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22,922 results for “Collections as data”
Primary collected data for modelling the additive MAR/R process by means of the PBF-LB process based on the example of tool steel 1.2709 powder
<p>For the evaluation of a MAR/R process, not only process-, material- and demonstrator-specific correlations and data must be combined. In addition to secondary data (e.g. databases, publications, etc.), primary data (e.g. process times, volume flows, etc.) must also be collected for the specific application.</p> <p>The attached table shows the primary data to be collected for the cradle-to-gate process depending on the process phases and steps. This data is used as support for ecological as well as economic process and component evaluations.</p>
Data Collection and Manipulation Template for FlexTool
<p>This repository includes the following files:</p> <p><strong>1) FlexTool_Data_Collection_template.xlsx: </strong>Workbook for inserting raw data, manipulating it and preparing FlexTool input sheets. The data collection and manipulation template for IRENA FlexTool 2.0 serves as a quick and standardized methodology for putting together a single-node country model in FlexTool.<br> <strong>2) FlexTool_Starter_template.xlsm: </strong>Template FlexTool input file structured to receive the input sheets, as prepared in the FlexTool_Data_Collection_template.xlsx<br> <strong>3) Data Collection Template for IRENA FlexTool.pdf</strong>: Guidelines for preparing a FlexTool model based on the Data Collection and Manipulation template. </p> <p><strong>4) OSeMOSYS and FlexTool data sharing</strong>: Instructions on how to populate the FlexTool input file from the OSeMOSYS SAND file<br> FlexTool data file population:</p> <p><strong>5) Instructions on how to populate the FlexTool Data Collection File from the OSeMOSYS Data Collection File</strong>.</p>
Individual and collective school-students' self-efficacy on climate change. Raw data set.
<p>The dataset comprises 12 items on the topic of "self-efficacy on climate change" and additionally some items on person-related information. The items were developed with individual self-efficacy (8 items, iSE1 to iSE8) and collective self-efficacy (4 items, coSE1 to coSE4) in mind. The instruction was: "How do you think about yourself? Please tick to what extent the following statements apply to you" [translated from German]. A six-point Likert-type scale was used as response scale, headed with numbers from 1 to 6, only the ends of the response scale were verbally labelled (1 = "strongly disagree" to 6 = "strongly agree" [translated from German]). The items were used in German. The English translations were added to the dataset in square brackets. Data collection took place at German "Gymnasien" (equivalent to grammar schools) in 2022. The data set includes N = 163 school-students.</p>
Individual and collective university students' self-efficacy on climate change. Raw data set.
<p>The dataset comprises 12 items on the topic of "self-efficacy on climate change" and additionally some items on person-related information. The items were developed with individual self-efficacy (8 items, iSE1 to iSE8) and collective self-efficacy (4 items, coSE1 to coSE4) in mind. The instruction was: "How do you think about yourself? Please tick to what extent the following statements apply to you" [translated from German]. A six-point Likert-type scale was used as response scale, headed with numbers from 1 to 6, only the ends of the response scale were verbally labelled (1 = "strongly disagree" to 6 = "strongly agree" [translated from German]). The items were used in German. The English translations were added to the dataset in square brackets. Data collection took place at a German university in 2022. The data set includes N = 145 university students studying to become geography teachers.</p>
RECCAP2-ocean data collection
<p>This repository contains data sets that were submitted to and analysed in the ocean branch of phase two of the REgional Carbon Cycle Assessment and Processes (RECCAP2-ocean) project.</p> <p>Protocols defining the criteria for data set submissions to RECCAP2-ocean are available through the project website:<br> https://reccap2-ocean.github.io/protocols/</p> <p>A detailed description of the submitted data sets is provided in three files contained in the supplementary.tar file. These are:</p> <ul> <li>An overview table of all regularly submitted data sets including contact details for each submission is provided in RECCAP2-ocean_data_products_overview.xlsx.</li> <li>The documentation of a quality control exercise is provided in QC_results_RECCAP2-ocean.pdf. All regular data sets have been quality controlled (i.e. checked for consistency with the submission protocol and general plausibility of results) by the participants of RECCAP2 (i.e. the users of the data sets).</li> <li>A documentation of remaining data set issues that were identified after the initial quality control is provided in Remaining_data_set_issues.pdf. These remaining issues have not been corrected.</li> </ul> <p>All originally submitted files were unpacked, compressed, and repacked into tar files before submission to Zenodo.</p> <p>The individual submitted data sets are broadly grouped into four classes indicated by the prefix of the tar files:</p> <ul> <li>models: Global and regional ocean biogeochemical hindcast models (GOBM/ROBM) as well as ocean data-assimilation models (data-assimilation models)</li> <li>surface_co2: Surface ocean pCO2-observation products (pCO2 products)</li> <li>ocean_interior: Ocean interior DIC-observation products (DIC products)</li> <li>atmospheric _inversions: Atmospheric inversion models</li> </ul> <p>Some additional files that were only used by individual chapters of RECCAP2-ocean and were not quality controlled are contained in the supplementary.tar file.</p> <p>Jens Daniel Müller, RECCAP2-ocean coordinator, May 2023</p> <p>on behalf of the entire RECCAP2-ocean team</p>
Data for: Soil organic carbon contents of collected soil samples from China's black soil region
<p><span>The long-term use of cropland and cropland reclamation from natural ecosystems led to soil degradation. This study investigated the effect of the long-term use of cropland and cropland reclamation from natural ecosystems on soil organic carbon (SOC) content and density over the past 35 years. Altogether, 2140 topsoil samples (0</span>–<span>20 cm) were collected across Northeast China. Landsat images were acquired from 1985 to 2020 through Google Earth Engine, and the reflectance of each soil sample was extracted from the Landsat image that its time was consistent with sampling. The hybrid model that included two individual SOC prediction models for two clustering regions was built for accurate estimation after k-means clustering. The probability hybrid model, a combination between the hybrid model and classification probabilities of pixels, was introduced to enhance the accuracy of SOC mapping. Cropland reclamation results were extracted from the land cover time series dataset at a 5-year interval. Our study indicated that: (1) Long-term use of cropland led to a 3.07 g kg<sup>-1</sup> and 6.71 Mg C ha<sup>-1</sup> decrease in SOC content and density, respectively, and the decrease of SOC stock was 0.32 Pg over the past 35 years; (2) Nearly 64% of cropland had a negative change in terms of SOC content from 1985 to 2020; (3) Cropland reclamation track changed from high to low SOC content, and almost no cropland was reclaimed on the 'Black soils' after 2005; (4) Cropland reclamation from wetlands resulted in the highest decrease, and reclamation period of years 31</span>–<span>35 decreased when SOC density and SOC stock were 16.05 Mg C ha<sup>-1</sup> and 0.005 Pg, respectively, while reclamation period of years 26</span>–<span>30 from forest witnessed SOC density and stock decreases of 8.33 Mg C ha<sup>-1</sup> and 0.01 Pg, respectively. Our research results provide a reference for SOC change in the black soil region of Northeast China and can attract more attention to the area of the protection of 'Black soils' and natural ecosystems.</span></p>
Raw EK80 echosounder data of northern shrimp collected by wideband autonomous transceiver in mesocosm AZKABAN 2023-01-26
<p>Broadband active acoustic measurements using two echosounders (90-170 kHz and 185-255 kHz) of northern shrimp (Pandalus borealis) collected in Krossfjorden in a mesocosm (2m x 2m x 3m). The experiment was completed from the wharf in Ny-Ålesund. The fileset includes measurements from the experiment of January 26, 2023 and CTD data. Calibration data is available <a href="https://doi.org/10.5281/zenodo.8289670">https://doi.org/10.5281/zenodo.8289670</a>.</p>
Data collected from questionnaires to collect feedback on the services' performance / accelerator programme and outcomes
<p>Results of the immediate satisfaction surveys deployed to: <br> (i) all participants of the syncronous induction training [Tab: 2.InductionTrainingSync]; <br> (ii) all participants of the asyncrhous induction training - [Tab: 3.InductionTrainingAsync]; <br> (iii) makerpsace admins, consultants,mentors that participated in the delivery of the entire PMCA programme [Tab: 4.PCMA - Admins] and <br> (iv) to maker teams that succesfully finished the Mentoring stage of the PCMA programme [Tab: 5.PCMA - Makers] with a view to capture their impressions of programme and recommendations for further improvements. </p> <p>The present spreadsheet is also accompanied by four (4) documents (.pdf) that present the surveys deployed for collecting the above responses. In particular: <br> 1.InductionTrainingAsyncSatisfactionSurvey_Questions.pdf<br> 2.InductionTrainingSyncSatisfactionSurvey_Questions.pdf<br> 3.PCMASatisfactionSurveyMakerpace_Questions.pdf<br> 4.PCMASatisfactionSurveyMakers_Questions.pdf</p>
GPT vs Stack Overflow: data collection (A2I2 T2 2023)
<p><strong>About</strong></p> <p>The dataset components produced by <a href="https://github.com/MHLoppy/A2I2-T2-2023">this repo</a>. Please see the documentation there for more information.</p> <p>Each CSV has been individually zipped so that you only have to download the specific file(s) that you want.</p> <p> </p> <p><strong>Overview of Files</strong></p> <p>From using the <a href="https://archive.org/details/stackexchange">Stack Exchange Data Dump</a> as the data source (these zip files have a <strong>DD_</strong> prefix):</p> <ul> <li>Raw dataset before processing: <strong>saved_dataset.csv (DD_saved_dataset.zip)</strong></li> <li>Completed tag count: <strong>tag_count.csv (DD_tag_count.zip)</strong></li> <li>Processed dataset with completed evaluations: <strong>dataset_results.csv (DD_dataset_results.zip)</strong></li> </ul> <p>From using Google BigQuery as the data source (these zip files have a <strong>BQ_</strong> prefix):</p> <ul> <li>Raw dataset before processing: <strong>saved_dataset.csv (BQ_saved_dataset.zip)</strong></li> <li>Completed tag count: <strong>tag_count.csv (BQ_tag_count.zip)</strong></li> <li><em>No large-scale evaluation was completed when using BigQuery as a data source.</em></li> </ul> <p>As noted in the linked repo, the use of Google BigQuery as a data source is not recommended for this work, but the working code and dataset have nonetheless been provided for completeness.</p> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under the <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA 4.0 license</a>, the same license used by the Stack Exchange Data Dump.</p>
data collection for NEMO BGC assessment in idealised models (GM in non-eddying models)
<p>Edit in ver 3, 17 Oct 2023:</p><ul><li>(<strong>important</strong>) there is a bug NEMO GEOMETRIC code (advection of parameterised eddy energy; see ldfeke.F90), fixed in the present version</li><li>new data files updated (everything is included for completeness, although only the ones using GEOMETRIC have been updated)</li><li>data files and software files have been split out into separate zip files to enable easier downloads</li></ul><p>=========================</p><p>Edit in ver 2, 06 Feb 2023:</p><ul><li>added calculation files with Treguier et al variant of GM, supplement data not explicitly leading to figure in paper;</li><li>fixes of masking for boundary values when computing averages (no change to figures, minor changes to numerical values);</li><li>fixing a bug in the calculation of surface vorticity (raw numerical value in units of s-1 correct, but incorrect in units of f_0 because of a missing sin(latitude) factor)</li></ul><p>=========================</p><p>Data archive for "Combined physical and biogeochemical assessment of mesoscale eddy parameterisations in ocean models: eddy induced advection at non-eddying resolutions". Provided are:</p><p>1) modification and configuration files for the GYRE_PISCES configuration in NEMO 4.0.5 (r14538), with sample restart and output files</p><ul><li>CONST restart file at year 2000 (end of spin up, denoted year -300 in the paper)</li><li>CONST, GEOM, R12 restart file at year 2300 (beginning of control/climate change split, denoted year 0 in the paper)</li><li>CONST, GEOM, R12 sample output files at year 2366 to 2370 (denoted year 66 to 70 in paper)</li></ul><p>2) processed time-averaged data for regenerating figures from the article<br>3) python scripts and notebooks for analysing the data</p>
FIRE AND ICE Re-Ablations (Retrospective Data Collection)
ClinicalTrials.gov study NCT03314753. IPD Sharing: NO. Countries: 6. Publications: 1.
Pilot Study to Collect and Evaluate Data on the Use of IV* Ibuprofen in the Treatment of an Acute Migraine Attack
ClinicalTrials.gov study NCT01230411. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Post Market Study To Collect Efficacy Data For The Treatment Of Wrinkles With A Radiofrequency Device
ClinicalTrials.gov study NCT05096247. IPD Sharing: NO. Countries: 1. Publications: 1.
A Non-interventional, International, Multicentre Clinical Research Study to Build the Largest Collection of Multimodal Data (Including Clinical Data, Imaging Data and Omics Data) in Oncology
ClinicalTrials.gov study NCT06625203. IPD Sharing: YES. Countries: 4. Publications: 7.
Recommendations of Enhanced Recovery Interventions for Patient's Clinical Team and Collection of Associated Data
ClinicalTrials.gov study NCT04606264. IPD Sharing: YES. Countries: 1. Publications: 1.
Wearable Assisted Viral Evidence (WAVE) Study A Decentralized, Prospective Study Exploring the Relationship Between Passively-collected Data From Wearable Activity Devices and Respiratory Viral Infect
ClinicalTrials.gov study NCT06207929. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
Paclitaxel, Bevacizumab and Pemetrexed in Patients With Untreated, Advanced Non-Small Cell Lung Cancer Using Web-Based Data Collection, Patient Self-Reporting of Adverse Effects and Automated Response
ClinicalTrials.gov study NCT00807573. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Retrospective Review of Mechanically Ventilated Patients Using a Continuous Data Collection System.
ClinicalTrials.gov study NCT02184208. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Real-World Data Collection of the GORE® VIABAHN® VBX Balloon Expandable Endoprosthesis When Used as a Bridging Stent With Branched and Fenestrated Endografts in the Treatment of Aortic Aneurysms Invol
ClinicalTrials.gov study NCT05143138. IPD Sharing: Not stated. Countries: 6. Publications: 0.
ASCEND: ApproacheS to CHC ImplEmeNtation of SDH Data Collection and Action
ClinicalTrials.gov study NCT03607617. IPD Sharing: NO. Countries: 1. Publications: 3.
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.