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1,099 results for “cross section”
A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models
<p>Dataset corresponding to the associated publication, "A Missing Piece of the E-Region Puzzle: High-Resolution Photoionization Cross Sections and Solar Irradiances in Models." The dataset includes high-resolution photoionization and photoabsorption cross section for O and N<sub>2</sub> as well as high-resolution solar spectrum. Photoionization rates from model runs obtained from AURIC and the Meier photoionization code are also included. Please refer to the readme for information on the data structure.</p> <p><strong>***Please note that the paper is under review and has not been accepted yet.***</strong></p>
Near-field images and cross-section of guided modes in a laser-inscribed double-tracks waveguide in TZN:Ag glass sample
<p><strong>Raw images were captured</strong> with a Thorlabs beam monitoring camera, while the waveguides were injected at 633 nm.<br> The fours cross-sections were computed from these raw images.<br> These files are new data from the co-authors among those presented in the review publication "Materials 2020, 13, 3846" (DOI: 10.3390/ma13173846.<br> <strong>Extracted, centered and scaled horizontal cross-sections are given in "Fig12-b-c_final.xlsx"</strong></p> <p>Sample name : TZN:Ag.</p> <p> </p>
S50 | CCSCOMPEND | The Unified Collision Cross Section (CCS) Compendium
<p>This is the collection associated with list S50 CCSCOMPEND on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S50 CCSCOMPEND <strong>The Unified Collision Cross Section (CCS) Compendium</strong></p> <p>>3800 experimental collision cross section values (drift tube MS), provided by Jackie Picache and John McLean, Vanderbilt. Further details available here: <a href="https://lab.vanderbilt.edu/mclean-group/collision-cross-section-database/">https://lab.vanderbilt.edu/mclean-group/collision-cross-section-database/</a></p> <p>v0.1.1: removed char errors in InChIKey file. v0.1.2 (17 July 2022): added SMILES and separate substance deposition file, updated InChIKeys. SMILES were added via InChIKey in the CCS records (PubChem ID Exchange) then filling in gaps using PubChem Search to find the preferred tautomer; the substance deposition created from unique CIDs (via webchem), then the InChIKey file was created from this. v0.1.3 (19 July 2022): mapped to parents; substance deposition, SMILES, CIDs and annotations now based on parent form (not original salt form).</p>
S79 | UACCSCEC | Collision Cross Section (CCS) Library from UAntwerp
<p>This is the collection associated with list S79 UACCSCEC Collision Cross Section (CCS) Library from UAntwerp on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A library containing the collision cross section (CCS) values of 311 adducts of 148 contaminants of emerging concern (CECs) and their metabolites measured with drift tube ion mobility high resolution mass spectrometry (in positive and negative ionization modes with N2 as drift gas) as described in Belova <em>et al.</em> (2021) DOI: <a href="https://pubs.acs.org/doi/10.1021/acs.analchem.1c00142">10.1021/acs.analchem.1c00142</a>.</p> <p>Changes: 27/04/2021 added new CIDs from deposition. 10/5/2021: added transformations table. 30/8/2022: corrected [M+H]+ for BDCIPP (CID <a href="https://pubchem.ncbi.nlm.nih.gov/compound/188119#section=Collision-Cross-Section">188119</a>) to 157.35 A^2 (from 178.72) upon request of the authors (see Belova <em>et al</em>. (2022) DOI: <a href="https://doi.org/10.1016/j.aca.2022.340361">10.1016/j.aca.2022.340361</a>).</p>
Cross-sectional images from x-ray computed tomography (XCT) of conserved archaeological samples
<p>The repository contains cross-sections of 83 wood samples derived from X-ray computed tomography (CT) data. The samples are a part of the LEIZA reference collection, which were created within the framework of the project "Mass Finds in Archaeological Collections", which was funded by the "Kulturstiftung des Bundes" and the "Kulturstiftung der Länder" from 15.04.2008 to 31.12.2011 as part of the "Program for the Conservation and Restoration of Mobile Cultural Property" (KUR, see www.rgzm.de/kur).</p> <p>Around 10 years later, during the CuTAWAY project (ConservaTion And Wod AnalYses), the wood samples were digitized using an in-house laboratory X-ray CT system (Diondo d2, Germany) at HSLU with a nominal voxel size between 27 and 44 μm in order to analyse the structure of the interior. You can download the cross-sectional images of the data here. The 3D data acquisition was carried out during November 2019 - April 2021.</p> <p>The CuTAWAY project was funded by the German Research Association (DFG) and the Swiss National Science Foundation (SNSF) from 2019 to 2023 (CuTAWAY - Conservation and Wood Analyses, DFG - 416877131 and SNSF - 200021E_183684).</p>
Exploring the Impact of Physiotherapy on Health Outcomes in Elderly Patients with Chronic Diseases: A Cross-Sectional Analysis
<p>In this cross-sectional analysis, we investigate the transformative impact of physiotherapy on health outcomes among elderly patients grappling with chronic diseases. Physiotherapy emerges as a pivotal intervention, offering multifaceted benefits that extend beyond mere symptom management. Through tailored exercises, mobility enhancements, and targeted pain management strategies, physiotherapy not only mitigates physical limitations but also fosters greater independence and quality of life. By examining a diverse cohort of elderly individuals diagnosed with chronic conditions such as osteoarthritis and cardiovascular diseases, this study underscores the profound role of physiotherapy in promoting functional mobility, reducing healthcare burdens, and enhancing overall well-being among this vulnerable population."</p>
Barmur Group Cross Section High Quality
<p>Virtual outcrop model and structural interpretation. (<strong>a</strong>) Bárðarson’s (1925) original coastal section featuring the 25 marine horizons (beds 1 – 25) and 10 terrestrial horizons (beds A – J). (<strong>b</strong>) The modern analogue to Bárðarson’s coastal section created using the virtual outcrop model. The 24 key horizons used in the creation of the coastal cross-section are highlighted in this panel. F1 – F31, faults featured in the fitting the modelled stratigraphy to the virtual outcrop model. Exposure used in quantifying model fit are highlighted, orange lines represent the back projection of cross-section stratigraphy onto the virtual outcrop model used in assessing fit. (<strong>c</strong>) the coastal cross-section constructed using Petroleum Expert's MOVE software using the stratigraphical classifications of Eiríksson & Símonarson (2021).</p> <p>Also given are the Agisoft Metashape batch processing files used in the generation of the virtual outcrop model. </p>
Database of Residual Stress Measurements on Hot-rolled Wide Flange Steel Cross Sections
<p><a href="https://zenodo.org/deposit/7677600#:~:text=Delete-,Data_info.csv,-md5%3A98a0f787ce2ea1b81d42ac898f6bb110">Data_info.csv</a>: Database of 'Residual Stress Measurements on Hot-rolled Wide Flange Steel Cross Sections' including cross-sectional and material characteristics as well as information relevant to ploting the residual stress distributions.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=7%20kB-,Distributions.zip,-md5%3Af7f66a9ad607f27edde3dc7438b82ad2">Distributions.zip</a>: Residual stress distributions for the web and the flanges. To be unziped and positioned at the same location with the 'Data_info.csv', 'Processor.m' and 'QP_Coefficients' folder.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=197%20kB-,Processor.m,-md5%3A29935e24d40cbd4398d260124ec71fa8">Processor.m</a>: MATLAB code that plots the residual stress distributions of a selected research work.</p> <p><a href="https://zenodo.org/deposit/7677600#:~:text=16%20kB-,QP_Coefficients.zip,-md5%3Aafd1a8771cf39c9c6584331d10030d96">QP_Coefficients.zip</a>: Coefficients of a proposed optimization method to fit the measured residual stresses in the web and the flanges. To be unziped and positioned at the same location with the 'Data_info.csv', 'Processor.m' and 'Distributions' folder.</p>
STAR2D simulation and Hydrogen photodissociation cross sections dataset
<p>This dataset consists of two tables: extreme ultraviolet (XUV) and vacuum ultraviolet (VUV) emission spectra from a laser produced, 1 micron wavelength plasma. The emission spectra was calculated from STAR-2D simulation results. </p><p>The data consists of wavelength-dependent spectra at varying drive laser intensities from 1E11 to 1E9 W/cm2. More information on the STAR-2D code can be cited here: A. Sunahara, A. Sasaki, and K. Nishihara, J. Phys.: Conf. Ser. 112, 042048 (2008). </p><p>The second dataset consists of hydrogen photodissociation and photoionization cross section data from Heays et al: A. N. Heays, A. D. Bosman, and E. F. van Dishoeck, Astronomy &Astrophysics 602, A105 (2017).</p>
Measurement of 139La(p,x) cross sections from 35-60 MeV by stacked-target activation
<p>This repository contains all raw gamma-ray spectra analyzed for the present manuscript, as well as calibration spectra. Further details and analysis code are available on reasonable request. </p> <p>A stacked-target of natural lanthanum foils (99.9119% 139La) was irradiated using a 60 MeV proton beam at the LBNL 88-Inch Cyclotron. 139La(p,x) cross sections are reported between 35–60 MeV for nine product radionuclides. The primary motivation for this measurement was the need to quantify the production of 134Ce. As a positron-emitting analogue of the promising medical radionuclide 225Ac, 134Ce is desirable for in vivo applications of bio-distribution assays for this emerging radio-pharmaceutical. The results of this measurement were compared to the nuclear model codes TALYS, EMPIRE and ALICE (using default parameters), which showed significant deviation from the measured values.</p>
MiRoR7-P1- Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design
<p>dataset referring to </p> <p><strong>Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design</strong></p> <p> </p> <p> </p> <p>Lorenzo Bertizzolo<sup>1</sup>, Patrick M Bossuyt<sup>2</sup>, Ignacio Atal<sup>1, 5</sup>, Philippe Ravaud<sup>1, 3-6</sup>, Agnès Dechartres<sup>7</sup></p> <p> </p> <p><sup>1</sup> INSERM, U1153 Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases Team (METHODS), Paris, F-75004 France; Paris Descartes University, Sorbonne Paris Cité, France.</p> <p><sup>2</sup> Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Netherlands.</p> <p><sup>3</sup> Centre d’Épidémiologie Clinique, Hôpital Hôtel Dieu, AP-HP (Assistance Publique des Hôpitaux de Paris), Paris, France.</p> <p><sup>4</sup> Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France.</p> <p><sup>5</sup> Cochrane France, Paris, France</p> <p><sup>6</sup> Columbia University, Mailman School of Public Health, Department of Epidemiology, New York, USA</p> <p><sup>7</sup> Sorbonne Université, INSERM, Institut Pierre Louis de Santé Publique, Département Biostatistique, Santé Publique et Information Médicale, AP-HP, Hôpitaux Universitaires Pitié Salpêtrière – Charles Foix, Paris, France</p>
O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data release
<p>Raw data and codes used in M. Gacesa, R. J. Lillis, and K. J. Zahnle, "O(3P)+CO2 scattering cross sections at superthermal collision energies for planetary aeronomy", MNRAS 491, 5650-5659 (2020).</p> <ul> <li>v1.1 includes <strong>differential cross section</strong> data for inelastic scattering: O(3P)+CO2(v=0,j=ji) -> O(3P)+CO2(v=0,jf) and energy transfer to the internal degrees of freedom calculated as in Gacesa & Kharchenko, Geophys. Res. Lett. 39, L10203 (2012).</li> </ul> <p>These files are distributed under GNU General Public License v3.0 and include NO liability or warranty of any kind. No support is provided by the authors. We cannot promise to answer any questions related to this dataset nor to prepare different products for you.</p> <p>Please cite this work as: Marko Gacesa, Lillis, Robert J., & Zahnle, Kevin J. (2019). O(3P)+CO_2 scattering cross sections at superthermal collision energies for planetary aeronomy: Raw data pre-release (Version v0.9-beta) [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.3256699">http://doi.org/10.5281/zenodo.3256699</a></p>
Socioeconomic status and adiposity in childhood cancer survivors: A cross-sectional retrospective study
<p>This dataset contains information on selected indicators of socioeconomic status and anthropometric indicators of adiposity in a population of childhood cancer survivors from the Late Effect Outpatient Clinic at St. Anne's Hospital in Brno, Czech Republic. </p>
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>
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>
Prevalence of Multimorbidity among Urban–Rural Older Adults in Mongolia: A Cross-Sectional Study
<p>A face-to-face, questionnaire-based cross-sectional study was conducted with 800 valid participants aged ≥60 years in Mongolia from June to September 2023.</p>
Dataset for "Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey"
<p>Data and R code used for the analysis of data for the publication: Coumoundouros et al., Cognitive behavioural therapy self-help intervention preferences among informal caregivers of adults with chronic kidney disease: an online cross-sectional survey. BMC Nephrology</p> <p><strong>Summary of study</strong></p> <p>An online cross-sectional survey for informal caregivers (e.g. family and friends) of people living with chronic kidney disease in the United Kingdom. Study aimed to examine informal caregivers' cognitive behavioural therapy self-help intervention preferences, and describe the caregiving situation (e.g. types of care activities) and informal caregiver's mental health (depression, anxiety and stress symptoms).</p> <p>Participants were eligible to participate if they were at least 18 years old, lived in the United Kingdom, and provided unpaid care to someone living with chronic kidney disease who was at least 18 years old.</p> <p>The online survey included questions regarding (1) informal caregiver's characteristics; (2) care recipient's characteristics; (3) intervention preferences (e.g. content, delivery format); and (4) informal caregiver's mental health. Informal caregiver's mental health was assessed using the 21 item Depression, Anxiety, and Stress Scale (DASS-21), which is composed of three subscales measuring depression, anxiety, and stress, respectively.</p> <p>Sixty-five individuals participated in the survey.</p> <p>See the published article for full study details.</p> <p><strong>Description of uploaded files</strong></p> <p>1. ENTWINE_ESR14_Kidney Carer Survey Data_FULL_2022-08-30: Excel file with the complete, raw survey data. Note: the first half of participant's postal codes was collected, however this data was removed from the uploaded dataset to ensure participant anonymity.</p> <p>2. ENTWINE_ESR14_Kidney Carer Survey Data_Clean DASS-21 Data_2022-08-30: Excel file with cleaned data for the DASS-21 scale. Data cleaning involved imputation of missing data if participants were missing data for one item within a subscale of the DASS-21. Missing values were imputed by finding the mean of all other items within the relevant subscale. </p> <p>3. ENTWINE_ESR14_Kidney Carer Survey_KEY_2022-08-30: Excel file with key linking item labels in uploaded datasets with the corresponding survey question.</p> <p>4. R Code for Kidney Carer Survey_2022-08-30: R file of R code used to analyse survey data.</p> <p>5. R code for Kidney Carer Survey_PDF_2022-08-30: PDF file of R code used to analyse survey data.</p>
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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.