cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20200337.v2
title: Evidence for reproductive senescence across ray-finned fishes: A review

description: The review is focused on examining evidence for reproductive senescence in fish. We defined three selection criteria for assessing an article to be suitable for our review. The article must have been on ray-finned fish (Actinopterygii), age of the fish has been determined and the study must have looked at a reproductive trait in relationship with age of the fish.<br> We compiled a collection of articles based on search in Web of Science and Scopus databases. In the second stage, we performed thorough screen of full-texts of the pre-selected articles and extracted detailed information. This information was summarized for writing the review.<br> Attached is the original article collection with 29 317 entries. Then we include collection after the initial screen and subsequent identification of unsuitable and duplicate entries (1 249 entries). The file of records of the reproductive traits (multiple triats per study) contains 409 entries. We also added annotation of the final records file for completeness and PRISMA flowchart to show the steps and the number of entries during our literature search and data retrieval.
DOI: 10.6084/m9.figshare.20200337.v1
title: Evidence for reproductive senescence across ray-finned fishes: A review

description: The review is focused on examining evidence for reproductive senescence in fish. We defined three selection criteria for assessing an article to be suitable for our review. The article must have been on ray-finned fish (Actinopterygii), age of the fish has been determined and the study must have looked at a reproductive trait in relationship with age of the fish.<br> We compiled a collection of articles based on search in Web of Science and Scopus databases. In the second stage, we performed thorough screen of full-texts of the pre-selected articles and extracted detailed information. This information was summarized for writing the review.<br> Attached is the original article collection with 29 317 entries. Then we include collection after the initial screen and subsequent identification of unsuitable and duplicate entries (1 249 entries). The file of records of the reproductive traits (multiple triats per study) contains 409 entries. We also added annotation of the final records file for completeness and PRISMA flowchart to show the steps and the number of entries during our literature search and data retrieval.
DOI: 10.5281/zenodo.7411077
title: High frequency (tick data) of historical FOREX prices

description: Price tick data for the most liquid Forex assets (AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, USDJPY). The period covered 09 March 2020 to 07, September 2022.
DOI: 10.5281/zenodo.7411078
title: High frequency (tick data) of historical FOREX prices

description: Price tick data for the most liquid Forex assets (AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, USDJPY). The period covered 09 March 2020 to 07, September 2022.
DOI: 10.5281/zenodo.7411148
title: High frequency (tick data) of historical FOREX prices

description: Price tick data for the most liquid Forex assets (AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, USDJPY). The period covered 09 March 2020 to 07, September 2022.
DOI: 10.5519/qd.7pffdcso
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (51 records)
DOI: 10.5517/ccdc.csd.cc28c4h9
title: CCDC 2096080: Experimental Crystal Structure Determination

description: Related Article: Qingbao Gong, Xinfu Zhang, Wanwan Li, Xing Guo, Qinghua Wu, Changjiang Yu, Lijuan Jiao, Yi Xiao, Erhong Hao|2022|J.Am.Chem.Soc.|144|21992|doi:10.1021/jacs.2c08947
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21695528
title: Mykrobe panel typhi version 20221208

description: This is typhi panel data version 20221208, to be used by the command line tool mykrobe.
DOI: 10.5281/zenodo.7252282
title: A grid of mass loss rates from catestrophically evaporating rocky planets

description: The code and data associated with the paper "Dust formation in the outflows of catastrophically evaporating planets" by Booth, Owen & Schulik (2023). Contains: aiolos.tar.gz: The code used to generate the models mass_loss_grid.tar.gz: The grid of mass-loss rates from planets of different masses and surface temperatures. Note the mass-loss rates are computed assuming the stellar properties of Kepler-1520.
description: {"references": ["Booth, Richard et al. (2023). Arxiv version: https://arxiv.org/abs/2204.13117"]}
DOI: 10.5281/zenodo.7252281
title: A grid of mass loss rates from catestrophically evaporating rocky planets

description: The code and data associated with the paper "Dust formation in the outflows of catastrophically evaporating planets" by Booth, Owen & Schulik (2023). Contains: aiolos.tar.gz: The code used to generate the models mass_loss_grid.tar.gz: The grid of mass-loss rates from planets of different masses and surface temperatures. Note the mass-loss rates are computed assuming the stellar properties of Kepler-1520.
description: {"references": ["Booth, Richard et al. (2023). Arxiv version: https://arxiv.org/abs/2204.13117"]}
DOI: 10.5519/qd.psqcwxe1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (758 records)
DOI: 10.6084/m9.figshare.21314132
title: Utility of preclinical species for uncertainty assessment and correction of prediction of human volume of distribution using the Rodgers-Lukacova model

description: Prediction of rat, dog, monkey, and human volume of distribution (VD<sub>ss</sub>) by Rodgers-Lukacova model was evaluated using a data set of more than 100 compounds.The prediction accuracy was best for humans followed by monkeys and dogs with 59, 52, and 41% of compounds within 2-fold, respectively.The accuracy of predictions in preclinical species was indicative of the human situation. This was particularly true for monkeys, where 87% of the compounds that were predicted within 2-fold in monkeys were also predicted within 2-fold in humans.The model's tendency to underestimate VD<sub>ss</sub> was higher in rats and dogs compared to humans and monkeys for all ion classes but zwitterions. Hence, correction of human predictions using prediction errors in rats and dogs resulted in overestimation of VD<sub>ss</sub>.The model had a similar degree of underestimation in humans and monkeys. Correction using monkeys improved the accuracy of the human estimate, especially for basic and zwitterion compounds.A strategy is proposed based on the accuracy of prediction in monkey and monkey scalars for prediction and prospective assessment of the accuracy of human VD<sub>ss</sub>. Prediction of rat, dog, monkey, and human volume of distribution (VD<sub>ss</sub>) by Rodgers-Lukacova model was evaluated using a data set of more than 100 compounds. The prediction accuracy was best for humans followed by monkeys and dogs with 59, 52, and 41% of compounds within 2-fold, respectively. The accuracy of predictions in preclinical species was indicative of the human situation. This was particularly true for monkeys, where 87% of the compounds that were predicted within 2-fold in monkeys were also predicted within 2-fold in humans. The model's tendency to underestimate VD<sub>ss</sub> was higher in rats and dogs compared to humans and monkeys for all ion classes but zwitterions. Hence, correction of human predictions using prediction errors in rats and dogs resulted in overestimation of VD<sub>ss</sub>. The model had a similar degree of underestimation in humans and monkeys. Correction using monkeys improved the accuracy of the human estimate, especially for basic and zwitterion compounds. A strategy is proposed based on the accuracy of prediction in monkey and monkey scalars for prediction and prospective assessment of the accuracy of human VD<sub>ss</sub>.
DOI: 10.6084/m9.figshare.21387459
title: Do differences in cell lines and methods used for calculation of IC<sub>50</sub> values influence categorisation of drugs as P-glycoprotein substrates and inhibitors?

description: <i>In vitro</i> bidirectional assays are employed to determine whether a drug is a substrate and/or inhibitor of P-glycoprotein (P-gp) transport. Differences between cell lines and calculation methods can lead to variations in the determination of efflux ratios (ER) and IC<sub>50</sub> values used to classify a drug as a P-gp substrate and inhibitor, respectively.Information was collected from the literature on ER and IC<sub>50</sub> values with digoxin as the probe substrate using different cell lines and inhibition calculation methods. Predictive performance was evaluated by comparing [I<sub>gut</sub>]/IC<sub>50</sub> ratios versus reported <i>in vivo</i> results.For known P-gp substrates, 50% of the drugs had their highest ER value in MDCK-MDR1 cells while 81% had their lowest ER value in Caco-2 cells. For 30 drugs with inhibition data, lower mean IC<sub>50</sub> values were often observed with the Caco-2 cells and calculations based on ER. Based on the cut-off criteria of [I<sub>gut</sub>]/IC<sub>50</sub> ≥ 10, there were no significant differences in positive or negative predictive values based on either cell line or calculation method for the drugs.Within this limited dataset, differences between cell lines or IC<sub>50</sub> calculation methods do not seem to impact the prediction of <i>in vivo</i> P-gp inhibitor classification. <i>In vitro</i> bidirectional assays are employed to determine whether a drug is a substrate and/or inhibitor of P-glycoprotein (P-gp) transport. Differences between cell lines and calculation methods can lead to variations in the determination of efflux ratios (ER) and IC<sub>50</sub> values used to classify a drug as a P-gp substrate and inhibitor, respectively. Information was collected from the literature on ER and IC<sub>50</sub> values with digoxin as the probe substrate using different cell lines and inhibition calculation methods. Predictive performance was evaluated by comparing [I<sub>gut</sub>]/IC<sub>50</sub> ratios versus reported <i>in vivo</i> results. For known P-gp substrates, 50% of the drugs had their highest ER value in MDCK-MDR1 cells while 81% had their lowest ER value in Caco-2 cells. For 30 drugs with inhibition data, lower mean IC<sub>50</sub> values were often observed with the Caco-2 cells and calculations based on ER. Based on the cut-off criteria of [I<sub>gut</sub>]/IC<sub>50</sub> ≥ 10, there were no significant differences in positive or negative predictive values based on either cell line or calculation method for the drugs. Within this limited dataset, differences between cell lines or IC<sub>50</sub> calculation methods do not seem to impact the prediction of <i>in vivo</i> P-gp inhibitor classification.
DOI: 10.6084/m9.figshare.21314132.v1
title: Utility of preclinical species for uncertainty assessment and correction of prediction of human volume of distribution using the Rodgers-Lukacova model

description: Prediction of rat, dog, monkey, and human volume of distribution (VD<sub>ss</sub>) by Rodgers-Lukacova model was evaluated using a data set of more than 100 compounds.The prediction accuracy was best for humans followed by monkeys and dogs with 59, 52, and 41% of compounds within 2-fold, respectively.The accuracy of predictions in preclinical species was indicative of the human situation. This was particularly true for monkeys, where 87% of the compounds that were predicted within 2-fold in monkeys were also predicted within 2-fold in humans.The model's tendency to underestimate VD<sub>ss</sub> was higher in rats and dogs compared to humans and monkeys for all ion classes but zwitterions. Hence, correction of human predictions using prediction errors in rats and dogs resulted in overestimation of VD<sub>ss</sub>.The model had a similar degree of underestimation in humans and monkeys. Correction using monkeys improved the accuracy of the human estimate, especially for basic and zwitterion compounds.A strategy is proposed based on the accuracy of prediction in monkey and monkey scalars for prediction and prospective assessment of the accuracy of human VD<sub>ss</sub>. Prediction of rat, dog, monkey, and human volume of distribution (VD<sub>ss</sub>) by Rodgers-Lukacova model was evaluated using a data set of more than 100 compounds. The prediction accuracy was best for humans followed by monkeys and dogs with 59, 52, and 41% of compounds within 2-fold, respectively. The accuracy of predictions in preclinical species was indicative of the human situation. This was particularly true for monkeys, where 87% of the compounds that were predicted within 2-fold in monkeys were also predicted within 2-fold in humans. The model's tendency to underestimate VD<sub>ss</sub> was higher in rats and dogs compared to humans and monkeys for all ion classes but zwitterions. Hence, correction of human predictions using prediction errors in rats and dogs resulted in overestimation of VD<sub>ss</sub>. The model had a similar degree of underestimation in humans and monkeys. Correction using monkeys improved the accuracy of the human estimate, especially for basic and zwitterion compounds. A strategy is proposed based on the accuracy of prediction in monkey and monkey scalars for prediction and prospective assessment of the accuracy of human VD<sub>ss</sub>.
DOI: 10.7910/dvn/2drkkn
title: La Revista de la AUGC / Asociación Unificada de Guardias Civiles

description: Se trata de la Revista de la AUGC / Asociación Unificada de Guardias Civiles. En las que contiene artículos de investigación y de divulgación científica.
DOI: 10.11588/data/wi9184/jmgkso
title: HTR_Train_Set_Pracalit_for_Sanskrit_and_Newar_MSS_16th_to_19th_C.zip

description: :unav
DOI: 10.25403/upresearchdata.21640706
title: Traits of African acacias

description: This data is for trait descriptions of each African <em>Senegalia </em>and <em>Vachellia </em>species native to African savannas were compiled using field guides of trees and shrubs. Supplemented images were used to estimate architectural traits. Information on how each trait was measured or calculated is included in the metadata tab of the excel file. Estimates of the missing values were imputed in RStudio.
DOI: 10.25403/upresearchdata.21640706.v1
title: Traits of African acacias

description: This data is for trait descriptions of each African <em>Senegalia </em>and <em>Vachellia </em>species native to African savannas were compiled using field guides of trees and shrubs. Supplemented images were used to estimate architectural traits. Information on how each trait was measured or calculated is included in the metadata tab of the excel file. Estimates of the missing values were imputed in RStudio.
DOI: 10.34740/kaggle/dsv/4687803
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.21085042
title: RDF triplets for the graph Les Miserables

description: The extracted RDF triplets from the graph Les Miserables (The coappearance network of characters in the novel). We consider three types of property: structural property, computed property, and semantic property. These data can be imported into RDF databases like virtuoso and query using SPARQL queries. The original graph data in GML format comes from the http://www-personal.umich.edu/~mejn/netdata/, the network data datasets collected Mark Newman, University of Michigan.
DOI: 10.6084/m9.figshare.21085042.v4
title: RDF triplets for the graph Les Miserables

description: The extracted RDF triplets from the graph Les Miserables (The coappearance network of characters in the novel). We consider three types of property: structural property, computed property, and semantic property. These data can be imported into RDF databases like virtuoso and query using SPARQL queries. The original graph data in GML format comes from the http://www-personal.umich.edu/~mejn/netdata/, the network data datasets collected Mark Newman, University of Michigan.
DOI: 10.6084/m9.figshare.21085042.v3
title: RDF triplets for graph Les Miserables

description: The extracted RDF triplets from the graph Les Miserables (The coappearance network of characters in the novel). We consider three types of property: structural property, computed property, and semantic property. These data can be imported into RDF databases like virtuoso and query using SPARQL queries. The original graph data in GML format comes from the http://www-personal.umich.edu/~mejn/netdata/, the network data datasets collected Mark Newman, University of Michigan.
DOI: 10.11583/dtu.16739449.v1
title: Dataset for: Experimental investigation on the effects of shoreface nourishment placement and timing on long-term cross-shore profile development

description: <pre><code>This is a dataset for the results presented in:</code></pre> <pre><code>"Experimental investigation on the effects of shoreface nourishment placement and timing on long-term cross-shore profile development"</code></pre> <pre><code>The data is organized in matlab structs with one struct pr. case presented. The naming of the structs follows the naming in the article.</code></pre> <pre><code>Inside the struct the data is oganized as follows:</code></pre> <pre><code>x %The distance in m to the wave paddle [m]</code></pre> <pre><code>z %The vertical coordinate of the bed [m] at corresponding x locations at different times</code></pre> <pre><code>z_clean %Vertical coordinate of a filter profile [m]</code></pre> <pre><code>bedTime %The timing of the bed scans [h]</code></pre> <pre><code>xShore %The horizontal position of the shoreline at different times [m]</code></pre> <pre><code>hBerm %The height of the berm above the still water level [m]</code></pre> <pre><code>xBerm %The horizontal position of the berm [m]</code></pre> <pre><code>hBar %The water depht at the bar [m]</code></pre> <pre><code>xBar %The horizontal position of the bar [m]</code></pre> <pre><code>qSTot %The sediment tranport rate estimated based on bed scans [m^2/s]</code></pre> <pre><code>qSTime %The timing of the sediment transport rate [h] taken as the center between two subsequent bed scans</code></pre> <pre><code>run.Assym %The asymmetry of the surface elevations</code></pre> <pre><code>run.Hm0 %The characteristic wave height [m] of the experiments</code></pre> <pre><code>run.Skew %The skewness of the surface elevations</code></pre> <pre><code>run.xposWG %The horizontal position [m] of the wave gauges</code></pre> <pre><code>run.startTime %The start time [h] of the wave measurements</code></pre> <pre><code>run.centerTime %The center time [h] of the wave measurements</code></pre> <pre><code>run.duration %The duration [h] of the wave measurements</code></pre> <pre><code><strong>Example of usage</strong></code></pre> <pre><code>%% Show the inital profile of the reference case along with the wave heights at t\approx 2.5 h<br> figure()<br> set(gcf,'PaperPositionMode','auto')<br> set(gcf,'Position',[100 200 600 200]);<br> plot(Ref.x,Ref.z(1,:),'k') % Bed profile<br> hold on<br> plot(Ref.run(1).xposWG,Ref.run(1).Hm0,'ko') % Characteristic wave height<br> plot([5 Ref.xShore(1)],[0 0],'k--') % Still water level<br> xlabel('$x$ [m]','interpreter','latex')<br> ylabel('$z$ [m]','interpreter','latex')<br> axis([5 24 -0.5 0.2])<br> </code></pre> <pre><code><br></code></pre> <pre><code><br></code></pre>
DOI: 10.11583/dtu.16739449
title: Dataset for: Experimental investigation on the effects of shoreface nourishment placement and timing on long-term cross-shore profile development

description: <pre><code>This is a dataset for the results presented in:</code></pre> <pre><code>"Experimental investigation on the effects of shoreface nourishment placement and timing on long-term cross-shore profile development"</code></pre> <pre><code>The data is organized in matlab structs with one struct pr. case presented. The naming of the structs follows the naming in the article.</code></pre> <pre><code>Inside the struct the data is oganized as follows:</code></pre> <pre><code>x %The distance in m to the wave paddle [m]</code></pre> <pre><code>z %The vertical coordinate of the bed [m] at corresponding x locations at different times</code></pre> <pre><code>z_clean %Vertical coordinate of a filter profile [m]</code></pre> <pre><code>bedTime %The timing of the bed scans [h]</code></pre> <pre><code>xShore %The horizontal position of the shoreline at different times [m]</code></pre> <pre><code>hBerm %The height of the berm above the still water level [m]</code></pre> <pre><code>xBerm %The horizontal position of the berm [m]</code></pre> <pre><code>hBar %The water depht at the bar [m]</code></pre> <pre><code>xBar %The horizontal position of the bar [m]</code></pre> <pre><code>qSTot %The sediment tranport rate estimated based on bed scans [m^2/s]</code></pre> <pre><code>qSTime %The timing of the sediment transport rate [h] taken as the center between two subsequent bed scans</code></pre> <pre><code>run.Assym %The asymmetry of the surface elevations</code></pre> <pre><code>run.Hm0 %The characteristic wave height [m] of the experiments</code></pre> <pre><code>run.Skew %The skewness of the surface elevations</code></pre> <pre><code>run.xposWG %The horizontal position [m] of the wave gauges</code></pre> <pre><code>run.startTime %The start time [h] of the wave measurements</code></pre> <pre><code>run.centerTime %The center time [h] of the wave measurements</code></pre> <pre><code>run.duration %The duration [h] of the wave measurements</code></pre> <pre><code><strong>Example of usage</strong></code></pre> <pre><code>%% Show the inital profile of the reference case along with the wave heights at t\approx 2.5 h<br> figure()<br> set(gcf,'PaperPositionMode','auto')<br> set(gcf,'Position',[100 200 600 200]);<br> plot(Ref.x,Ref.z(1,:),'k') % Bed profile<br> hold on<br> plot(Ref.run(1).xposWG,Ref.run(1).Hm0,'ko') % Characteristic wave height<br> plot([5 Ref.xShore(1)],[0 0],'k--') % Still water level<br> xlabel('$x$ [m]','interpreter','latex')<br> ylabel('$z$ [m]','interpreter','latex')<br> axis([5 24 -0.5 0.2])<br> </code></pre> <pre><code><br></code></pre> <pre><code><br></code></pre>
DOI: 10.34885/nswv-8994
title: World Checklist of Vascular Plants (WCVP) – Version 10.

description: Kew, UK