cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4035124
title: DWDE drifters: Trajectories of surface drifters in the western Gulf of Mexico from the Deep-Water Dispersion Experiment

description: This dataset is comprised of the final processed data collected by 207 surface drifters deployed in the western Gulf of Mexico during the Deep Water Dispersion Experiment (DWDE), as part of the project “Implementación de redes de observación oceanográficas (físicas geoquímicas, ecológicas) para la generación de escenarios ante posibles contingencias relacionadas a la exploración y producción de hidrocarburos en aguas profundas del Golfo de México” of the Gulf de Mexico Research Consortium (CIGoM), funded by CONACYT‐SENER‐Hydrocarbon Fund, Mexico”. The DWDE drifter data is also made available with the GulfDriftersWithDWDE dataset (http://doi.org/10.5281/zenodo.3985917), which is a merged and uniformly processed surface drifter dataset which includes publicly available data from several different drifter experiments. These latter database is the basis for the gridded product GulfFlow (http://doi.org/10.5281/zenodo.3978794). See Lilly and Pérez-Brunius (2020) "A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements" for details. García Carrillo, P., Ronquillo Méndez, A., Rodríguez Outerelo, J., Pérez Brunius, P. 2018. Experimento de Dispersión en Aguas Profundas ( DWDE ). Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California. 253 pp.
description: The acquisition of the surface drifter data from the DWDE experiment has been financed by the CONACYT-SENER-Hydrocarbons Sector Fund, Mexico (project 201441), developed by the Gulf of Mexico Research Consortium (CIGoM). We recognize PEMEX for promoting to the Fund the specific demand on oil spills and the environment. See database webpage with additional information, as well as request for download form (https://giola.cicese.mx/database/DWDE)
description: {"references": ["Garc\u00eda Carrillo, P., Ronquillo M\u00e9ndez, A., Rodr\u00edguez Outerelo, J., P\u00e9rez Brunius, P. 2018. Experimento de Dispersi\u00f3n en Aguas Profundas ( DWDE ). Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, Baja California. 253 pp."]}
DOI: 10.5281/zenodo.4421998
title: Effective de-icing skin using graphene-based flexible heater

description: dataset on Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement, Transmission Electron Microscopy and Stress Strain
DOI: 10.5281/zenodo.4421997
title: Effective de-icing skin using graphene-based flexible heater

description: dataset on Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement, Transmission Electron Microscopy and Stress Strain
DOI: 10.5281/zenodo.4422002
title: Low-Voltage Icing Protection Film for Automotive and Aeronautical Industries

description: dataset on Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement, Transmission Electron Microscopy and Stress Strain
DOI: 10.5281/zenodo.4422001
title: Low-Voltage Icing Protection Film for Automotive and Aeronautical Industries

description: dataset on Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement, Transmission Electron Microscopy and Stress Strain
DOI: 10.6073/pasta/a95f510a2e57662b66b28be26f02f4fc
title: Little Rock Lake Experiment at North Temperate Lakes LTER: pH and Alkalinity 1983 - 2000

description: The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. pH and alkalinity of the treatment and reference basins of Little Rock Lake are measured at one station in the deepest part of each basin at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. During the course of the study, three different types of electrodes were used to measure pH. Sampling Frequency: varies - Number of sites: 2
DOI: 10.6084/m9.figshare.13530554
title: Dataset

description: This data set comprises data with the ground reaction forces (with a dual force platform setup) of subjects standing still for 60 s in different conditions where vision and the standing surface were manipulated. Twenty-five subjects with Parkinson's disease were evaluated.
DOI: 10.6084/m9.figshare.13530578.v1
title: Surfaces_Data.xlsx

description: The data set contains raw data that was used to generate figures and tables in the manuscript titled "Location, location, location: Feeding site affects aphid performance by altering access and quality of nutrients" submitted to and published in thh journal PLOSOne<br>
DOI: 10.6084/m9.figshare.13530578.v2
title: Location, Location, Location: Aphid Feeding Site Choice

description: The data set contains raw data that was used to generate figures and tables in the manuscript titled "Location, location, location: Feeding site affects aphid performance by altering access and quality of nutrients" submitted to and published in thh journal PLOSOne<br>
DOI: 10.6084/m9.figshare.13530578
title: Soybean aphid data on plant locations

description: The data set contains raw data that was used to generate figures and tables in the manuscript titled "Location, location, location: Feeding site affects aphid performance by altering access and quality of nutrients" submitted to and published in thh journal PLOSOne<br>
DOI: 10.6084/m9.figshare.13530578.v4
title: Soybean aphid data on plant locations

description: The data set contains raw data that was used to generate figures and tables in the manuscript titled "Location, location, location: Feeding site affects aphid performance by altering access and quality of nutrients" submitted to and published in thh journal PLOSOne<br>
DOI: 10.6084/m9.figshare.13530578.v3
title: Soybean data on plant locations

description: The data set contains raw data that was used to generate figures and tables in the manuscript titled "Location, location, location: Feeding site affects aphid performance by altering access and quality of nutrients" submitted to and published in thh journal PLOSOne<br>
DOI: 10.6073/pasta/21650245acdf99704b4571b887fdeb2a
title: Hyporheic diatom community assemblages from Von Guerard Stream, Taylor Valley, McMurdo Dry Valleys, Antarctica in January 2019

description: In this data package, we present diatom community assemblages from hyporheic sediments collected in January 2019 from six transects across Von Guerard Stream, Taylor Valley, Antarctica. These samples were collected to address questions about the retention and processing of particulate organic matter in the hyporheic zone of McMurdo Dry Valley streams. The six transects were located at pools, riffles, and meanders (three of each geomorphology type) along Von Guerard Stream and extended across the stream channel to the edges of the wetted zone, ranging from 6.6 to 13.6 m in length. At each sampling location, we collected subsurface sediment sample that was preserved in formalin directly after sample collection. We characterized diatom assemblages by counting a total of 300 diatom valves from each preserved diatom sample. This data package is associated with a complementary data package that contains hyporheic sediment chemistry for the same samples.
DOI: 10.6073/pasta/3f76ccdf3d330df54aed1aee815dab23
title: Hyporheic sediment characteristics from Von Guerard Stream, Taylor Valley, McMurdo Dry Valleys, Antarctica in January 2019

description: In this data package, we present chlorophyll concentrations, percent loss-on-ignition organic matter, sorbed ammonium concentrations, and percent biogenic silica for hyporheic sediments collected in January 2019 from nine transects across Von Guerard Stream, Taylor Valley, Antarctica. These samples were collected to address questions about the retention and processing of particulate organic matter in the hyporheic zone of McMurdo Dry Valley streams. The nine transects were located at pools, riffles, and meanders (three of each geomorphology type) along Von Guerard Stream and extended across the stream channel to the edges of the wetted zone, ranging from 6.6 to 13.6 m in length. At each sampling location, we collected subsurface bulk sediment down to a depth of 10 cm. We analyzed the sediment samples for chlorophyll-a, phaeophytin, loss-on-ignition, ammonium sorbed to the sediment, and the UV absorbance at 254 nm of sediment extractions. This data package is associated with a complementary data package that contains diatom community assemblages for the same samples.
DOI: 10.5281/zenodo.4422037
title: Reproducible results for: pmparser and PMDB: resources for large-scale, open studies of the biomedical literature

description: The zip file contains all code and results. See the included readme.
DOI: 10.5281/zenodo.4004909
title: Reproducible results for: pmparser and PMDB: resources for large-scale, open studies of the biomedical literature

description: The zip file contains all code and results. See the included readme.
DOI: 10.6084/m9.figshare.13530605
title: Supplementary Material: Defining Chaperone-Usher Fimbriae Repertoire in Serratia marcescens

description: Supplementary Material<br><br>Supplementary File 1. Fimbrial Usher Protein (FUP) consensus sequences for α, β, γ1, γ2, γ3, γ4, κ, and π families were built with cons tool (http://www.bioinformatics.nl/cgi-bin/emboss/cons) and used as query in a tBLASTn against S. marcescens genomes.<br><br>Supplementary File 2. Genomic coordinates of S. marcescens FUP-coding genes. Each row corresponds to a S. marcescens sequenced strain. Each CU operon family and given name is indicated on top. Nucleotide start of each FUP-coding gene in the associated genome is indicated in individual cells.<br><br>Supplementary File 3. CLUSTALX v2.0 alignment of the 421 FUP of S. marcescens along with 189 FUP from different Gram-negative bacteria. The S. marcescens isolate name along with the here assigned CU fimbriae nomenclature associated is indicated. GenBank accession numbers of non-S. marcescens FUPs along with their previously assigned family are also indicated.<br><br>Supplementary File 4. Growth curves at 37°C of S. marcescens SmUNAM836 (A) or Db10 (B) strains, carrying pSEVA246 plasmid. Data represent the mean ± SD from two independent experiments.
DOI: 10.5281/zenodo.4422045
title: Contextualizing Trending Entities in News Stories

description: This repository contains the enrichments for the dataset The New York Times Annotated Corpus developed for the paper: “Marco Ponza, Diego Ceccarelli, Paolo Ferragina, Edgar Meij, Sambhav Kothari. Contextualizing Trending Entities in News Stories. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021).” It includes a total of 149 trends constituted by 120K entities. The goal is to retrieve a set of entities ranked with respect to their usefulness in explaining why a given trending entity is actually trending. <strong>Format</strong> The repository contains the enrichments in JSON format. The news stories of the New York Times from which these enrichments have been developed are available from LDC. <strong>Data Splits</strong> We perform two kinds of evaluation. Unsupervised evaluation, where we use the complete dataset of 149 trends as a benchmark. Supervised evaluation, where we train/tune our models on a training/development set and we test them on a test set. The training set contains 50 trends constituted by 36.3K entities from 1996 to 2000. The development set contains 34 trends constituted by 26.7K entities from 2000 to 2002. The test set contains 65 trends constituted by 57K entities from 2002 to 2007. Use Please cite the data set and the accompanying paper if you found the resources in this repository useful: @inproceedings{ponza2021,<br> Title = {Contextualizing Trending Entities in News Stories},<br> author = {Ponza, Marco and Ceccarelli, Diego and Ferragina, Paolo and Meij, Edgar and Kothari, Sambhav},<br> Booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining},<br> Year = {2021},<br> }
DOI: 10.5281/zenodo.4422044
title: Contextualizing Trending Entities in News Stories

description: This repository contains the enrichments for the dataset The New York Times Annotated Corpus developed for the paper: “Marco Ponza, Diego Ceccarelli, Paolo Ferragina, Edgar Meij, Sambhav Kothari. Contextualizing Trending Entities in News Stories. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021).” It includes a total of 149 trends constituted by 120K entities. The goal is to retrieve a set of entities ranked with respect to their usefulness in explaining why a given trending entity is actually trending. <strong>Format</strong> The repository contains the enrichments in JSON format. The news stories of the New York Times from which these enrichments have been developed are available from LDC. <strong>Data Splits</strong> We perform two kinds of evaluation. Unsupervised evaluation, where we use the complete dataset of 149 trends as a benchmark. Supervised evaluation, where we train/tune our models on a training/development set and we test them on a test set. The training set contains 50 trends constituted by 36.3K entities from 1996 to 2000. The development set contains 34 trends constituted by 26.7K entities from 2000 to 2002. The test set contains 65 trends constituted by 57K entities from 2002 to 2007. Use Please cite the data set and the accompanying paper if you found the resources in this repository useful: @inproceedings{ponza2021,<br> Title = {Contextualizing Trending Entities in News Stories},<br> author = {Ponza, Marco and Ceccarelli, Diego and Ferragina, Paolo and Meij, Edgar and Kothari, Sambhav},<br> Booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining},<br> Year = {2021},<br> }
DOI: 10.6084/m9.figshare.13530596
title: Dataset Experiment 2

description: <b>Columns</b> <br>'Activity', 'AccX0.5Max', 'AccX0.5Min', 'AccX0.5Avg', 'AccX1Max', 'AccX1Min', 'AccX1Avg', 'AccX3Max', 'AccX3Min', 'AccX3Avg', 'AccX5Max', 'AccX5Min', 'AccX5Avg', 'AccY0.5Max', 'AccY0.5Min', 'AccY0.5Avg', 'AccY1Max', 'AccY1Min', 'AccY1Avg', 'AccY3Max', 'AccY3Min', 'AccY3Avg', 'AccY5Max', 'AccY5Min', 'AccY5Avg', 'AccZ0.5Max', 'AccZ0.5Min', 'AccZ0.5Avg', 'AccZ1Max', 'AccZ1Min', 'AccZ1Avg', 'AccZ3Max', 'AccZ3Min', 'AccZ3Avg', 'AccZ5Max', 'AccZ5Min', 'AccZ5Avg', 'GyrX0.5Max', 'GyrX0.5Min', 'GyrX0.5Avg', 'GyrX1Max', 'GyrX1Min', 'GyrX1Avg', 'GyrX3Max', 'GyrX3Min', 'GyrX3Avg', 'GyrX5Max', 'GyrX5Min', 'GyrX5Avg', 'GyrY0.5Max', 'GyrY0.5Min', 'GyrY0.5Avg', 'GyrY1Max', 'GyrY1Min', 'GyrY1Avg', 'GyrY3Max', 'GyrY3Min', 'GyrY3Avg', 'GyrY5Max', 'GyrY5Min', 'GyrY5Avg', 'GyrZ0.5Max', 'GyrZ0.5Min', 'GyrZ0.5Avg', 'GyrZ1Max', 'GyrZ1Min', 'GyrZ1Avg', 'GyrZ3Max', 'GyrZ3Min', 'GyrZ3Avg', 'GyrZ5Max', 'GyrZ5Min', 'GyrZ5Avg', 'LocX1.5Max', 'LocX1.5Min', 'LocX1.5Avg', 'LocX5Max', 'LocX5Min', 'LocX5Avg', 'LocX10Max', 'LocX10Min', 'LocX10Avg', 'LocY1.5Max', 'LocY1.5Min', 'LocY1.5Avg', 'LocY5Max', 'LocY5Min', 'LocY5Avg', 'LocY10Max', 'LocY10Min', 'LocY10Avg', 'LocZ1.5Max', 'LocZ1.5Min', 'LocZ1.5Avg', 'LocZ5Max', 'LocZ5Min', 'LocZ5Avg', 'LocZ10Max', 'LocZ10Min', 'LocZ10Avg', 'BinWc5', 'BinBathroom - motion5', 'BinBathroom- shower5', 'BinBathroom - cupboard5', 'BinKitchen - motion5', 'BinFridge5', 'BinDishwasher5', 'BinFreezer5', 'BinOven5', 'BinWc30', 'BinBathroom - motion530', 'BinBathroom- shower30', 'BinBathroom - cupboard30', 'BinKitchen - motion30', 'BinFridge30', 'BinDishwasher30', 'BinFreezer30', 'BinOven30'<br><b>Activities</b><b><br></b>1 : Walk2 : Smartphone3 : Sleep4 : PC5 : Dress6 : WC7 : Comb8 : Brush teeth9 : Wash hands10 : Shower11 : Drink water13 : Eat13 : Sweep14 : Watch TV<b><br></b>
DOI: 10.6084/m9.figshare.13530596.v1
title: Dataset

description: <b>Columns</b> <br>'Activity', 'AccX0.5Max', 'AccX0.5Min', 'AccX0.5Avg', 'AccX1Max', 'AccX1Min', 'AccX1Avg', 'AccX3Max', 'AccX3Min', 'AccX3Avg', 'AccX5Max', 'AccX5Min', 'AccX5Avg', 'AccY0.5Max', 'AccY0.5Min', 'AccY0.5Avg', 'AccY1Max', 'AccY1Min', 'AccY1Avg', 'AccY3Max', 'AccY3Min', 'AccY3Avg', 'AccY5Max', 'AccY5Min', 'AccY5Avg', 'AccZ0.5Max', 'AccZ0.5Min', 'AccZ0.5Avg', 'AccZ1Max', 'AccZ1Min', 'AccZ1Avg', 'AccZ3Max', 'AccZ3Min', 'AccZ3Avg', 'AccZ5Max', 'AccZ5Min', 'AccZ5Avg', 'GyrX0.5Max', 'GyrX0.5Min', 'GyrX0.5Avg', 'GyrX1Max', 'GyrX1Min', 'GyrX1Avg', 'GyrX3Max', 'GyrX3Min', 'GyrX3Avg', 'GyrX5Max', 'GyrX5Min', 'GyrX5Avg', 'GyrY0.5Max', 'GyrY0.5Min', 'GyrY0.5Avg', 'GyrY1Max', 'GyrY1Min', 'GyrY1Avg', 'GyrY3Max', 'GyrY3Min', 'GyrY3Avg', 'GyrY5Max', 'GyrY5Min', 'GyrY5Avg', 'GyrZ0.5Max', 'GyrZ0.5Min', 'GyrZ0.5Avg', 'GyrZ1Max', 'GyrZ1Min', 'GyrZ1Avg', 'GyrZ3Max', 'GyrZ3Min', 'GyrZ3Avg', 'GyrZ5Max', 'GyrZ5Min', 'GyrZ5Avg', 'LocX1.5Max', 'LocX1.5Min', 'LocX1.5Avg', 'LocX5Max', 'LocX5Min', 'LocX5Avg', 'LocX10Max', 'LocX10Min', 'LocX10Avg', 'LocY1.5Max', 'LocY1.5Min', 'LocY1.5Avg', 'LocY5Max', 'LocY5Min', 'LocY5Avg', 'LocY10Max', 'LocY10Min', 'LocY10Avg', 'LocZ1.5Max', 'LocZ1.5Min', 'LocZ1.5Avg', 'LocZ5Max', 'LocZ5Min', 'LocZ5Avg', 'LocZ10Max', 'LocZ10Min', 'LocZ10Avg', 'BinWc5', 'BinBathroom - motion5', 'BinBathroom- shower5', 'BinBathroom - cupboard5', 'BinKitchen - motion5', 'BinFridge5', 'BinDishwasher5', 'BinFreezer5', 'BinOven5', 'BinWc30', 'BinBathroom - motion530', 'BinBathroom- shower30', 'BinBathroom - cupboard30', 'BinKitchen - motion30', 'BinFridge30', 'BinDishwasher30', 'BinFreezer30', 'BinOven30'<br><b>Activities</b><b><br></b>1 : Walk2 : Smartphone3 : Sleep4 : PC5 : Dress6 : WC7 : Comb8 : Brush teeth9 : Wash hands10 : Shower11 : Drink water13 : Eat13 : Sweep14 : Watch TV<b><br></b>
DOI: 10.5063/f1k935x4
title: Sea otter foraging data, visual observations from Prince of Wales, Alaska

description: Sea otter diets were assessed during summertime 2018 (May-August) visual observations from land. Data were collected on western Prince of Wales Island Alaska, at various sites where sea otter densities are high. These data were collected as a part of an NSF-funded project: Apex Predators, Ecosystems, and Community Sustainability (APECS, http://apecs-ak.org/). Other datasets to support this work are also archived with KNB.
DOI: 10.6084/m9.figshare.13530671
title: Dataset Experiment 1

description: <b>Columns</b><br>'Activity', 'AccX0.5Max', 'AccX0.5Min', 'AccX0.5Avg', 'AccX1Max', 'AccX1Min', 'AccX1Avg', 'AccX3Max', 'AccX3Min', 'AccX3Avg', 'AccX5Max', 'AccX5Min', 'AccX5Avg', 'AccY0.5Max', 'AccY0.5Min', 'AccY0.5Avg', 'AccY1Max', 'AccY1Min', 'AccY1Avg', 'AccY3Max', 'AccY3Min', 'AccY3Avg', 'AccY5Max', 'AccY5Min', 'AccY5Avg', 'AccZ0.5Max', 'AccZ0.5Min', 'AccZ0.5Avg', 'AccZ1Max', 'AccZ1Min', 'AccZ1Avg', 'AccZ3Max', 'AccZ3Min', 'AccZ3Avg', 'AccZ5Max', 'AccZ5Min', 'AccZ5Avg', 'GyrX0.5Max', 'GyrX0.5Min', 'GyrX0.5Avg', 'GyrX1Max', 'GyrX1Min', 'GyrX1Avg', 'GyrX3Max', 'GyrX3Min', 'GyrX3Avg', 'GyrX5Max', 'GyrX5Min', 'GyrX5Avg', 'GyrY0.5Max', 'GyrY0.5Min', 'GyrY0.5Avg', 'GyrY1Max', 'GyrY1Min', 'GyrY1Avg', 'GyrY3Max', 'GyrY3Min', 'GyrY3Avg', 'GyrY5Max', 'GyrY5Min', 'GyrY5Avg', 'GyrZ0.5Max', 'GyrZ0.5Min', 'GyrZ0.5Avg', 'GyrZ1Max', 'GyrZ1Min', 'GyrZ1Avg', 'GyrZ3Max', 'GyrZ3Min', 'GyrZ3Avg', 'GyrZ5Max', 'GyrZ5Min', 'GyrZ5Avg', 'LocX1.5Max', 'LocX1.5Min', 'LocX1.5Avg', 'LocX5Max', 'LocX5Min', 'LocX5Avg', 'LocX10Max', 'LocX10Min', 'LocX10Avg', 'LocY1.5Max', 'LocY1.5Min', 'LocY1.5Avg', 'LocY5Max', 'LocY5Min', 'LocY5Avg', 'LocY10Max', 'LocY10Min', 'LocY10Avg', 'LocZ1.5Max', 'LocZ1.5Min', 'LocZ1.5Avg', 'LocZ5Max', 'LocZ5Min', 'LocZ5Avg', 'LocZ10Max', 'LocZ10Min', 'LocZ10Avg', 'BinWc5', 'BinBathroom - motion5', 'BinBathroom- shower5','BinBathroom - cupboard5', 'BinKitchen - motion5', 'BinFridge5', 'BinDishwasher5', 'BinFreezer5', 'BinOven5', 'BinWc30', 'BinBathroom - motion530', 'BinBathroom- shower30', 'BinBathroom - cupboard30', 'BinKitchen - motion30', 'BinFridge30', 'BinDishwasher30', 'BinFreezer30', 'BinOven30'<br><b>Activities</b><b><br></b>1 : Walk2 : Smartphone3 : Sleep4 : PC5 : Dress6 : WC7 : Comb8 : Brush teeth9 : Wash hands10 : Shower11 : Drink water13 : Eat13 : Sweep14 : Watch TV
DOI: 10.6084/m9.figshare.13530671.v1
title: Dataset Experiment 1

description: <b>Columns</b><br>'Activity', 'AccX0.5Max', 'AccX0.5Min', 'AccX0.5Avg', 'AccX1Max', 'AccX1Min', 'AccX1Avg', 'AccX3Max', 'AccX3Min', 'AccX3Avg', 'AccX5Max', 'AccX5Min', 'AccX5Avg', 'AccY0.5Max', 'AccY0.5Min', 'AccY0.5Avg', 'AccY1Max', 'AccY1Min', 'AccY1Avg', 'AccY3Max', 'AccY3Min', 'AccY3Avg', 'AccY5Max', 'AccY5Min', 'AccY5Avg', 'AccZ0.5Max', 'AccZ0.5Min', 'AccZ0.5Avg', 'AccZ1Max', 'AccZ1Min', 'AccZ1Avg', 'AccZ3Max', 'AccZ3Min', 'AccZ3Avg', 'AccZ5Max', 'AccZ5Min', 'AccZ5Avg', 'GyrX0.5Max', 'GyrX0.5Min', 'GyrX0.5Avg', 'GyrX1Max', 'GyrX1Min', 'GyrX1Avg', 'GyrX3Max', 'GyrX3Min', 'GyrX3Avg', 'GyrX5Max', 'GyrX5Min', 'GyrX5Avg', 'GyrY0.5Max', 'GyrY0.5Min', 'GyrY0.5Avg', 'GyrY1Max', 'GyrY1Min', 'GyrY1Avg', 'GyrY3Max', 'GyrY3Min', 'GyrY3Avg', 'GyrY5Max', 'GyrY5Min', 'GyrY5Avg', 'GyrZ0.5Max', 'GyrZ0.5Min', 'GyrZ0.5Avg', 'GyrZ1Max', 'GyrZ1Min', 'GyrZ1Avg', 'GyrZ3Max', 'GyrZ3Min', 'GyrZ3Avg', 'GyrZ5Max', 'GyrZ5Min', 'GyrZ5Avg', 'LocX1.5Max', 'LocX1.5Min', 'LocX1.5Avg', 'LocX5Max', 'LocX5Min', 'LocX5Avg', 'LocX10Max', 'LocX10Min', 'LocX10Avg', 'LocY1.5Max', 'LocY1.5Min', 'LocY1.5Avg', 'LocY5Max', 'LocY5Min', 'LocY5Avg', 'LocY10Max', 'LocY10Min', 'LocY10Avg', 'LocZ1.5Max', 'LocZ1.5Min', 'LocZ1.5Avg', 'LocZ5Max', 'LocZ5Min', 'LocZ5Avg', 'LocZ10Max', 'LocZ10Min', 'LocZ10Avg', 'BinWc5', 'BinBathroom - motion5', 'BinBathroom- shower5','BinBathroom - cupboard5', 'BinKitchen - motion5', 'BinFridge5', 'BinDishwasher5', 'BinFreezer5', 'BinOven5', 'BinWc30', 'BinBathroom - motion530', 'BinBathroom- shower30', 'BinBathroom - cupboard30', 'BinKitchen - motion30', 'BinFridge30', 'BinDishwasher30', 'BinFreezer30', 'BinOven30'<br><b>Activities</b><b><br></b>1 : Walk2 : Smartphone3 : Sleep4 : PC5 : Dress6 : WC7 : Comb8 : Brush teeth9 : Wash hands10 : Shower11 : Drink water13 : Eat13 : Sweep14 : Watch TV
DOI: 10.5517/ccdc.csd.cc25jsvc
title: CCDC 2012143: Experimental Crystal Structure Determination

description: Related Article: Yuyuan Xiao, Xiao-Kun Zhao, Tianpin Wu, Jeffrey T. Miller, Han-Shi Hu, Jun Li, Wenliang Huang, Paula L. Diaconescu|2021|Chemical Science|12|227|doi:10.1039/D0SC03555F
DOI: 10.5517/ccdc.csd.cc25jswd
title: CCDC 2012144: Experimental Crystal Structure Determination

description: Related Article: Yuyuan Xiao, Xiao-Kun Zhao, Tianpin Wu, Jeffrey T. Miller, Han-Shi Hu, Jun Li, Wenliang Huang, Paula L. Diaconescu|2021|Chemical Science|12|227|doi:10.1039/D0SC03555F
DOI: 10.5517/ccdc.csd.cc25jsxf
title: CCDC 2012145: Experimental Crystal Structure Determination

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