <p>The data set was experimentally obtained with a hydraulic test rig. This test rig consists of a primary working and a secondary cooling-filtration circuit which are connected via the oil tank [1], [2]. The system cyclically repeats constant load cycles (duration 60 seconds) and measures process values such as pressures, volume flows and temperatures while the condition of four hydraulic components (cooler, valve, pump and accumulator) is quantitatively varied.</p> <p> </p> <p><strong>Attribute Information:</strong></p> <p>The data set contains raw process sensor data (i.e. without feature extraction) which are structured as matrices (tab-delimited) with the rows representing the cycles and the columns the data points within a cycle. The sensors involved are:<br> Sensor Physical quantity Unit Sampling rate<br> PS1 Pressure bar 100 Hz<br> PS2 Pressure bar 100 Hz<br> PS3 Pressure bar 100 Hz<br> PS4 Pressure bar 100 Hz<br> PS5 Pressure bar 100 Hz<br> PS6 Pressure bar 100 Hz<br> EPS1 Motor power W 100 Hz<br> FS1 Volume flow l/min 10 Hz<br> FS2 Volume flow l/min 10 Hz<br> TS1 Temperature °C 1 Hz<br> TS2 Temperature °C 1 Hz<br> TS3 Temperature °C 1 Hz<br> TS4 Temperature °C 1 Hz<br> VS1 Vibration mm/s 1 Hz<br> CE Cooling efficiency (virtual) % 1 Hz<br> CP Cooling power (virtual) kW 1 Hz<br> SE Efficiency factor % 1 Hz<br> <br> The target condition values are cycle-wise annotated in ‘profile.txt’ (tab-delimited). As before, the row number represents the cycle number. The columns are<br> <br> 1: Cooler condition / %:<br> 3: close to total failure<br> 20: reduced effifiency<br> 100: full efficiency<br> <br> 2: Valve condition / %:<br> 100: optimal switching behavior<br> 90: small lag<br> 80: severe lag<br> 73: close to total failure<br> <br> 3: Internal pump leakage:<br> 0: no leakage<br> 1: weak leakage<br> 2: severe leakage<br> <br> 4: Hydraulic accumulator / bar:<br> 130: optimal pressure<br> 115: slightly reduced pressure<br> 100: severely reduced pressure<br> 90: close to total failure<br> <br> 5: stable flag:<br> 0: conditions were stable<br> 1: static conditions might not have been reached yet</p> <p> </p> <p><strong>Relevant Papers:</strong></p> <p>[1] Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.<br> [2] N. Helwig, A. Schütze, ‘Detecting and compensating sensor faults in a hydraulic condition monitoring system’, in Proc. SENSOR 2015 - 17th International Conference on Sensors and Measurement Technology, oral presentation D8.1, Nuremberg, Germany, May 19-21, 2015, doi: 10.5162/sensor2015/D8.1.<br> [3] Tizian Schneider, Nikolai Helwig, Andreas Schütze, ‘Automatic feature extraction and selection for classification of cyclical time series data’, tm - Technisches Messen (2017), 84(3), 198 – 206, doi: 10.1515/teme-2016-0072.</p> <p><br> </p> <p><strong>Citation Request:</strong></p> <p>Nikolai Helwig, Eliseo Pignanelli, Andreas Schütze, ‘Condition Monitoring of a Complex Hydraulic System Using Multivariate Statistics’, in Proc. I2MTC-2015 - 2015 IEEE International Instrumentation and Measurement Technology Conference, paper PPS1-39, Pisa, Italy, May 11-14, 2015, doi: 10.1109/I2MTC.2015.7151267.</p>
Envixlab/OpenMICE: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data
<p>Provided in support of the Data-paper: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data by Paniccia, C., M. Di Febbraro, L. Delucchi, R. Oliveto, M. Marchetti, and A. Loy. 2018. Ecology. <a href="https://github.com/Envixlab/OpenMICE/files/2273658/OpenMICE.sqlite.zip">OpenMICE.sqlite.zip</a></p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: LPJ-GUESS potato
<p>This is model output from LPJ-GUESS for potato as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</p>
AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set: CLM-Crop cotton
<p>This is model output from CLM-Crop for cotton as part of AgMIP's Global Gridded Crop Model Intercomparison (GGCMI) phase 1 output data set.</p> <p>The data have been generated following the modeling protocol of Elliott et al. (2015) and has been used to evaluate the models (Müller et al., 2017). A data description paper has been published in Scientific Data (Müller et al. 2019).</p> <p>References:</p> <p>Elliott J, Müller C, Deryng D, Chryssanthacopoulos J, Boote KJ, Büchner M, Foster I, Glotter M, Heinke J, Iizumi T, Izaurralde RC, Mueller ND, Ray DK, Rosenzweig C, Ruane AC, and Sheffield J. 2015, The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261-277, doi:10.5194/gmd-8-261-2015</p> <p>Müller C, Elliott J, Chryssanthacopoulos J, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Glotter M, Hoek S, Iizumi T, Izaurralde RC, Jones C, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Ray DK, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Song CX, Wang X, de Wit A, and Yang H. 2017, Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403-1422, doi: 10.5194/gmd-10-1403-2017</p> <p>Müller C, Elliott J, Kelly D, Arneth A, Balkovic J, Ciais P, Deryng D, Folberth C, Hoek S, Izaurralde RC, Jones CD, Khabarov N, Lawrence P, Liu W, Olin S, Pugh TAM, Reddy A, Rosenzweig C, Ruane AC, Sakurai G, Schmid E, Skalsky R, Wang X, de Wit A, and Yang H. 2019, The Global Gridded Crop Model Intercomparison phase 1 simulation dataset, Scientific Data, 6, 50, doi: 10.1038/s41597-019-0023-8</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.