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87 results for “data documentation”
Altmetric data of the documents from Web of Science with the affiliation to the Czech Republic
<p>These datasets were generated for the master thesis with the title "Altmetrics and its use in the evaluation of scientific research" (in Czech "Altmetrie a její využití při hodnocení vědeckého výzkumu"), that was submitted in 2022 at the Institute Information Studies and Librarianship, Charles University, Prague, Czech Republic. There are 3 files, where the first one "Příloha_číslo_1-processed_data.xlsx" contains processed and analyzed data from the other two. "Příloha_číslo_2-data_altmetrics_Kvet_1st_download.csv" is the file with raw altmetric data downloaded on the 9/1/2022. "Příloha_číslo_3-data_altmetrics_Kvet_2st_download.csv" are the same data but downloaded two months later to see the difference.</p> <p>Altmetric data came from PlumX and Altmetric.com aggregators. Before publishing them here there were completely anonymized so there is no possibility to assign them to particular research papers. Dataset contains altmetric data of the research documents published in the period from 2017 to 2021. All documents have affiliation to the Czech Republic.</p> <p>The tool for collection and analyzation is available on Github here: <a href="https://github.com/kvetjo/Altmetrics_analyze_tool">https://github.com/kvetjo/Altmetrics_analyze_tool</a></p> <p>Thesis reference in Czech:</p> <p>KVĚT, Jonáš. <em>Altmetrie a její využití při hodnocení vědeckého výzkumu</em> [online]. Praha, 2022 [cit. 2022-05-03]. Diplomová práce. Univerzita Karlova. Filozofická fakulta. Ústav informačních studií a knihovnictví. Vedoucí práce Jan Dvořák.</p>
Original dataset of :"First pre-Miocene paleomagnetic data from the Calabrian block document a 160° post-late Jurassic CCW rotation as a consequence of left-lateral shear along Alpine Tethys"
<p>In this table the original paleomagnetic dataset related to the research article :"First pre-Miocene paleomagnetic data from the Calabrian block document a 160° post-late Jurassic CCW rotation as a consequence of left-lateral shear along Alpine Tethys" is published</p>
Assisted Data Annotation for Business Process Information Extraction from Textual Documents
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Data from: A new dimension in documenting new species: high-detail imaging for myriapod taxonomy and first 3D cybertype of a new millipede species (Diplopoda, Julida, Julidae)
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Data from: The challenge of accurately documenting bee species richness in agroecosystems: bee diversity in eastern apple orchards
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Data from: Capturing goats: documenting two hundred years of mitochondrial DNA diversity among goat populations from Britain and Ireland
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Data from: A low-cost solution for documenting distribution and abundance of endangered marine fauna and impacts from fisheries
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Data from: A metacalibrated time-tree documents the early rise of flowering plant phylogenetic diversity
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Deep sequencing data for document titled: Rolling circle RNA synthesis catalysed by RNA
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Data from: Tampa Bay (Florida, USA): documenting seagrass recovery since the 1980’s and reviewing the benefits
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Does autotext usage decrease documentation time among resident physicians? A retrospective analysis of EHR usage data
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Dispatches from the neighborhood watch: using citizen science and field survey data to document color morph frequency in space and time
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Keizer et al. "Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics" – Data, software and documentation (10/16)
<p>Data, software and documentation to reproduce the results presented in [<a href="https://www.science.org/doi/10.1126/science.abi9810">Keizer <em>et al.</em> (2022) ‘<strong>Live-cell micromanipulation of a genomic locus reveals interphase chromatin mechanics</strong>’ Science, 377:6605</a>, DOI: 10.1126/science.abi9810].</p> <table> <tbody> <tr> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Location</strong></p> </td> </tr> <tr> <td> <p><strong>Centralized GitHub repository</strong> with:</p> <ul> <li>Local copy of all the code and trajectory/force files</li> <li>Jupyter notebooks to make all the graphs in Keizer <em>et al</em>.</li> <li>Pointers to all the datasets also shown in this table</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/Keizer-et-al">Keizer <em>et al.</em></a> repository</p> </td> </tr> <tr> <td> <p><strong>Raw microscopy data</strong>:</p> <ul> <li>Experiments performed with the <strong>30’-PR</strong> scheme</li> <li>Experiment performed with the<strong> 100”-PR</strong> scheme</li> <li>Experiment performed with high frame rate (<strong>dt = 0.5”</strong>)</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4626942">Zenodo 1</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627034">Zenodo 2</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626909">Zenodo 3</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626914">Zenodo 4</a> (30’-PR)<br> <a href="https://zenodo.org/record/4627010">Zenodo 5</a> (30’-PR)<br> <a href="https://zenodo.org/record/4626981">Zenodo 6</a> (100”-PR)<br> <a href="https://zenodo.org/record/6510099">Zenodo 7</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510103">Zenodo 8</a> (30’-PR)<br> <a href="https://zenodo.org/record/6510065">Zenodo 9</a> (dt = 0.5")<br> <a href="https://zenodo.org/record/6510105">Zenodo 10</a> (30’-PR)</p> </td> </tr> <tr> <td> <p>Concatenated TIFFs and timestamp files for all of the 30’-PR data.</p> </td> <td> <p><a href="https://zenodo.org/record/6510107">Zenodo 11</a> (1/2)<br> <a href="https://zenodo.org/record/6510109">Zenodo 12</a> (2/2)</p> </td> </tr> <tr> <td> <p><strong>Python pipeline </strong>to generate (i) concatenated movies, (ii) cropped and rotated movies for each cell, and (iii) force time profiles for each cell.</p> </td> <td> <p><a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository</p> </td> </tr> <tr> <td> <ul> <li><strong>Final registered and rotated TIFF files</strong>: <ul> <li><strong>30’-PR</strong> experiments: n = 35 cells</li> <li><strong>100”-PR</strong> experiment, including time projections & kymograph</li> <li><strong>dt = 0.5”</strong> experiments: n = 3 cells</li> <li><strong>no force</strong>: n = 11 cells before manipulation, n = 8 cells after manipulation</li> </ul> </li> <li><strong>Data files with trajectories and force time profiles</strong> for all analyzed cells</li> <li>Instructions and Fiji/Python scripts to reproduce these files.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510207">Zenodo 13</a></p> </td> </tr> <tr> <td> <p><strong>Single-MNPs fluorescence</strong>: raw data, Python/Fiji scripts and instructions</p> </td> <td> <p><a href="https://zenodo.org/record/6510209">Zenodo 14</a></p> </td> </tr> <tr> <td> <ul> <li>MagSim, <strong>Python library for magnetic simulations</strong></li> <li>Jupyter notebook for calibrating and generating maps (Fig. S5 & Fig. S6).</li> </ul> </td> <td> <p><a href="https://github.com/CoulonLab/MagSim">MagSim</a> repository</p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 1</strong>: Gradient of free GFP-ferritin in solution</p> <ul> <li>Raw microscopy data (6 pillars; Fig. S6B-C)</li> <li>Calculated force maps, with Fiji scripts and instructions to generate them.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/4627062">Zenodo 15</a></p> </td> </tr> <tr> <td> <p><strong>Force calibration – Method 2</strong>: Attraction of ferritin-coated beads (Fig. S7)</p> <ul> <li>Raw microscopy data (free diffusion and attraction)</li> <li>Python/Fiji scripts to calculate forces.</li> </ul> </td> <td> <p><a href="https://zenodo.org/record/6510211">Zenodo 16</a></p> </td> </tr> <tr> <td> <ul> <li><strong>Python library for force inference</strong> using different polymer models</li> </ul> </td> <td> <p><a href="https://github.com/SGrosse-Holz/rouselib">rouselib</a> repository</p> </td> </tr> </tbody> </table> <p><strong>License:</strong> All the code, data and documentation in this repository is under <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a> license. The <a href="https://hal-cnrs.archives-ouvertes.fr/hal-03740646"><em>Author Accepted Manuscript</em></a> of the study [Keizer <em>et al.</em> 2022] is under <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a> license. The <a href="https://www.science.org/doi/10.1126/science.abi9810"><em>Final Published Version</em></a>, published by AAAS, is not (<a href="https://www.science.org/content/page/science-licenses-journal-article-reuse">more information</a>).</p> <p> </p> <p><strong>Overview of the raw data repositories (Zenodo 1-10)</strong></p> <p><em>Refer to the Material and Methods section of the article for details on data production.</em></p> <p>Each Zenodo dataset represents one day of acquisition. It includes the data that was not retained for further downstream analysis. Each dataset contains:</p> <ul> <li>The raw MicroManager folder architecture (one folder contains multiple positions on the coverslip). On occasions where placement or removal of the external magnet led to a loss of focus, the acquisition was stopped and restarted, creating a new MicroManager folder each time. For instance: <ul> <li>The various positions were imaged before injection (folder with the <em>_preInjection,</em> <em>_1-pre-inj or _1-inj_1</em> suffix)</li> <li>These positions were imaged again after injection (suffix <em>_postInjection,</em> <em>_2-post-inj </em>or <em>_1-inj_2</em>) and before the magnet was added (suffix <em>_beforeexp</em> or <em>_before-attr</em>)</li> <li>They were imaged again with the magnet added (suffix <em>_attraction1</em>). If acquisition was stopped and restarted an extra folder is created (suffix <em>_attraction2</em>)</li> <li>They were then imaged after the magnet was removed (suffix <em>_release1</em>)</li> <li>Finally, the cells were monitored after the experiment (suffix <em>_after-exp</em> or <em>_postexp</em>)</li> </ul> </li> <li>A text file named <em>lab_journal_[...].txt</em> contains extra information the acquisition and experimental procedure</li> <li>Note: the MicroManager metadata in the TIFF file are fully populated</li> </ul> <p> </p> <p><strong>Overview of the concatenated datasets (Zenodo 11-12)</strong></p> <p>In these Zenodo repository, each position (acquired in different folders), is concatenated into a single TIFF movie using code available in the <a href="https://github.com/CoulonLab/chromag-pipeline">ChroMag-pipeline</a> repository. The folder contains:</p> <ul> <li>One TIFF file per selected position</li> <li>One .xls file per selected position, with one line per frame, and columns with the following information: <ul> <li><strong>path</strong> (Relative path): Reference to the original (raw MicroManager) file</li> <li><strong>start_time</strong> (Timestamp): Timestamp saved by MicroManager when the acquisition was started (the «acquire » button was pressed).</li> <li><strong>time_in_file</strong> (seconds): Number of seconds between start_time and the acquisition of the current timepoint</li> <li><strong>start_time_s</strong> (seconds): Variable start_time converted to a number of seconds</li> <li><strong>time</strong> (seconds): Sum of start_time and time_in_file</li> <li><strong>timestamp</strong> (Timestamp): Variable time, back-converted to a timestamp</li> <li><strong>timeOn</strong> (Timestamp): Time(s) when the magnet was added. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>timeOff</strong> (Timestamp): Time(s) when the magnet was removed. This timestamp is provided in the datasets.cfg file in the github repository chromag-pipeline</li> <li><strong>forceActivated</strong> (Boolean): If the magnet is present during the current frame (calculated from timeOn and timeOff)</li> <li><strong>seconds_since_first_magnet_ON</strong> (seconds): Number of (relative) seconds since the magnet was added for the first time.</li> <li><strong>Frame</strong> (Integer) Frame number (1-indexed)</li> <li><strong>Positions</strong> (Integer): The position number</li> </ul> </li> </ul> <p> </p> <p><strong>Processed datasets (Zenodo 13) and calibration datasets (Zenodo 14-16)</strong></p> <p>These datasets and their analysis are fully described in the <em>Materials and Methods</em> section of the article and in the different README.md files within the various folders of the datasets.</p>
Data from: Genetic analysis identifies the missing parchment of New Zealand's founding document, The Treaty of Waitangi.
Genetic analyses provide a powerful tool with which to identify the biological components of historical objects. Te Tiriti o Waitangi | The Treaty of Waitangi is New Zealand's founding document, intended to be a partnership between the indigenous Māori and the British Crown. Here we focus on an archived piece of blank parchment that has been proposed to be the missing portion of the lower parchment of the Waitangi Sheet of the Treaty. However, its physical dimensions and characteristics are not consistent with this hypothesis. We perform genetic analyses on the parchment membranes of the Treaty, plus the blank piece of parchment. We find that all three parchments were made from ewes and that the blank parchment is highly likely to be a portion cut from the lower membrane of the Waitangi Sheet because they share identical whole mitochondrial genomes, including an unusual heteroplasmic site. We suggest that the differences in size and characteristics between the two pieces of parchment may have resulted from the Treaty's exposure to water in the early 20th century and the subsequent repair work, light exposure during exhibition or the later conservation treatments in the 1970s and 80s. The blank piece of parchment will be valuable for comparison tests to study the effects of earlier treatments and to monitor the effects of long-term display on the Treaty.
OSH Automated Documentation Data
<p>CAD files that are used as examples to show how to generate assembly manuals with OSH Automated Documentation.</p>
Data from: Genetic analysis identifies the missing parchment of New Zealand’s founding document, The Treaty of Waitangi.
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Usability Evaluation Process for Domain-Specific Languages - Documents and survey data
<p>Usability Evaluation Process for Domain-Specific Languages - Documents and survey data</p>
Meterstick Benchmark: Source, Documentation and Data
<p>Contains the artifacts and data collected of Meterstick: a benchmark for performance variability in cloud-based and self-hosted modifiable virtual environments.</p> <p>Included is the source code and compiled artifacts of Meterstick, as well as associated documentation.</p> <p>Also included is the data collected using Meterstick during the experiments described in the related article, as well as plotting instruments for this data.</p>
Open Label Study to Collect Clinical Data to Document Clinical Performance and Safety in Total Knee Arthroplasty
ClinicalTrials.gov study NCT04727060. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Historical DNA documents long distance natal homing in marine fish
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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.