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5,155 results for “Data Base”
Data for: Image-based evaluation of beers at an online Pint of Science festival using Projective Mapping, Check-All-That-Apply and Acceptability
<p>Data obtained from n=67 untrained attendants at an outreach Pint of Science festival, online because of the COVID-19 pandemic but usually held at bars. The participants used images of brand logos to evaluate eight beers among the most commonly consumed in Spain. Three sensory analysis techniques were used: Projective Mapping, Acceptability and Check-All-That-Apply (CATA).</p>
Jupiter Notebook and example data for "Prospects for a camera-based detector for Neutron Reflectometry"
<p>We report the outcome of a proof-of-principle (IPTS-29165) neutron reflectivity measurement obtained using a neutron scintillator and a Photonis (brand) camera. We were motivated to test this technology because it provides much better spatial resolution and count rate capability than the BL4A <sup>3</sup>He position sensitive detector (Table 1). The report describes the detector setup, challenges encountered, a reflectivity measurement and next steps.</p> <p>Two example measurements are provided and a Jupyter Notebook to create a NumPy binary file consisting of event positions and times.</p>
Data for Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing
<p>Data for<em> Survival probabilities of atmospheric particles: comparison based on theory, cluster population simulations, and observations in Beijing </em>(https://doi.org/10.5194/acp-2022-484)</p> <p>Contact Santeri Tuovinen (santeri.tuovinen@helsinki.fi) for more details.</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
PLOS ONE – a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)
<p>This is a dataset used in and produced by research described in article "PLOS ONE - a case study of citation analysis of research papers based on the data in an open citation index (The OpenCitations Corpus)" that is translation of the original Polish text "PLOS ONE – studium przypadku analizy cytowań prac naukowych na podstawie danych otwartego indeksu cytowań (OpenCitations Corpus)" published by EBiB bulletin (2017, No 176).</p> <p>Data were extracted, as nodes (PLOS_cited_nodes.csv) and edges (PLOS_edges.csv) files from the OpenCitations Corpus (http://opencitations.net/download) on 2017.07.25 and describe all cited papers published by PLOS ONE (nodes), and all citing relations (edges). The research was conducted using Gephi (https://gephi.org/) platform so the same source data are also avaiable as GEXF file (for "one-click" import capabilities). In addition, the same data are published in NET format (but be warned that due to this format limitations, information about the publication year of papers has been lost) used by PAJEK platform, as it is very popular tool for analysis of network data.</p> <p>Published figures have prefix names corresponding to figures captions in the original paper, where they have been thoroughly discussed. This data set contains also the additional figure not published in the article, showing most cited paper with citing chains of articles of lenght not greater than 3.<br> These pictures have much better quality than those published in the article, which allows for "drill down"/zoom-in analysis and large format printing.</p>
Training data for 'Reference based RADSeq ' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes RAD-seq data from a study published by Hohelnlohe et al., 2010 (DOI:10.1371/journal.pgen.1000862) to identify and type single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations and thus estimate genetic diversity and differentiation among populations. </p>
A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on Parkinson's Disease. We provide data extracted via web scraping, along with metadata resulting from the extraction process using the NCBI API. The data pertains to the article titled 'A bibliometric study on Parkinson's Disease based on the open access data of the Michael J. Fox Foundation'.</p> <h2>Metadata Description</h2> <ul> <li> <h3>Analisys_MJFF_05_04_2024.xlsx</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>AU</td> <td>List of authors in abbreviated format.</td> <td>Text</td> </tr> <tr> <td>AF</td> <td>List of authors with full names.</td> <td>Text</td> </tr> <tr> <td>TI</td> <td>Full title of the article.</td> <td>Text</td> </tr> <tr> <td>SO</td> <td>Name of the journal or publication.</td> <td>Text</td> </tr> <tr> <td>SO_CO</td> <td>Country of origin of the publication.</td> <td>Text</td> </tr> <tr> <td>LA</td> <td>Language of the article.</td> <td>Text</td> </tr> <tr> <td>DT</td> <td>Type of document, such as "Journal Article".</td> <td>Text</td> </tr> <tr> <td>DE</td> <td>Keywords or descriptors associated with the article.</td> <td>Text</td> </tr> <tr> <td>MESH</td> <td>MeSH terms that describe the content of the article.</td> <td>Text</td> </tr> <tr> <td>DI</td> <td>Digital Object Identifier (DOI).</td> <td>Text</td> </tr> <tr> <td>PG</td> <td>Number of pages or page range.</td> <td>Numeric</td> </tr> <tr> <td>GRANT_ID</td> <td>Identification of funding, when available.</td> <td>Text</td> </tr> <tr> <td>GRANT_ORG</td> <td>Organization that provided the funding.</td> <td>Text</td> </tr> <tr> <td>UT, PMID</td> <td>Unique identifiers of the article.</td> <td>Numeric</td> </tr> <tr> <td>DB</td> <td>Name of the database where the article is indexed.</td> <td>Text</td> </tr> <tr> <td>AU_UN</td> <td>Information about the academic unit or institution of the authors.</td> <td>Text</td> </tr> </tbody> </table> <ul> <li> <h3>References_MJFF_v2_Final_Corrected.csv</h3> </li> </ul> <table> <tbody> <tr> <th>Field</th> <th>Description</th> <th>Data Type</th> </tr> </tbody> <tbody> <tr> <td>Title</td> <td>Name of the article or publication.</td> <td>Text</td> </tr> <tr> <td>Authors</td> <td>List of authors who contributed to the article.</td> <td>Text</td> </tr> <tr> <td>Journal Name</td> <td>Name of the journal or periodical where the article was published.</td> <td>Text</td> </tr> <tr> <td>Publisher</td> <td>Name of the publisher who published the article.</td> <td>Text</td> </tr> <tr> <td>Volume</td> <td>Volume number of the journal in which the article appears.</td> <td>Numeric or Text</td> </tr> <tr> <td>Edition Number</td> <td>Number of the edition of the journal in which the article is found.</td> <td>Numeric or Text</td> </tr> <tr> <td>Starting Page</td> <td>Number of the first page of the article in the publication.</td> <td>Numeric</td> </tr> <tr> <td>Ending Page</td> <td>Number of the last page of the article.</td> <td>Numeric</td> </tr> <tr> <td>Publication Date</td> <td>Date on which the article was published.</td> <td>Date</td> </tr> <tr> <td>Open Access Status</td> <td>Indicates whether the article is available in open access.</td> <td>Text</td> </tr> <tr> <td>License</td> <td>Type of license under which the article was published.</td> <td>Text</td> </tr> <tr> <td>DOI (Digital Object Identifier)</td> <td>Unique identifier for the article that provides a permanent link to the online access.</td> <td>Text</td> </tr> <tr> <td>OA Location URL</td> <td>Direct URL to the article, if available in open access.</td> <td>Text</td> </tr> <tr> <td>Citation Count</td> <td>Number of times the article has been cited by other publications.</td> <td>Numeric</td> </tr> </tbody> </table> <p> </p>
An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data
<p>This repository contains the data used in "An Objective Detection of Separation Scenario in Tropical Cyclone Trajectories Based on Ensemble Weather Forecast Data" by Oettli and Kotsuki (submitted to Journal of Geophysical Research: Atmospheres).</p>
Рис. 1. Àинамика посевных пΛощаΔей сои в Приморском крае в 1996–2018 гг. (по Δанным Àепартамента сеΛьского хозяйства и проΔовоΛьствия Приморского края) Fig. 1. The dynamic of soybean crop area at Primorsky Region in 1996–2018 (based on data from the Department of Agriculture and provision of the Primorsky Region) in Reproductive potential of Soybean Cyst Nematode Heterodera glycines - quarantine pest of soybean - in Primorsky Region conditions
Рис. 1. Àинамика посевных пΛощаΔей сои в Приморском крае в 1996–2018 гг. (по Δанным Àепартамента сеΛьского хозяйства и проΔовоΛьствия Приморского края) Fig. 1. The dynamic of soybean crop area at Primorsky Region in 1996–2018 (based on data from the Department of Agriculture and provision of the Primorsky Region)
Simulation Data & R scripts for: "Introducing recurrent events analyses to assess species interactions based on camera trap data: a comparison with time-to-first-event approaches"
<p><strong>Files descriptions:</strong></p> <p>All csv files refer to results from the different models (PAMM, AARs, Linear models, MRPPs) on each iteration of the simulation. One row being one iteration. <br>"results_perfect_detection.csv" refers to the results from the first simulation part with all the observations.<br>"results_imperfect_detection.csv" refers to the results from the first simulation part with randomly thinned observations to mimick imperfect detection.</p> <p>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>PAMM30: p-value of the PAMM running on the 30-days survey.<br>PAMM7: p-value of the PAMM running on the 7-days survey.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p>"results_int_dir_perf_det.csv" refers to the results from the second simulation part, with all the observations.<br>"results_int_dir_imperf_det.csv" refers to the results from the second simulation part, with randomly thinned observations to mimick imperfect detection.<br>ID_run: identified of the iteration (N: number of sites, D_AB: duration of the effect of A on B, D_BA: duration of the effect of B on A, AB: effect of A on B, BA: effect of B on A, Se: seed number of the iteration).<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of A on B.<br>p_pamm7_AB: p-value of the PAMM running on the 7-days survey testing for the effect of B on A.<br>AAR1: ratio value for the Avoidance-Attraction-Ratio calculating AB/BA.<br>AAR2_BAB: ratio value for the Avoidance-Attraction-Ratio calculating BAB/BB.<br>AAR2_ABA: ratio value for the Avoidance-Attraction-Ratio calculating ABA/AA.<br>Harmsen_P: p-value from the linear model with interaction Species1*Species2 from Harmsen et al. (2009).<br>Niedballa_P: p-value from the linear model comparing AB to BA (Niedballa et al. 2021).<br>Karanth_permA: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species A (Karanth et al. 2017).<br>MurphyAB_permA: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>MurphyBA_permA: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species A (Murphy et al. 2021). <br>Karanth_permB: rank of the observed interval duration median (AB and BA undifferenciated) compared to the randomized median distribution, when permuting on species B (Karanth et al. 2017).<br>MurphyAB_permB: rank of the observed AB interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br>MurphyBA_permB: rank of the observed BA interval duration median compared to the randomized median distribution, when permuting on species B (Murphy et al. 2021). <br> </p> <p><strong>Scripts files description:</strong><br>1_Functions: R script containing the functions:<br> - MRPP from Karanth et al. (2017) adapted here for time efficiency.<br> - MRPP from Murphy et al. (2021) adapted here for time efficiency.<br> - Version of the ct_to_recurrent() function from the recurrent package adapted to process parallized on the simulation datasets.<br> - The simulation() function used to simulate two species observations with reciprocal effect on each other.<br>2_Simulations: R script containing the parameters definitions for all iterations (for the two parts of the simulations), the simulation paralellization and the random thinning mimicking imperfect detection.<br>3_Approaches comparison: R script containing the fit of the different models tested on the simulated data.<br>3_1_Real data comparison: R script containing the fit of the different models tested on the real data example from Murphy et al. 2021.<br>4_Graphs: R script containing the code for plotting results from the simulation part and appendices.<br>5_1_Appendix - Check for similarity between codes for Karanth et al 2017 method: R script containing Karanth et al. (2017) and Murphy et al. (2021) codes lines and the adapted version for time-efficiency matter and a comparison to verify similarity of results.<br>5_2_Appendix - Multi-response procedure permutation difference: R script containing R code to test for difference of the MRPPs approaches according to the species on which permutation are done.</p>
AiNU data for Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
<p>This file contains the AiNU data used for the article entitled by <em>Physics-based material parameters extraction from perovskite experiments via Bayesian optimization</em> (https://arxiv.org/abs/2402.11101).</p>
Data and results for "A corpus-based study to triangulating experimental evidence regarding verb-noun association for action verbs"
<p>This repository provides spreadsheets containing the results of corpus-based and experimental studies for my undergraduate thesis titled "A corpus-based study to triangulating experimental evidence regarding verb-noun association for action verbs" (supervised by Gede Primahadi Wijaya Rajeg, PhD [main] and Ketut Santi Indriani, M.Hum. [associate]) in the Bachelor of English Literature (BoEL) program, Faculty of Humanities, Udayana University. The thesis explores convergences/divergences between different methods and data types for a set of verb-noun collocations for several action verbs and their synonyms. The description of the dataset is as follows:</p> <ol> <li>"data-raw": A raw dataset containing the results of an experiment conducted using Gorilla Experiment Builder. This consists of responses regarding verb-noun collocation co-occurrences from 17 participants into one. Link to Gorilla Experiment: (https://app.gorilla.sc/openmaterials/622948).</li> <li>"Corpus Analysis Results": A compiled data containing search results of frequencies found in the Corpus of Contemporary American English (COCA). The frequencies were compiled into tables for the five main verbs showing the number of co-occurrences of specific verb-noun collocations.</li> <li>"Experiment Results (1)": A compiled data containing the experiment results calculated as a total, showing the number of co-occurrences of specific verb-noun collocations across five verbs from the participant responses.</li> </ol> <p>The thesis is part of the pedagogical outcome of the <a title="CompLexico" href="https://www.cirhss.org/complexico/" target="_blank" rel="noopener"><em>CompLexico</em></a> research group at <a title="CIRHSS" href="https://www.cirhss.org/" target="_blank" rel="noopener"><em>CIRHSS</em></a>, and the Psycholinguistics course I took with I Made Sena Darmasetiyawan, PhD at BoEL, both in the Faculty of Humanities, Udayana University.</p>
Data and codes from "Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA"
<p><span>Data and codes used for </span><span>“Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA”</span></p>
Data for paper MethPhaser: methylation-based haplotype phasing of human genomes
<p>Data for paper MethPhaser: methylation-based haplotype phasing of human genomes. </p> <p>Files start with R9 or R10 are HG002 sample data.</p> <p>Zip files are block connection intermediate for MethPhaser. </p> <p>GTFs are block assignments. </p>
Data from: Fast mvSLOUCH: Multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies
<p>The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for a wide range of phylogenetic Gaussian models. Taking advantage of it, we redesigned the R package mvSLOUCH. Here, we demonstrate how the new version of the package can be used to thoroughly examine the evolution and adaptation of traits in a large dataset of 1252 vascular plants through the use of multivariate Ornstein-Uhlenbeck processes. The results of our analysis demonstrate the ability of the modeling framework to distinguish between various alternative hypotheses regarding the evolution of functional traits in angiosperms.</p>
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study. in Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats
TA B L E 1 Summary of model fit, based on the area under the curve (AUC) of the receiver operating characteristic (ROC) for training data, and the most important bioclimatic variables in past, present, and future (2070) Maxent models of 13 bat species included in this study.
Composition-based estimates of the thermal properties of New Zealand basement rocks: data used for calculations and figures
<p>This archive contains four files with compositional data (mineralogical and geochemical) used to estimate thermal conductivity and heat production in New Zealand basement terranes (Kirkby et al., 2024).</p> <p><br>HPR_source_data.csv - contains K, Th, and U concentrations and calculated heat production rates.</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>K_wtpct - K concentration in weight percent<br>Th_ppm - Th concentration in ppm<br>U_ppm - U concentration in ppm<br>Heat_production_uW/m3 - heat production calculated from K, Th and U concentrations, in microWatts per meter cubed<br>Terrane - name of basement terrane that sample has been assigned to<br>Source - source of data, either PetLAB (Strong et al. 2016), PMAP (Turnbull ref) or separate compilation for this study<br>Reference - Reference citation for data as listed in PetLAB/PMAP or added for this study</p> <p> </p> <p>TC_from_majors_PMAP.csv - contains thermal conductivity estimated from major element geochemistry (Kirkby et al., 2024) using method of, Jennings et al. (2019).<br>Columns are as follows:</p> <p>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_Number - sample number within the collection above<br>Sample_ID - unique sample ID in PetLAB<br>Analysis_ID - unique analysis ID in PetLAB<br>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>**_wtpct - major oxide concentration in weight percent (normalised to non-volatile component)<br>Terrane - name of basement terrane that sample has been assigned to<br>Analysis_Method - analysis method for major oxide concentrations<br>Thermal_conductivity_pred - calculated estimate of thermal conductivity based on major oxide composition, W/mK<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p><br>TC_from_modal_mineralogy.csv - contains thermal conductivity estimated from modal mineralogy (Kirkby et al., 2024).<br>Columns are as follows:</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>Analysis_ID - unique analysis ID in PetLAB<br>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_ID - sample number within the collection above<br>Subsample - in the case of thermal conductivity, sometimes two samples were measured. This column distinguishes the two measurements<br>Mineralogy_method - method of determining mineralogy, either point count or QEMSCAN<br>Quartz - percentage of quartz in sample<br>Olivine - percentage of olivine in sample<br>Pyroxene - percentage of pyroxene in sample<br>Other - percentage of other minerals in sample<br>Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024), W/mK<br>Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sanders et al (2024), W/mK<br>Porosity_measured_pct - measured porosity (percent) for samples reported by Sanders et al (2024)<br>Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity<br>Terrane - name of basement terrane that sample has been assigned to<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p><br>TC_from_normative_mineralogy.csv</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>Analysis_ID - unique analysis ID in PetLAB<br>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_ID - sample number within the collection above<br>Mineralogy_method - method of determining mineralogy, either Mesonorm or CIPWnormhb (CIPW norm with hornblende)<br>Quartz - percentage of quartz in sample<br>Olivine - percentage of olivine in sample<br>Pyroxene - percentage of pyroxene in sample<br>Other - percentage of other minerals in sample<br>Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024)<br>Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sagar et al (2022)<br>Porosity_measured_pct - measured porosity (percent) for samples reported by Sagar et al (2024)<br>Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity<br>Terrane - name of basement terrane that sample has been assigned to<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p> </p> <p>tc_by_terrane.csv</p> <p>Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024)</p> <p> </p> <p>hpr_by_terrane.csv</p> <p>Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024).</p> <p> </p> <p><br>References</p> <p>Jennings, S., Hasterok, D., & Payne, J. (2019). A new compositionally based thermal conductivity model for plutonic rocks. Geophysical Journal International, 219(2), 1377-1394. https://doi.org/10.1093/gji/ggz376<br>Kirkby, A., N. Mortimer, R. Funnell, M. Sagar, A. Seward, K. Faure and F. Sanders (2024). Composition-based estimates of the thermal properties of New Zealand basement rocks, New Zealand Journal of Geology and Geophysics.<br>Sagar, M. W., Funnell, R., Randell, K., Faure, K., Seward, A., Sanders, F., & Stratford, W. R. (2022). Physical properties of Te Riu-a-Māui / Zealandia crustal rocks: Reconnaissance study and future research. (GNS Science Internal Report 2022/05. <br>Sanders, F., Seward, A., Sagar, M., & Faure, K. (2024). Thermal Properties of Zealandia Basement Rocks: results of Thermal Conductivity Scanner measurements 2023. Lower Hutt. (GNS Science Report. <br>Strong, D. T., Turnbull, R. E., Haubrock, S., & Mortimer, N. (2016). Petlab: New Zealand’s national rock catalogue and geoanalytical database. New Zealand Journal of Geology and Geophysics, 59(3), 475-481. https://doi.org/10.1080/00288306.2016.1157086 </p>
Data files for "Quantifying the global climate feedback from energy-based adaptation"
<p>Data files for "Quantifying the global climate feedback from energy-based adaptation".</p> <p>Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/ACDM_Climate_Adaptation_Feedback.</p> <p>Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p> <p> </p> <p><strong>Attribution:</strong></p> <p><br>Some processed data contain excerpts of Non-Creative Commons Material as defined by the International Energy Agency (IEA -- see their terms of use at `https://www.iea.org/terms/terms-of-use-for-non-cc-material'). The emissions factors we use in our analysis are generated using IEA datasets. These data are aggregates of the underlying country-by-fuel level emissions factors and as presented contain only insubstantial amounts of the Non-CC Material. We attest they cannot be used to reconstruct individual data points in the original dataset. The factors we produce are attributable to the following two sources: </p> <p>IEA. Emissions factors. Tech. Rep., International Energy Agency (IEA 2021). URL https://www.iea.org/data-and-statistics/data-product/910emissions-factors-2021. All Rights Reserved.</p> <p>IEA. World energy balances 2021. Tech. Rep., International Energy Agency (IEA) (2022). URL https://www.iea.org/data-and-statistics/data-product/world-energy-balances. All Rights Reserved.</p> <p> </p>
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
<p>Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. <em>In vitro</em> human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing <em>in vitro</em> and <em>in vivo</em> models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted <em>in vitro</em> to <em>in vivo</em> therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (<em>i.e.</em>, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (<em>i.e., </em>quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable <em>in vitro</em> proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.</p>
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.