Find research datasets worth reusing

Figure 69 b in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report

Figure 69 b

Figure 48 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report

Figure 48: The three forms of Salvelinus in Swiss lakes described by Konrad Gessner in 1575.

Figure 57 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report

Figure 57: Extremes of variation in fin colour in Perca fluviatilis within and among lakes.

Figure 60 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report

Figure 60: An example of a littoral habitat map from Lake Bret.

Figure 2 in Diversity, distribution and community composition of fish in perialpine lakes – "Projet Lac" synthesis report

Figure 2: Example of the distribution of sampling actions throughout a lake (Constance Untersee).

opencc-by-4.0Dec 2021View details →

Dataset- Self-reported periodontal disease, dental presence and COVID-19, in older adults of the Playa municipality

Esta base de datos fue creada con el objetivo de determinar la relación existente entre la enfermedad periodontal autoinformada, la perdida dentaria y el comportamiento de la covid-19. ---------------------------------------------------------------------------------ENG This database was created with the objective of determining the relationship between self-reported periodontal disease, tooth loss and the behavior of covid-19.

opencc-by-4.0Jan 2022View details →

An Evaluation of Self-reported Level of Entrustment across the First Year of Clinical Practice [dataset]

This file contains the data files from each of the four surveys (Times 1 to 4), and the questionnaires used to collect the data at Time 1, and Times 2-3. Background. Newly graduated medical students often report that they lack the skills required to care for patients, and feel unprepared for clinical practice. However, little is known about when, and if, they acquire these skills in practice. Objective. To assess self-reported level of entrustment in, and frequency of performance of, the seven Entrustable Professional Activities (EPAs) comprising the EPA framework for interns in Ireland. EPAs describe discrete activities that are essential to a particular profession. Methods. Self-report survey data were collected from doctors in the Republic of Ireland during their first year of clinical practice at four different time points during 2020/21. Results. Response rates to the survey varied from 73.3% (126/172) at Time 1 to 25.6% (44/172) at Time 4. After 3 months, the respondents reported that they could execute all of the EPAs, inclusive of 12 essential procedural skills, with indirect supervision. As the year progressed there was an increase in the proportion of respondents reporting that they performed the EPAs at least once a week. However, the proportion of respondents performing five of the essential procedural skills (e.g. nasogastric tube insertion) remained low across all time points. Conclusion. Consideration should be given as to how to better prepare medical students to execute these EPAs, how the interns can be better supported during the first quarter of internship. The findings from this research are positive. However, there is an urgent need to carry out formal assessments of entrustability, rather than relying on self-report.

opencc-by-4.0Jan 2022View details →

Dataset of "Assessment of transparency and selective reporting of interventional trials studying colorectal cancer"

This is the dataset used for our work "Assessment of transparency and selective reporting of interventional trials studying colorectal cancer".  Trials included were identified through ClinicalTrials.gov. This dataset is presented in the form of an excel sheet with each line corresponding to a different trial (NCT number).

opencc-by-4.0Jan 2022View details →

Interactive VISION reports for "KP-Tracer Tumors from study "Lineage Recording Reveals the Phylodynamics, Plasticity and Paths of Tumor Evolution"

This repository contains VISION reports for the KP-Tracer tumors described in the manuscript "Lineage Recording Reveals the Phylodynamics, Plasticity, and Paths of Tumor Evolution" (Yang*, Jones*, et al bioRxiv 2021). In this study, single-cell lineage tracing was performed in the KP autochthonous mouse model of non-small-cell lung cancer. Tumors were initiated with Cre recombinase (and optionally an additional gRNA targeting the other well-studied tumor suppressors) and allowed to grow for approximately 4-6 months at which point mice were sacrificed and tumors harvested. After purifying cancer cells by fluorescent markers, cells were profiled with the 10X chromium platform and four libraries were collected: a single-cell transcriptome library, a single-cell lineage tracing library, a single-cell multiplexing library, and a single-cell lenti-barcode library (used for confirming the clonality of tumors). Data was processed using the 10X Cellranger suite and custom pipelines as described in the manuscript above. For interactive work with these objects, users must first install VISION from Github (https://github.com/YosefLab/VISION) and then launch these reports either locally or on a server. For detailed instructions on how to launch reports, and create new reports for additional clones, please view our tutorials in our public reproducibility repository at https://github.com/mattjones315/KPTracer-release.

openother-openJan 2022View details →

Data and R code - ISRR11/Rooting2021 Meeting Report

This repository contains the raw data and R code used to analyse the results of the online root phenotyping survey that was created and disseminated by ISRR Ambassadors during the ISRR11/Rooting2021 meeting.

dryad36/100

Patient-reported outcomes among people living with HIV on single- versus multi-tablet regimens

Background: Single-tablet regimens (STR) have become the golden standard in antiretroviral therapy. However, there are calls to reintroduce (generic) components as multi-tablet regimens (MTR) because of cost-savings. Patient-reported outcome measures (PROs) are an important touchstone to support or to renounce this proposal. As such, our aim was to compare PROs of people living with HIV taking an STR versus a MTR in a real world setting. Materials and methods: 188 people living with HIV participated in a longitudinal study. 132 remained on their MTR and 56 switched to an STR. At baseline, months 1-3-6-12-18 and 24, participants filled in questionnaires on health-related quality of life (HRQoL), depressive symptoms, HIV symptoms, neurocognitive complaints (NCC) treatment satisfaction and adherence. Generalized linear mixed models and generalized estimation equations mixed models were built. Results: At baseline, the two groups did not differ in clinical parameters and PROs. The mixed models revealed differences among the groups regarding neurocognitive complaints and treatment satisfaction. The odds of having NCC increased by 4,1% per month in the STR-group as compared to the MTR-group (p=0.035). Nonetheless, treatment satisfaction also increased in the STR-group: at month 6, median change score was highly positive: 24 (IQR 7,5-29). Treatment satisfaction showed a significantly different evolution in the groups: the estimated state score increased by 3,3 in the STR-group while it decreased by 0,2 in the MTR-group (p=0.003). HRQoL measures, HIV symptoms, depressive symptoms and adherence did not differ between the groups over time. Conclusions: Neurocognitive complaints and treatment satisfaction differed between the two groups. More people on an STR reported NCC over time compared to the MTR-group. Contrarily, treatment satisfaction in the STR-group increased significantly over time. It should be further investigated to which extent treatment satisfaction is a predictor for long-term treatment persistence and health outcomes.

opencc-zeroJan 2022View details →

Khirbet el Eika Supplements for the zooarchaeological report (2022)

Zooarchaeological supplement that includes (1) the full database with faunal identifications, and (2) comparanda of Hellenistic assemblages from Israel.

LIVERUR - Database for for elaboration of the Report on existing business models in EU countries and regions

Database for for elaboration of the Report on existing business models in EU countries and regions within the WP2: Conceptualization of existing rural business models in EU and regional areas.

Content Validity and Reliability of the Italian Language Version of the US National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE®)

ABSTRACT Introduction: The US National Cancer Institute’s (NCI) Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE®) is a library of 78 symptom terms and 124 items enabling patient reporting of symptomatic adverse events in cancer trials. This multicenter study used mixed methods to develop an Italian language version of this widely accepted measure, and evaluate content validity and reliability in a diverse sample of Italian-speaking patients. Methods: All PRO-CTCAE items were translated in accordance with international guidelines. Subsequently, the content validity of the PRO-CTCAE-Italian was examined and iteratively refined through cognitive debriefing interviews. Participants (n=96; 52% male; median age 64 years; 26% older adults; 18% lower educational attainment) completed a PRO-CTCAE survey and participated in a semi-structured interview to determine if the translation captured the concepts of the original English language PRO-CTCAE, and to evaluate comprehension, clarity and ease of judgement. Test-retest reliability of the finalized measure was evaluated in a second sample (n=135). Results: Four rounds of cognitive debriefing interviews were conducted. The majority of PRO-CTCAE symptom terms, attributes and associated response choices were well-understood, and respondents found the items easy to judge.  To improve comprehension and clarity, the symptom terms for nausea and pain were rephrased and retested in subsequent interview rounds.  Test-retest reliability was excellent for 41/49 items (84%); the median intraclass correlation coefficient was 0.83 (range 0.64-0.94). Discussion: Results support the semantic, conceptual and pragmatic equivalence of PRO-CTCAE-Italian to the original English version, and provide preliminary evidence of content validity and reliability.

Excel mapping tools for 2021 zoonoses data reporting

The main objective of the mapping tools is to provide a simple and useable platform for Member states and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the Data Collection Framework (DCF). The catalogues and the specific hierarchy of each data model (PRV, FBO, AP, DS and SSD2 for Echinococcus multilocularis) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the five data models are available. Dynamic or manual version of the tool can be chosen for the first four data models. The Emulti_SSD2_tool can be used to report sample-based results of Echinococcus multilocularis under the Commission Delegated Regulation (EU) 2018/772.

opencc-by-4.0Dec 2021View details →

Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines

Code and data for Section 2 of the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines Versions: Version 1.1 This one: <ul> <li>updated region names</li> </ul> Version 1.0 <a href="https://doi.org/10.5281/zenodo.5951626">https://doi.org/10.5281/zenodo.5951626</a> This repository contains the code and data needed to produce the trajectories, projections, and observations for the Interagency report: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. The report can be found on <a href="https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html">https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report-sections.html</a> An interactive tool to study the observations, trajectories, and scenarios can be accessed from <a href="https://sealevel.nasa.gov/task-force-scenario-tool">https://sealevel.nasa.gov/task-force-scenario-tool</a> Frequently-asked questions: <a href="https://sealevel.nasa.gov/faq/16/">https://sealevel.nasa.gov/faq/16/</a> Authors <ul> <li>William V. Sweet, NOAA National Ocean Service</li> <li>Benjamin D. Hamlington, NASA Jet Propulsion Laboratory</li> <li>Robert E. Kopp, Rutgers University</li> <li>Christopher P. Weaver, U.S. Environmental Protection Agency</li> <li>Patrick L. Barnard, U.S. Geological Survey</li> <li>Michael Craghan, U.S. Environmental Protection Agency</li> <li>Gregory Dusek, NOAA National Ocean Service</li> <li>Thomas Frederikse, NASA Jet Propulsion Laboratory</li> <li>Gregory Garner, Rutgers University</li> <li>Ayesha S. Genz, University of Hawai‘i at Mānoa, Cooperative Institute for Marine and Atmospheric Research</li> <li>John P. Krasting, NOAA Geophysical Fluid Dynamics Laboratory</li> <li>Eric Larour, NASA Jet Propulsion Laboratory</li> <li>Doug Marcy, NOAA National Ocean Service</li> <li>John J. Marra, NOAA National Centers for Environmental Information</li> <li>Jayantha Obeysekera, Florida International University</li> <li>Mark Osler, NOAA National Ocean Service</li> <li>Matthew Pendleton, Lynker</li> <li>Daniel Roman, NOAA National Ocean Service</li> <li>Lauren Schmied, FEMA Risk Management Directorate</li> <li>William C. Veatch, U.S. Army Corps of Engineers</li> <li>Kathleen D. White, U.S. Department of Defense</li> <li>Casey Zuzak, FEMA Risk Management Directorate</li> </ul> Contents This data and code set contains the following directories: Results The <code>Results</code> folder contains the resulting projections, trajectories and observations from the report. <ul> <li><code>TR_global_projections.nc</code>: GMSL projections, trajectory, and observations</li> <li><code>TR_regional_projections.nc</code>: Regional observations, projections and trajectories</li> <li><code>TR_local_projections.nc</code>: Local observations, projections and trajectories</li> <li><code>TR_gridded_projections.nc</code>: Gridded projections</li> </ul> These files are in the NetCDF forrmat. To read the NetCDF files, many free software packages are available, including <a href="http://meteora.ucsd.edu/~pierce/ncview_home_page.html">ncview</a> and <a href="https://www.giss.nasa.gov/tools/panoply/">Panoply</a>. Free NetCDF packages are available to directly import the data into <a href="https://github.com/Alexander-Barth/NCDatasets.jl">Julia</a> and <a href="https://unidata.github.io/netcdf4-python/">Python</a> code. Code The <code>Code</code> folder contains all the computer code used to read and analyze the observations and the projections, and to generate the trajectories. To run this code, you need <a href="https://julialang.org/">Julia</a>. The code requires the Julia packages <code>CSV</code>, <code>Interpolations</code>, <code>JSON</code>, <code>LoopVectorization</code>, <code>MAT</code>, <code>NCDatasets</code>, <code>NetCDF</code>, <code>Plots</code>, <code>XLSX</code>, <code>LinearAlgebra</code>, and <code>Statistics</code>. They can be installed by pressing <code>]</code> at the Julia REPL and typing: <pre><code>add CSV Interpolations JSON LoopVectorization MAT NCDatasets NetCDF Plots XLSX LinearAlgebra Statistics </code></pre> This program also requires <a href="http://segal.ubi.pt/hector/">Hector</a>. Hector needs to be installed or compiled. In the file <code>Hector.jl</code> update the path to the Hector executable on lines 30 and 104. Run <code>Run_TR.jl</code> in the REPL or run <code>julia Run_TR.jl</code> from the command line to run the projections. The projections are then written to the <code>.\Data</code> directory. The folder contains the following files: <ul> <li><code>Run_TR.jl</code>: This is the main routine that (eventually) calls all the functions to compute the projections.</li> <li><code>ConvertNCA5ToGrid.jl</code>: Converts the original NCA5 projections to a set of netCDF files that's used throughout this code</li> <li><code>ProcessObservations.jl</code>: Reads and processes the tide-gauge and altimetry observations</li> <li><code>GlobalProjections.jl</code>: Reads and processes the GMSL observations and projections, and computes the trajectory</li> <li><code>RegionalProjections.jl</code>: Reads and processes the regional projections and computes the trajectories</li> <li><code>LocalProjections.jl</code>: Reads and processes the local projections at the tide-gauge locations and computes the trajectories</li> <li><code>GriddedProjections.jl</code>: Reads the gridded NCA5 projections and add a GMSL baseline correction for the 2005 vs 2000 baseline</li> <li><code>SaveFigureData.jl</code>: Reads the results and writes text files for GMT</li> <li><code>Hector.jl</code>: Wrapper for <a href="http://segal.ubi.pt/hector/">Hector</a>, used to compute trends and uncertainties.</li> <li><code>Masks.jl</code>: Defines the region masks for each region.</li> </ul> Data The <code>Data</code> directory contains the input data sets used during the computations. Please appropriately cite the input data if you use it. It contains the following: Directories: <ul> <li><code>ClimIdx</code>: Map with climate indices (NAO, PDO, MEI) used to remove internal variability. All the indices come from NOAA <a href="https://psl.noaa.gov/data/climateindices/">Physical Sciences Laboratory (PSL)</a> and <a href="https://www.cpc.ncep.noaa.gov/data/teledoc/telecontents.shtml">NOAA Climate Prediction Centre (CPC)</a></li> <li><code>NCA5_projections</code> Contains the NCA5 projections for each scenario (Low, IntLow, Int, IntHigh, and High). For each scenario, the GMSL projections, projections at tide-gauge locations and on a 1-degree grid are provided.</li> </ul> Files: <ul> <li><code>basin_codes.nc</code>: Map with basin codes. from Eric Leuliette/NOAA. Data provided by the NOAA Laboratory for Satellite Altimetry.</li> <li><code>CDS_monthly_1993_2020.nc</code>: Monthly-mean sea level (1993-2020) from gridded altimetry. Obtained from <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global">Copernicus Climate Data Store</a>. This dataset contains modified Copernicus Climate Change Service information [2020]</li> <li><code>enso_correction.mat</code>: GMSL correction for ENSO/PDO from Hamlington, B. D., Frederikse, T., Nerem, R. S., Fasullo, J. T., & Adhikari, S. (2020). Investigating the Acceleration of Regional Sea‐level Rise During the Satellite Altimeter Era. Geophysical Research Letters. <a href="https://doi.org/10.1029/2019GL086528">https://doi.org/10.1029/2019GL086528</a></li> <li><code>filelist_psmsl.txt</code>: List with PSMSL file names and PSMSL IDs. Obtained from the Permanent Service for Mean Sea Level (<a href="http://www.psmsl.org/">PSMSL</a>), 2021, Retrieved 29 Nov 2021. Simon J. Holgate, Andrew Matthews, Philip L. Woodworth, Lesley J. Rickards, Mark E. Tamisiea, Elizabeth Bradshaw, Peter R. Foden, Kathleen M. Gordon, Svetlana Jevrejeva, and Jeff Pugh (2013) New Data Systems and Products at the Permanent Service for Mean Sea Level. Journal of Coastal Research: Volume 29, Issue 3: pp. 493 – 504. <a href="https://doi.org/:10.2112/JCOASTRES-D-12-00175.1">https://doi.org/:10.2112/JCOASTRES-D-12-00175.1</a>.</li> <li><code>GEBCO_bathymetry_05.nc</code>: Bathymetry map of the global oceans from the General Bathymetric Chart of the Oceans (<a href="https://www.gebco.net/">GEBCO</a>). Source: GEBCO Compilation Group (2021) GEBCO 2021 Grid (<code>doi:10.5285/c6612cbe-50b3-0cff-e053-6c86abc09f8f</code>) The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>GIA_Caron_stats_05.nc</code>: Glacial Isostatic Adjustment estimates from Caron, L., Ivins, E. R., Larour, E., Adhikari, S., Nilsson, J., & Blewitt, G. (2018). GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science. Geophysical Research Letters, 45(5), 2203–2212. <a href="https://doi.org/10.1002/2017GL076644">https://doi.org/10.1002/2017GL076644</a>. The source data have been re-gridded onto a 0.5 degree grid.</li> <li><code>global_timeseries_measures.nc</code>: Time series of estimated 20th-century GMSL and its components, based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_ensembles.nc</code>: Ensemble GMSL reconstruction from tide-gauges based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>GMSL_TPJAOS_5.0_199209_202106.txt</code>: Global Mean Sea Level Trend from Integrated Multi-Mission Ocean Altimeters TOPEX/Poseidon, Jason-1, OSTM/Jason-2, and Jason-3 Version 5.1 [Data set]. NASA Physical Oceanography DAAC. <a href="https://doi.org/10.5067/GMSLM-TJ151">https://doi.org/10.5067/GMSLM-TJ151</a>. This altimetry dataset uses the methods as described in Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., & Ray, R. D. (2017). On the “Cal-Mode” Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371–8384. <a href="https://doi.org/10.1002/2017JC013090">https://doi.org/10.1002/2017JC013090</a></li> <li><code>grd_1992_2020.nc</code>: Seafloor deformation due to contemporary GRD effects based on Frederikse, T., Landerer, F., Caron, L., Adhikari, S., Parkes, D., Humphrey, V. W., Dangendorf, S., Hogarth, P., Zanna, L., Cheng, L., & Wu, Y.-H. (2020). The causes of sea-level rise since 1900. Nature, 584(7821), 393–397. <a href="https://doi.org/10.1038/s41586-020-2591-3">https://doi.org/10.1038/s41586-020-2591-3</a></li> <li><code>region_mask.nc</code>: Mask with the definition of all regions.</li> <li><code>US_tg_monthly.xlsx</code>: Tide gauge observations from the NOAA tide gauge network</li> </ul> GMT This directory contains the <a href="https://www.generic-mapping-tools.org/">GMT</a> scripts to make Figures 1.2, 2.1, 2.2, 2.6, and A.1.2 from the report. To generate the figures, make sure GMT is installed and run the Shell script in each directory.

Figure 7. Male Xanthofreya albosignata. A in First report of Eustiromastix spinipes (Taczanowski 1872) (Araneae: Salticidae: Saltafresia) from Colombia, with new salticid records for the Department of Córdoba

Figure 7. Male Xanthofreya albosignata. A, habitus, dorsal view. B-D, Right pedipalp, ventral (B), retrolateral (C) and prolateral (D) views.

Figure 5 in First report of Eustiromastix spinipes (Taczanowski 1872) (Araneae: Salticidae: Saltafresia) from Colombia, with new salticid records for the Department of Córdoba

Figure 5. Female Eustiromastix spinipes (Taczanowski, 1872). A, Epigyne, dorsal view. B, Epigyne, ventral view. CD= copulatory duct; FD= fertilization duct; Sp= Spermatheca.

Figure 6. Male Pachomius dybowskii. A in First report of Eustiromastix spinipes (Taczanowski 1872) (Araneae: Salticidae: Saltafresia) from Colombia, with new salticid records for the Department of Córdoba

Figure 6. Male Pachomius dybowskii. A, habitus, dorsal view. B-C, Left pedipalp, ventral (B) and retrolateral (C) views.

Figure 4. Female Eustiromastix spinipes. A in First report of Eustiromastix spinipes (Taczanowski 1872) (Araneae: Salticidae: Saltafresia) from Colombia, with new salticid records for the Department of Córdoba

Figure 4. Female Eustiromastix spinipes. A, Habitus, dorsal view. B, Chelicerae, posterior aspect. C-E, Epigyne, (C) ventral view, (D), same, cleared, (E) dorsal view, cleared.