cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22763621
title: Global clustered Gross Domestic Product per capita

description: This dataset provides a globally clustered GDPpc with respect to the six classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763621.v1
title: Global clustered Gross Domestic Product per capita

description: This dataset provides a globally clustered GDPpc with respect to the six classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763078.v1
title: Global clustered GDPpc

description: This dataset provides a globally clustered GDPpc with respect to the six classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763081
title: Global clustered GDPpc

description: This dataset provides a globally clustered GDPpc with respect to the six classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763081.v1
title: Global clustered GDPpc

description: This dataset provides a globally clustered GDPpc with respect to the six classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763078
title: Global clustered GDPpc

description: This dataset provides a globally clustered GDPpc with respect to the six classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763663.v1
title: Global clustered Heat Density

description: This dataset provides a globally clustered heat density with respect to the four classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763666.v1
title: Dataset of global geospatial cross-validation errors

description: This dataset provides the results of the third validation process in addition to the validation previously discussed. The error between the agent parametrisation values and the aggregated parameter at the regional level is provided in this dataset. Errors have been estimated for GDP, GDPpp, TE, SH, WH, Pop and HDI. Overall, the agent parametrisation approach suggests a global measure of error that is satisfactory, as the error is minimum in most agents and regions.
DOI: 10.6084/m9.figshare.22763669
title: Global agents with two characteristics

description: This dataset provides global agent characterisation based on two geospatial characteristics.
DOI: 10.6084/m9.figshare.22763663
title: Global clustered Heat Density

description: This dataset provides a globally clustered heat density with respect to the four classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763666
title: Dataset of global geospatial cross-validation errors

description: This dataset provides the results of the third validation process in addition to the validation previously discussed. The error between the agent parametrisation values and the aggregated parameter at the regional level is provided in this dataset. Errors have been estimated for GDP, GDPpp, TE, SH, WH, Pop and HDI. Overall, the agent parametrisation approach suggests a global measure of error that is satisfactory, as the error is minimum in most agents and regions.
DOI: 10.6084/m9.figshare.22763672
title: Dataset of global energy demand by agents and regions

description: This dataset provides the energy demand in the residential sector worldwide, disaggregated by agents and regions. The demand is further dissagregated in six service demands, as follows: space heating (hspace), water heating (hwater), space cooling (cspace), cooking (cook), lighting (light) and appliances (appl). These demands were used in eight previously defined scenarios.
DOI: 10.6084/m9.figshare.22763672.v1
title: Dataset of global energy demand by agents and regions

description: This dataset provides the energy demand in the residential sector worldwide, disaggregated by agents and regions. The demand is further dissagregated in six service demands, as follows: space heating (hspace), water heating (hwater), space cooling (cspace), cooking (cook), lighting (light) and appliances (appl). These demands were used in eight previously defined scenarios.
DOI: 10.6084/m9.figshare.22763675
title: Global agents with three characteristics

description: This dataset provides global agent characterisation based on three geospatial characteristics.
DOI: 10.6084/m9.figshare.22763675.v1
title: Global agents with three characteristics

description: This dataset provides global agent characterisation based on three geospatial characteristics.
DOI: 10.6084/m9.figshare.22763678.v1
title: Dataset of global MUSE region shapes

description: This dataset provides the MUSE-RASA regions used in this research in a geospatial format [. shp]. The 28 regions of the MUSE model are provided.
DOI: 10.6084/m9.figshare.22763681
title: Dataset to define agent evolution in space and time globally

description: This dataset provides the values of GDPpc and Population for each agent zone in each region from 2010 to 2100.
DOI: 10.6084/m9.figshare.22763681.v1
title: Dataset to define agent evolution in space and time globally

description: This dataset provides the values of GDPpc and Population for each agent zone in each region from 2010 to 2100.
DOI: 10.6084/m9.figshare.22763678
title: Dataset of global MUSE region shapes

description: This dataset provides the MUSE-RASA regions used in this research in a geospatial format [. shp]. The 28 regions of the MUSE model are provided.
DOI: 10.6084/m9.figshare.22763687
title: Dataset to define the decision-making process in Ecuador

description: This dataset provides a range of variables to define the current status of the residential sector in Ecuador, in terms of energy consumption and willingness to invest in new energy technologies or retrofitting. Variables are self-explanatory.
DOI: 10.6084/m9.figshare.22763690.v1
title: Global clustered Heat Demand per capita

description: This dataset provides global clustered heat demand per capita with respect to the four classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763669.v1
title: Global agents with two characteristics

description: This dataset provides global agent characterisation based on two geospatial characteristics.
DOI: 10.6084/m9.figshare.22763684
title: Dataset to define agent diversity globally

description: This dataset provides 12 parameters to define agent diversity worldwide aggregated in 28 regions. All the values were provided in 2010.
DOI: 10.6084/m9.figshare.22763684.v1
title: Dataset to define agent diversity globally

description: This dataset provides 12 parameters to define agent diversity worldwide aggregated in 28 regions. All the values were provided in 2010.
DOI: 10.6084/m9.figshare.22763687.v1
title: Dataset to define the decision-making process in Ecuador

description: This dataset provides a range of variables to define the current status of the residential sector in Ecuador, in terms of energy consumption and willingness to invest in new energy technologies or retrofitting. Variables are self-explanatory.
DOI: 10.6084/m9.figshare.22763696
title: Dataset to define the decision-making process in China

description: This dataset provides a range of variables to define the current status of the residential sector in China in terms of energy consumption and willingness to invest in new energy technologies or retrofitting. Variables are self-explanatory.
DOI: 10.6084/m9.figshare.22763690
title: Global clustered Heat Demand per capita

description: This dataset provides global clustered heat demand per capita with respect to the four classes. The shape file presents a range of zones with clustered values, regardless of the geographical administrative areas.
DOI: 10.6084/m9.figshare.22763693
title: Dataset of global geospatial cross-validation

description: This dataset provides details of the results of the subclustering approach used in this study. The subclustering reduced the number of heating demand agents from 96 to 20 globally.
DOI: 10.6084/m9.figshare.22763696.v1
title: Dataset to define the decision-making process in China

description: This dataset provides a range of variables to define the current status of the residential sector in China in terms of energy consumption and willingness to invest in new energy technologies or retrofitting. Variables are self-explanatory.
DOI: 10.6084/m9.figshare.22763693.v1
title: Dataset of global geospatial cross-validation

description: This dataset provides details of the results of the subclustering approach used in this study. The subclustering reduced the number of heating demand agents from 96 to 20 globally.
DOI: 10.6084/m9.figshare.24192084
title: 20230926_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.24192084.v1
title: 20230926_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.5517/ccdc.csd.cc203zmr
title: CCDC 1850874: Experimental Crystal Structure Determination

description: Related Article: Jixiang Ni, Yong Jiang, Zhenjie Qi, Rulong Yan|2019|Chem.Asian J.|14|2898|doi:10.1002/asia.201900567
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.24192015
title: Reference map of human monocytes and macrophages in blood and tumors

description: Reference map of Monocytes and Macrophages (MoMac) from blood and tumor from multiple cancer types (collated by Cheng et al. https://pubmed.ncbi.nlm.nih.gov/33545035/); integrated using semi-supervised STACAS.
DOI: 10.25452/figshare.plus.23564517.v1
title: <em><strong>Glo3DHydroClimEventSet(v1.0)</strong></em>: A global event set of hydroclimatic extremes with three-dimensional evolutions and metrics (1951—2022)

description: The <em><strong>Glo3DHydroClimEventSet(v1.0)</strong></em> is a 1950-2022 comprehensive and unified event-based set of multi-scale and multi-threshold heatwaves, cold surges, droughts, and extreme precipitation, generated with a 3D DBSCAN-based model by Dr. Zhenchen Liu and Prof. Wen Zhou @Fudan University, the mainland of China. The event set consists of three parts as follows: <br> i) event sets are stored in four kinds of <em>zip-format</em> files prefixed with "<em>ExtremePreci_</em>", "<em>Cold_</em>", "<em>Heat_</em>", and "<em>Drought_</em>"; <br> ii) Typical Examples for valications are provided as "<em>TechValidation_Example_Animations_Table5.zip</em>" and "<em>TechValidation_Example_Files_Table5.zip</em>"; <br> iii) Codes for selection of events located within target areas during the target periods are also provided, entitled as "Codes_SciData_LiuZhou2023.zip" <br> In addition, the decompressed sizes of all the zip-format files of event sets are as follows: (1/9): Global-scale SA-based cold surge event set. The total size of decompressed files are 282GB. (2/9): Global-scale percentile-based cold surge event set. The total size of decompressed files is 96GB. (3/9): Global-scale SA-based extreme precipitation event set based on five-day-accumulated precipitation. The total size of decompressed files is 1.01TB. (4/9): Global-scale SA-based extreme precipitation event set based on daily total precipitation. The total size of decompressed files is 301GB. (5/9): Global-scale percentile-based extreme precipitation event set based on daily total precipitation. The total size of decompressed files is 72GB. (6/9): Global-scale percentile-based extreme precipitation event set based on five-day-accumulated total precipitation. The total size of decompressed files is 284GB. (7/9): Global-scale SA-based heatwave event set on daily scale. The total size of decompressed files is 205GB. (8/9): Global-scale percentile-based heatwave event set. The total size of decompressed files is 34.7GB. (9/9): Global-scale SA-based meteorological drought event set. The total size of decompressed files are 147GB. <br>
DOI: 10.25452/figshare.plus.23903202
title: Dataset supporting "Inhibitory feedback from the motor circuit gates mechanosensory processing in <i>Caenorhabditis elegans</i>"

description: This is the dataset used in the <i>PLOS Biology</i> publication- Kumar S, Sharma AK, Tran A, Liu M, Leifer AM (2023) Inhibitory feedback from the motor circuit gates mechanosensory processing in <i>Caenorhabditis elegans</i>. PLoS Biol 21(9): e3002280. Previously published closed-loop high throughput optogenetic delivery system (Liu M, Kumar S, Sharma AK, Leifer AM (2022) A high-throughput method to deliver targeted optogenetic stimulation to moving <i>C</i>. <i>elegans</i> populations. PLoS Biol 20(1): e3001524) was used to track worms crawling on an agarose plate as well as deliver optogenetic stimulus to worms in real time depending on the current behavior state of the worms. In this work, worms were optogenetically stimulated in various behavior states such as forward, turn, and reverse. We investigated key reversal neurons such as AIZ, RIM, AIB, AVE, and AVA as well as the turning neurons RIV, SMB, and SAA.Datasets are arranged in folders with the corresponding figure name. For e.g. the folder 'figure_2_and_supplementary_figure_S4' contains the dataset used in generating figure 2 and supplementary figure S4. Each folder consists of .mat file for a worm strain and the corresponding behavior assay. For e.g. in 'figure_2_and_supplementary_figure_S4' folder, there is a .mat file named as 'raw_data_AIB_closed_loop_blue_stim.mat'. This file contains the data for optogenetic stimulation in a closed-loop behavior assay for the strain expressing an excitatory opsin in neuron AIB. The .mat file has the following information on each worm track: worm's velocity, centerline, ellipse ratio, optogenetic stimulus, etc. More details about each of the experiments carried out in this study can be found in Table 2 of the <i>PLOS Biology </i>manuscript.The MATLAB code used to analyze the data and generate figures presented in this work is available on GitHub at https://github.com/leiferlab/kumar-sensorimotor-integration.gitIf you use this dataset, please cite the following works in addition to citing the dataset itself:Kumar S, Sharma AK, Tran A, Liu M, Leifer AM (2023) Inhibitory feedback from the motor circuit gates mechanosensory processing in <i>Caenorhabditis elegans</i>. PLoS Biol 21(9): e3002280.Liu M, Kumar S, Sharma AK, Leifer AM (2022) A high-throughput method to deliver targeted optogenetic stimulation to moving <i>C</i>. <i>elegans</i> populations. PLoS Biol 20(1): e3001524.
DOI: 10.25452/figshare.plus.23903202.v1
title: Dataset supporting "Inhibitory feedback from the motor circuit gates mechanosensory processing in <i>Caenorhabditis elegans</i>"

description: This is the dataset used in the <i>PLOS Biology</i> publication titled "Inhibitory feedback from the motor circuit gates mechanosensory processing in <i>Caenorhabditis elegans</i>" by S. Kumar, A. K. Sharma, A. Tran, A. M. Leifer. The preprint of this work can be accessed at: https://doi.org/10.48550/arXiv.2301.02709Previously published closed-loop high throughput optogenetic delivery system (Liu, Mochi, et al. "A high-throughput method to deliver targeted optogenetic stimulation to moving C. elegans populations." <i>PLoS biology</i> 20.1 (2022): e3001524.) was used to track worms crawling on an agarose plate as well as deliver optogenetic stimulus to worms in real time depending on the current behavior state of the worms. In this work, worms were optogenetically stimulated in various behavior states such as forward, turn, and reverse. We investigated key reversal neurons such as AIZ, RIM, AIB, AVE, and AVA as well as the turning neurons RIV, SMB, and SAA.Datasets are arranged in folders with the corresponding figure name. For e.g. the folder 'figure_2_and_supplementary_figure_S4' contains the dataset used in generating figure 2 and supplementary figure S4. Each folder consists of .mat file for a worm strain and the corresponding behavior assay. For e.g. in 'figure_2_and_supplementary_figure_S4' folder, there is a .mat file named as 'raw_data_AIB_closed_loop_blue_stim.mat'. This file contains the data for optogenetic stimulation in a closed-loop behavior assay for the strain expressing an excitatory opsin in neuron AIB. The .mat file has the following information on each worm track: worm's velocity, centerline, ellipse ratio, optogenetic stimulus, etc. More details about each of the experiments carried out in this study can be found in Table 2 of the <i>PLOS Biology </i>manuscript.The MATLAB code used to analyze the data and generate figures presented in this work is available on GitHub at https://github.com/leiferlab/kumar-sensorimotor-integration.gitIf you use this dataset, please cite the following works in addition to citing the dataset itself:Liu, Mochi, et al. "A high-throughput method to deliver targeted optogenetic stimulation to moving C. elegans populations." <i>PLoS biology</i> 20.1 (2022): e3001524.Kumar, Sandeep, et al. " publication titled "Inhibitory feedback from the motor circuit gates mechanosensory processing in <i>Caenorhabditis elegans</i>." <i>PLoS biology</i> (2023).
DOI: 10.5281/zenodo.7808833
title: The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements

description: Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (arXiv:2212.02512v1). When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations: SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W Please reach out if you have any questions or concerns.
DOI: 10.5281/zenodo.7900793
title: The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements

description: Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (arXiv:2212.02512v1). When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations: SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W Please reach out if you have any questions or concerns.
DOI: 10.5281/zenodo.8092701
title: The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements

description: Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press). Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations: SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W Please reach out if you have any questions or concerns.
DOI: 10.5281/zenodo.8170299
title: The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements

description: Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press). Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations: SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W Please reach out if you have any questions or concerns.
DOI: 10.5281/zenodo.8170300
title: The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements

description: Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press). Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations: SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W Please reach out if you have any questions or concerns.
DOI: 10.5281/zenodo.8377094
title: The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements

description: Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press). Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations: SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W Please reach out if you have any questions or concerns.
DOI: 10.5281/zenodo.7808832
title: The COSMOS2020 Galaxy Stellar Mass Function -- Key Measurements

description: Here we describe the release of the measurements of the galaxy Stellar Mass Function and quiescent mass fractions based on the COSMOS2020 Farmer Catalogue and LePhare estimates of redshifts, masses, and rest-frame colours as described in Weaver et al. 2023 (ApJS, in press). Measurements can be found in "SMF_Farmer_Weaver+23.tar.gz"<br> Markov chains are contained in the remaining '.dat.gz' files When using these data products please cite both this SMF paper (Weaver et al. 2023) and the COSMOS2020 Catalogue (Weaver et al. 2022). Links to ADS export citations: SMF | https://ui.adsabs.harvard.edu/abs/2022arXiv221202512W<br> COSMOS2020 | https://ui.adsabs.harvard.edu/abs/2022ApJS..258...11W Please reach out if you have any questions or concerns.
DOI: 10.5281/zenodo.8377108
title: tcga_wgbs_meth_rse_chr1

description: A RangedSummarizedExperiment object for hg38 chromosome 1 methylation data for the samples from the paper "DNA methylation loss in late-replicating domains is linked to mitotic cell division"
DOI: 10.5281/zenodo.8377107
title: tcga_wgbs_meth_rse_chr1

description: A RangedSummarizedExperiment object for hg38 chromosome 1 methylation data for the samples from the paper "DNA methylation loss in late-replicating domains is linked to mitotic cell division"