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4,856 results for “sections”
IODP Expedition 383 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 378 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 378 Section-half images
Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a>.
IODP Expedition 378 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
IODP Expedition 378 Section summary
Report includes data for individual core sections: coring/drilling depths and recovery, database identifiers for the whole section and section halves, and number of samples taken from the section before and after splitting.
General practice characteristics associated with life expectancy of practice populations: a cross-sectional study
<p>The dataset was used to investgate features of general practice associated with life expectancy of general practice populations in England for the period 2015-2019.</p>
Data from a cross-sectional study of fifth grade children in a sample of primary schools in Belgium that differ in amount of greenness at school and landscape level
<p>The data in this deposit were collected as part of the <code>B@SEBALL</code> project (Biodiversity at School Environments - Benefits for All). </p> <p>The project investigated how biodiversity in the school environment can positively affect children’s health and mental well-being. <code>B@SEBALL</code> also investigated the opportunities for reducing health inequalities among children via biodiversity at school environments.</p> <p>The data are organized according to the <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package standard</a>. All child-level and school-level data have been anonymized. Each data package is a collection of <code>csv</code> files and a <code>json</code> file. The <code>json</code> file holds descriptive information for all variables in all <code>csv</code> files. The <code>zip</code> file contains two frictionless data packages. The data packages contain information on 37 primary schools and 513 children. </p> <p>The data package, <code>data_package_an_zenodo_cleaned_data</code>, contains the original data in a tidied and cleaned format. It consists of 46 <code>csv</code> files. The files relate to the following contents:</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>landscape level variables</td> <td>wp1_landscape_level_data.csv</td> </tr> <tr> <td>metadata about participants</td> <td>wp2_participants_metadata.csv</td> </tr> <tr> <td>general school level data</td> <td>wp2_school_data.csv</td> </tr> <tr> <td>pollution data at school level</td> <td>wp3_ua_sirm_data.csv</td> </tr> <tr> <td>classroom data about air quality</td> <td>wp3_ucl_classroom_airquality.csv</td> </tr> <tr> <td>area of ecotopes in the school environment</td> <td>wp3_ucl_ecotope_categories.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_indicators.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_key.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenpatches.csv</td> </tr> <tr> <td>playground biodiversity indicators</td> <td>wp3_ucl_playground_biodiversity.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_child.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_line.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_linegroup.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_data.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_questions.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_data.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_questions.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_data.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_questions.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_data.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_key.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part1.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part2.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_key.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_data.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_key.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_data.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_key.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_data.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_key.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_data.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_key.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_data.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_key.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_data.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_key.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part1.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part2.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part3.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part4.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_key.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part1.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part2.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_key.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_data.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_key.csv</td> </tr> </tbody> </table> <p> </p> <p>The <code>data_package_an_zenodo_derived_data</code> data package, contains derived data that was calculated based on input from <code>data_package_an_zenodo_cleaned_data</code> at either child-level or at school-level.</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>derived data at child level</td> <td>wp1_child_level_key_variables.csv</td> </tr> <tr> <td>derived attention score based on d2-test data, aggregated to line-level</td> <td>wp1_d2_by_line_attention_score.csv</td> </tr> <tr> <td>derived data at school level</td> <td>wp1_school_level_key_variables.csv</td> </tr> </tbody> </table> <p>These data packages only store information for participants that gave consent for a particular part of the study and that gave consent for long-term storage of the data. There may therefore be slight differences between results published as part of the project consortium, which could make use of participant data that did not give consent for long-term data storage, and reproduction of these results based on the data in this data repository. We also note that the derived variables in the derived data package were calculated with these participants included and removal of participants for which we had no long-term storage consent was done after these calculations.</p> <p>As part of the project, microbiome data were also collected (both from cheek swabs on the children and from environmental samples), but this part of the data are not a part of this deposit and will be deposited in the European Nucleotide Archive (ENA).</p>
IODP Expedition 367 Whole-round core section composite 360 degree images
Images of the outside of hard rock whole-round sections were acquired using a linescan imager (Section Half Imaging Logger [SHIL]) and a special holder that allows each 90 degree segment of the outer surface to be positioned properly. The images were taken at a resolution of 20 lines/mm (50 micropixels). JRSO staff take these quadrant images and compile them into a side-by-side rollout photograph of the section. Composite images are available as both JPG and TIF image formats. Individual quadrant images are available as JPG images only through this report; contact the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a> if quadrant TIF files (~160 MB) are needed.
IODP Expedition 367 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 367 Whole-round core section images
Images of the outside of hard rock whole-round sections were acquired using a linescan imager (Section Half Imaging Logger [SHIL]) and a special holder that allows each 90 degree segment of the outer surface to be positioned properly. The images were taken at a resolution of 20 lines/mm (50 micropixels). JRSO staff take these quadrant images and compile them into a side-by-side rollout photograph of the section. Composite images are available as both JPG and TIF image formats. Individual quadrant images are available as JPG images only through this report; contact the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a> if quadrant TIF files (~160 MB) are needed.
IODP Expedition 367 Section summary
Report includes data for individual core sections: coring/drilling depths and recovery, database identifiers for the whole section and section halves, and number of samples taken from the section before and after splitting.
IODP Expedition 367 P-wave velocity caliper (section)
P-wave velocity data were measured on undisturbed section halves (JRSO-defined x-axis) and/or discrete cube and cylinder samples (x, y, or z-axis) using pairs of piezoelectric transducers mounted on a caliper system. Report includes P-wave velocity in x, y, and/or z-direction, caliper separation, traveltime between transucers, and first arrival picks.
IODP Expedition 367 P-wave velocity bayonet (section)
P-wave velocity data were measured on undisturbed section halves using pairs of piezoelectric transducers mounted in bayonets that are inserted into soft sediment along the JRSO-defined y-axis and/or z-axis. Report includes P-wave velocity in y and/or z direction, bayonet separation, traveltime between transducers, and first arrival picks.
IODP Expedition 367 Section-half images
Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a>.
IODP Expedition 367 Laser height profile (section half)
Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.
Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin
<p>This dataset and the associated Python notebooks are related to the publication "Spatial transcriptome data from coronal mouse brain sections after striatal injection of heme and heme-hemopexin".</p>
Serdyuchenko-Gorshelev UV/VIS/NIR ozone absorption cross-section
<p>This dataset provides ozone absorption cross-sections in the range of 213-1100 nm at a spectral resolution of about 1 cm^-1 (0.01-0.03nm) recorded with a combination of a Bruker HR 120 Fourier transform and ESA 400 Echelle spectrometer. Cross-section data are available for 11 temperatures from 193K to 293K sampled at 0.01nm.</p> <p>Further details on this dataset can be found in the two follwing publications:</p> <p>Gorshelev, V., Serdyuchenko, A., Weber, M., Chehade, W., and Burrows, J. P., <strong>High spectral resolution ozone absorption cross-sections – Part 1: Measurements, data analysis and comparison with previous measurements around 293 K</strong>, Atmos. Meas. Tech., 7, 609-624, doi:10.5194/amt-7-609-2014, 2014.</p> <p>Serdyuchenko, A., Gorshelev, V., Weber, M., Chehade, W., and Burrows, J. P., <strong>High spectral resolution ozone absorption cross-sections – Part 2: Temperature dependence</strong>, Atmos. Meas. Tech., 7, 625-636, doi:10.5194/amt-7-625-2014, 2014.</p>
Spatially gridded cross-shelf hydrographic sections and monthly climatologies from shipboard survey data collected along the Newport Hydrographic Line, 1997-2021
<p>This data set, described in detail in <a href="https://www.sciencedirect.com/science/article/pii/S2352340922001342">Risien et al. (2022)</a>, contains Newport Hydrographic Line station data; gridded, cross-shelf hydrographic sections; and derived monthly climatologies for temperature, practical salinity, potential density, spiciness, and dissolved oxygen. It consists of CSV (Comma Separated Values) files (<em>newport_hydrographic_line_station_data</em><em>.</em><em>zip</em>) that contain CTD observations collected at the seven hydrographic stations located 1, 3, 5, 10, 15, 20 and 25 nautical miles west of Newport, Oregon between March 1997 and July 2021. Additionally, the data set contains three NetCDF files that follow CF (Climate and Forecast) metadata conventions: <em>newport_hydrographic_line_gridded_sections</em><em>.nc</em> contains observations gridded to a 0.01<sup>o</sup> x 1 dbar longitude - pressure grid to create cross-shelf hydrographic sections for each of the five variables for each cruise. <em>newport_hydrographic_line_gridded_section_climatologies</em><em>.nc</em> contains climatological hydrographic sections, calculated using harmonic analysis over the 24-year period March 1997 to February 2021 and reported here for the middle of each month, and <em>newport_hydrographic_line_gridded_section_coefficients.nc</em> contains the associated linear regression model coefficients for all five variables. From the regression coefficients, users can construct seasonal cycles at any location in the gridded section with a temporal resolution that best suits their specific needs. Finally, this data set includes example MATLAB and R scripts that show how to read the data files, plot cross-shelf hydrographic sections, and calculate daily and monthly climatologies using the regression coefficients.</p>
Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study - DATASET
<p>Dataset to support the findings in the journal paper titled "Investigating the effects of COVID‑19 lockdown on Italian children and adolescents with and without neurodevelopmental disorders: a cross‑sectional study".</p> <p>Each row is a different subject.</p> <p>Each column represents an answer to the questionnaire. For single choice questions, the answer was reported as-is (Italian). For multiple choice questions, the alternatives where splitted in several columns and the answer was coded as 0/1 (one hot encoding). For the "school" column, 2=primary school, 3=middle school, 4=high school. For the "school.class" column, classes from 4 to 8 belong to primary school, from first to fifth grade; classes from 9 to 11 belong to middle school, from first to third grade; classes from 12 to 16 belong to high school, from first to fifth grade.</p>
Structure Annotations of Assessment and Plan Sections from MIMIC-III
<p>Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the "assessment and plan". This information is more clinically rich than structured billing codes assigned for an encounter but harder to reliably extract given the complexity of clinical language and documentation habits. To structure these sections we collected a dataset of annotations over assessment and plan sections from the publicly available and de-identified MIMIC-III dataset, and developed deep-learning based models to perform this task, described in the associated paper available as a pre-print at: <a href="https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1">https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1</a></p> <p>When using this data please cite our paper:</p> <pre><code>@article {Stupp2022.04.13.22273438, author = {Stupp, Doron and Barequet, Ronnie and Lee, I-Ching and Oren, Eyal and Feder, Amir and Benjamini, Ayelet and Hassidim, Avinatan and Matias, Yossi and Ofek, Eran and Rajkomar, Alvin}, title = {Structured Understanding of Assessment and Plans in Clinical Documentation}, year = {2022}, doi = {10.1101/2022.04.13.22273438}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2022/04/17/2022.04.13.22273438}, journal = {medRxiv} }</code></pre> <p>The dataset, presented here, contains annotations of assessment and plan sections of notes from the publicly available and de-identified MIMIC-III dataset, marking the active problems, their assessment description, and plan action items. Action items are additionally marked as one of 8 categories (listed below). The dataset contains over 30,000 annotations of 579 notes from distinct patients, annotated by 6 medical residents and students. </p> <p>The dataset is divided into 4 partitions - a training set (481 notes), validation set (50 notes), test set (48 notes) and an inter-rater set. The inter-rater set contains the annotations of each of the raters over the test set. Rater 1 in the inter-rater set should be regarded as an intra-rater comparison (details in the paper). The labels underwent automatic normalization to capture entire word boundaries and remove flanking non-alphanumeric characters.</p> <p>Code for transforming labels into TensorFlow examples and training models as described in the paper will be made available at GitHub: <a href="https://github.com/google-research/google-research/tree/master/assessment_plan_modeling">https://github.com/google-research/google-research/tree/master/assessment_plan_modeling</a></p> <p>In order to use these annotations, the user additionally needs to obtain the text of the notes which is found in the NOTE_EVENTS table from MIMIC-III, access to which is to be acquired independently (<a href="http://mimic.mit.edu">https://mimic.mit.edu/</a>)</p> <p>Annotations are given as character spans in a CSV file with the following schema:</p> <table> <tbody> <tr> <td>Field</td> <td>Type</td> <td>Semantics</td> </tr> <tr> <td>partition</td> <td>categorical (one of [train, val, test, interrater]</td> <td>The set of ratings the span belongs to.</td> </tr> <tr> <td>rater_id</td> <td>int</td> <td>Unique id for each the raters</td> </tr> <tr> <td>note_id</td> <td>int</td> <td>The note’s unique note_id, links to the MIMIC-III notes table (as ROW-ID).</td> </tr> <tr> <td>span_type</td> <td>categorical (one of [PROBLEM_TITLE,<br> PROBLEM_DESCRIPTION, ACTION_ITEM]</td> <td>Type of the span as annotated by raters.</td> </tr> <tr> <td>char_start</td> <td>int</td> <td>Character offsets from note start</td> </tr> <tr> <td>char_end</td> <td>int</td> </tr> <tr> <td>action_item_type</td> <td>categorical (one of [MEDICATIONS, IMAGING, OBSERVATIONS_LABS, CONSULTS, NUTRITION, THERAPEUTIC_PROCEDURES, OTHER_DIAGNOSTIC_PROCEDURES, OTHER])</td> <td>Type of action item if the span is an action item (empty otherwise) as annotated by raters.</td> </tr> </tbody> </table>
ScienceDex guides
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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.