cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4489875
title: An Interpretable Framework for Investigating Neighborhood Effect in POI Recommendation

description: Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect, which captures a user’s POI visiting behavior based on the user’s preference not only to a POI, but also to the POI’s neighborhood. To provide a interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp dataset to evaluate how different aspects of the neighborhood effect affect a user’s POI visiting behavior. Second, we propose a deep learning based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization based POI recommendation techniques.
description: There are three python scripts and ten dataset related files.
DOI: 10.6084/m9.figshare.13726531.v1
title: planarian_B01-B16.rds

description: Seurat object of Schmidtea mediterranea Drop-seq data (GSM3173562)
DOI: 10.6084/m9.figshare.13726531
title: planarian_B01-B16.rds

description: Seurat object of Schmidtea mediterranea Drop-seq data (GSM3173562)
DOI: 10.25919/m6wg-8t27
title: Parkes observations for project P1068 semester 2020APRS_39

description: In the fast-moving world of Fast Radio Bursts (FRBs), the key question of what fraction (as high as 100%?) of FRBs emit repeat bursts remains unanswered. Of order 10 repeating FRBs are now known, and their spectro-temporal properties hint at differences with apparently non-repeating bursts - but poor statistics preclude a definitive diagnosis. FRB 180301 is a promising source for further study for multiple reasons: it is the first repeating FRB seen by the FAST telescope, and has the second-highest rotation measure ever seen for a FRB (after the original repeater, FRB121102). We request Parkes observations with the UWL to characterise the broadband emission properties of this FRB, with the following key goals: to constrain the repetition rate, identify any variation of pulse characteristics over time, and provide insight into the emission mechanisms. In its initial detection, FRB 180301 was poorly fit by regular Faraday rotation models, and showed significant circular polarization, which may indicate the progenitor is embedded in an extreme magneto-ionic environment with complex field structure that has not been seen before. These observations will generate a large data volume. We will use the Medusa backend in parallel with the Breakthrough Listen data recorder (BL), and will transfer data to BL immediately after observations.These data will be used by P. Kumar to prototype a FRB pipeline for the UWL, that will be made broadly available for similar studies.
DOI: 10.5281/zenodo.4034666
title: Carlgren's hesitation allayed: redescription and systematics of Heteranthus verruculatus Klunzinger, 1877 (Cnidaria, Actiniaria), with a redefinition of Heteranthidae Carlgren, 1900

description: DNA sequence alignments and pairwise distances
DOI: 10.5281/zenodo.4034665
title: Carlgren's hesitation allayed: redescription and systematics of Heteranthus verruculatus Klunzinger, 1877 (Cnidaria, Actiniaria), with a redefinition of Heteranthidae Carlgren, 1900

description: DNA sequence alignments and pairwise distances
DOI: 10.6084/m9.figshare.13726555
title: V3 Genomics Data of Insect Intervention

description: Genomics data in fastq
DOI: 10.6084/m9.figshare.13726555.v1
title: V3 Genomics Data of Insect Intervention

description: Genomics data in fastq
DOI: 10.5281/zenodo.4515142
title: Dataset for "Uncertainty in East Antarctic firn thickness constrained using a model ensemble approach" by Verjans et al. (2021)

description: Dataset associated with "Uncertainty in East Antarctic firn thickness constrained using a model ensemble approach" of Verjans et al. (2021).<br> Author: Vincent Verjans (v.verjans@lancaster.ac.uk)<br> Please feel free to contact Vincent Verjans by email if you have any questions.<br> <br> The ReadMe txt file associated with the dataset includes all the relevant information.
description: {"references": ["Verjans et al. (2021): Uncertainty in East Antarctic firn thickness constrained using a model ensemble approach"]}
DOI: 10.5281/zenodo.4515141
title: Dataset for "Uncertainty in East Antarctic firn thickness constrained using a model ensemble approach" by Verjans et al. (2021)

description: Dataset associated with "Uncertainty in East Antarctic firn thickness constrained using a model ensemble approach" of Verjans et al. (2021).<br> Author: Vincent Verjans (v.verjans@lancaster.ac.uk)<br> Please feel free to contact Vincent Verjans by email if you have any questions.<br> <br> The ReadMe txt file associated with the dataset includes all the relevant information.
description: {"references": ["Verjans et al. (2021): Uncertainty in East Antarctic firn thickness constrained using a model ensemble approach"]}
DOI: 10.5281/zenodo.4395625
title: Positive Emotion Dispositions and Emotion Regulation in the Italian Population

description: Dataset of the Dispositional Positive Emotion Scale in the Italian population
DOI: 10.5281/zenodo.4395624
title: Positive Emotion Dispositions and Emotion Regulation in the Italian Population

description: Dataset of the Dispositional Positive Emotion Scale in the Italian population
DOI: 10.5281/zenodo.4514279
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2007 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2007. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.5281/zenodo.4514278
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2007 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2007. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).
DOI: 10.15161/oar.it/73477
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.4515126
title: Use or misuse of NMR to test molecular mobility during chemical reactions

description: This repository contains representative PGSE NMR raw data (pulsed gradient spin echo nuclear magnetic resonance) for the aqueous click CuAAC reaction: Copper catalyzed alkyne azide cycloaddition. Obtained using magnetic field gradients in random and decreasing sequence, it supports two publications: (a) Wang et al., “Response to Comment on Boosted molecular mobility during common chemical reactions”, <em>Science </em><strong>2021,</strong> <em>371</em> (6526), eabe8678, and (b) Wang, Huang and Granick, “Use or misuse of NMR to test molecular mobility during chemical reactions”. The 5 zip files in this repository can be opened using Mestrenova software (Bruker).
DOI: 10.5281/zenodo.4472030
title: Use or misuse of NMR to test molecular mobility during chemical reactions

description: This repository contains representative PGSE NMR raw data (pulsed gradient spin echo nuclear magnetic resonance) for the aqueous click CuAAC reaction: Copper catalyzed alkyne azide cycloaddition. Obtained using magnetic field gradients in random and decreasing sequence, it supports two publications: (a) Wang et al., “Response to Comment on Boosted molecular mobility during common chemical reactions”, <em>Science </em><strong>2021,</strong> <em>371</em> (6526), eabe8678, and (b) Wang, Huang and Granick, “Use or misuse of NMR to test molecular mobility during chemical reactions”. The 5 zip files in this repository can be opened using Mestrenova software (Bruker).
DOI: 10.17632/7km6y96bcz.2
title: Data for: Transient and persistent inefficiency traps in Chinese provinces

description: Technical efficiency indices are estimated by using the parametric stochastic frontier analysis. The regression is based on a fixed-effect panel translog stochastic production frontier model, which covers GDP, capital stock approximated by the perpetual inventory method, education augmented labour input, and a time variable. Persistent inefficiency trap is indexed by the difference between an underachieving province’ persistent (or average) efficiency level and national average efficiency level. Transient inefficiency trap is indexed by the difference between the underachieving province’s overall efficiency gap and its persistent efficiency gap. Determinants of transient inefficiency trap are identified by a panel data model. Determinants of persistent inefficiency trap are identified through a cross sectional data model.
DOI: 10.5517/ccdc.csd.cc2669mc
title: CCDC 2031852: Experimental Crystal Structure Determination

description: Related Article: Jin-Hu Dou, Maxx Q. Arguilla, Yi Luo, Jian Li, Weizhe Zhang, Lei Sun, Jenna L. Mancuso, Luming Yang, Tianyang Chen, Lucas R. Parent, Grigorii Skorupskii, Nicole J. Libretto, Chenyue Sun, Min Chieh Yang, Phat Vinh Dip, Edward J. Brignole, Jeffrey T. Miller, Jing Kong, Christopher H. Hendon, Junliang Sun and Mircea Dincă |2020|Nat.Mater|20|222|doi:10.1038/s41563-020-00847-7
DOI: 10.5517/ccdc.csd.cc2669nd
title: CCDC 2031853: Experimental Crystal Structure Determination

description: Related Article: Jin-Hu Dou, Maxx Q. Arguilla, Yi Luo, Jian Li, Weizhe Zhang, Lei Sun, Jenna L. Mancuso, Luming Yang, Tianyang Chen, Lucas R. Parent, Grigorii Skorupskii, Nicole J. Libretto, Chenyue Sun, Min Chieh Yang, Phat Vinh Dip, Edward J. Brignole, Jeffrey T. Miller, Jing Kong, Christopher H. Hendon, Junliang Sun and Mircea Dincă |2020|Nat.Mater|20|222|doi:10.1038/s41563-020-00847-7
DOI: 10.5517/ccdc.csd.cc2669pf
title: CCDC 2031854: Experimental Crystal Structure Determination

description: Related Article: Jin-Hu Dou, Maxx Q. Arguilla, Yi Luo, Jian Li, Weizhe Zhang, Lei Sun, Jenna L. Mancuso, Luming Yang, Tianyang Chen, Lucas R. Parent, Grigorii Skorupskii, Nicole J. Libretto, Chenyue Sun, Min Chieh Yang, Phat Vinh Dip, Edward J. Brignole, Jeffrey T. Miller, Jing Kong, Christopher H. Hendon, Junliang Sun and Mircea Dincă |2020|Nat.Mater|20|222|doi:10.1038/s41563-020-00847-7
DOI: 10.5281/zenodo.4515388
title: CO2 emissions from Chinese inland waters

description: This is the dataset for our research on assessing CO<sub>2</sub> emissions from Chinese inland waters, including streams, rivers, lakes and reservoirs. The dataset includes three parts, including Part 1: Lakes and reservoirs_1980s, Part 2: CO<sub>2 </sub>Dataset_2010s, and Part 3: Water chemistry records. Detailed information on these datasets can be found from the 'README' text file. Author of the repository: Ran Lishan<br> Department of Geography, <br> The University of Hong Kong<br> Email: lsran@hku.hk
DOI: 10.5281/zenodo.4515387
title: CO2 emissions from Chinese inland waters

description: This is the dataset for our research on assessing CO<sub>2</sub> emissions from Chinese inland waters, including streams, rivers, lakes and reservoirs. The dataset includes three parts, including Part 1: Lakes and reservoirs_1980s, Part 2: CO<sub>2 </sub>Dataset_2010s, and Part 3: Water chemistry records. Detailed information on these datasets can be found from the 'README' text file. Author of the repository: Ran Lishan<br> Department of Geography, <br> The University of Hong Kong<br> Email: lsran@hku.hk
DOI: 10.25646/7976
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.5281/zenodo.4515561
title: Climatization and Luminosity Optimization of Buildings using Genetic Algorithm, Random Forest and Regression Models - Training Datasets

description: The Genetic Algorithm is used to create the training dataset for both the Random Forest and Polynomial Regressions. It achieves these results by adjusting the air conditioner temperature, artificial lighting luminosity, remote blinds, and by advising the opening of doors and/or windows. All the input data (e.g. sensor data, MIBEL prices, and available PV) had to been retracted, due to privacy reasons, thus these files only represent the output given by the Genetic Algorithm, for each scenario. The culmination of all the diferent training data files, from the different rooms, represent the training set. File Description: Training_Dataset_1 - Training Dataset for the first room Training_Dataset_2 - Training Dataset for the second room Training_Dataset_3 - Training Dataset for the third room Training_Dataset_4 - Training Dataset for the fourth room
DOI: 10.5281/zenodo.4515560
title: Climatization and Luminosity Optimization of Buildings using Genetic Algorithm, Random Forest and Regression Models - Training Datasets

description: The Genetic Algorithm is used to create the training dataset for both the Random Forest and Polynomial Regressions. It achieves these results by adjusting the air conditioner temperature, artificial lighting luminosity, remote blinds, and by advising the opening of doors and/or windows. All the input data (e.g. sensor data, MIBEL prices, and available PV) had to been retracted, due to privacy reasons, thus these files only represent the output given by the Genetic Algorithm, for each scenario. The culmination of all the diferent training data files, from the different rooms, represent the training set. File Description: Training_Dataset_1 - Training Dataset for the first room Training_Dataset_2 - Training Dataset for the second room Training_Dataset_3 - Training Dataset for the third room Training_Dataset_4 - Training Dataset for the fourth room
DOI: 10.5281/zenodo.4515604
title: Climatization and Luminosity Optimization of Buildings using Genetic Algorithm, Random Forest and Regression Models - Genetic Algorithm

description: The case study for the Genetic Algorithm uses real sensor data, provided by a research unit that develops scientific research and innovation in areas such as Smart Grinds, Electricity Markets, Distributed Resources, Demand Response, Energy Efficiency, and Intelligent Buildings. The collected data represents a year, from the 1<sup>st</sup> of May 2019 to the 30<sup>th</sup> of April 2020, of sensor data from a room, and market prices, which are considered to be MIBEL prices. Also, all the data is provided in intervals of one hour. All the input data (e.g. sensor data, MIBEL prices, and available PV) had to been retracted, due to privacy reasons, thus these files only represent the output given by the Genetic Algorithm, for each scenario. File Description: Genetic_Algorithm_Dataset_Statistics - Dataset used for the genetic algorithm case study, with attached statistics regarding energy cost, temperature deviation, luminosity deviation, and VOC Input_Genetic_Algorithm_JSON_Example - Example of JSON input data for the genetic algorithm
DOI: 10.5281/zenodo.4515603
title: Climatization and Luminosity Optimization of Buildings using Genetic Algorithm, Random Forest and Regression Models - Genetic Algorithm

description: The case study for the Genetic Algorithm uses real sensor data, provided by a research unit that develops scientific research and innovation in areas such as Smart Grinds, Electricity Markets, Distributed Resources, Demand Response, Energy Efficiency, and Intelligent Buildings. The collected data represents a year, from the 1<sup>st</sup> of May 2019 to the 30<sup>th</sup> of April 2020, of sensor data from a room, and market prices, which are considered to be MIBEL prices. Also, all the data is provided in intervals of one hour. All the input data (e.g. sensor data, MIBEL prices, and available PV) had to been retracted, due to privacy reasons, thus these files only represent the output given by the Genetic Algorithm, for each scenario. File Description: Genetic_Algorithm_Dataset_Statistics - Dataset used for the genetic algorithm case study, with attached statistics regarding energy cost, temperature deviation, luminosity deviation, and VOC Input_Genetic_Algorithm_JSON_Example - Example of JSON input data for the genetic algorithm
DOI: 10.6084/m9.figshare.13727329
title: Higher Physical Activity Is Related to Lower Neck Adiposity in Young Men, but to Higher Neck Adiposity in Young Women: An Exploratory Study

description: Higher Physical Activity Is Related to Lower Neck Adiposity in Young Men, but to Higher Neck Adiposity in Young Women: An Exploratory Study<br>
DOI: 10.6084/m9.figshare.13727329.v1
title: Higher Physical Activity Is Related to Lower Neck Adiposity in Young Men, but to Higher Neck Adiposity in Young Women: An Exploratory Study

description: Higher Physical Activity Is Related to Lower Neck Adiposity in Young Men, but to Higher Neck Adiposity in Young Women: An Exploratory Study<br>
DOI: 10.6084/m9.figshare.13480314
title: UCSC Hosting

description: Items for UCSC browser access, Solana lab<br>
DOI: 10.6084/m9.figshare.13480314.v5
title: UCSC Hosting

description: Items for UCSC browser access, Solana lab<br>
DOI: 10.6084/m9.figshare.13480314.v4
title: UCSC Hosting

description: Items for UCSC browser access, Solana lab<br>
DOI: 10.5281/zenodo.4515228
title: MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 2008 at 1-km

description: Layers include: Land Surface Temperature daytime low (0.05), median (0.50) and high (0.95) temperatures for the year 2008. Derived using the data.table package and quantile function in R. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod11a2_v006. Antarctica is not included. To access and visualize maps use: OpenLandMap.org To download and compute with maps using Cloud-Optimized-GeoTIFF see <strong>this tutorial</strong>. If you discover a bug, artifact or inconsistency in the OpenLandMap maps, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: clm = theme: climate, lst = variable: land surface temperature, mod11a2.daytime = determination method: MOD11A2 product, day time values, d = median value / sd = standard deviation / u.95 = aggregation/statistics method: 95% probability upper quantile, 1km = spatial resolution / block support: 1 km, s0..0cm = vertical reference: land surface, 2000..2020 = time reference: from 2000 to 2020, v1.1 = version number: 1.1,
description: This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).