cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.16343844
title: CASAS Smart Home dataset (raw) - free living, motion, door, activity labels

description: This dataset represents ambient data collected longitudinally in 49 community homes.  This is a resource for analyzing naturalistic behavior in a home and building activity recognition models that operate in the wild. 

Data are collected continuously from ambient sensors while residents perform their normal routines. The data fields are date, time, sensor identifier, and message. The sensors consist of PIR (motion) sensors and magnetic door (open/close) sensors. Sensors are attached to ceilings and identified by their location in the home (e.g., Bathroom, Bedroom, DiningRoom, Bed, Bath, OfficeChair). If a home contains more than one room of a given type, the corresponding sensors are distinguished by a trailing letter to differentiate the rooms (e.g., BedroomA, BedroomB). The lens of most motion sensors are constrained to cover a 1 meter diameter area. To detect movement in a larger area, an unconstrained sensor is angled to cover an entire room or region and is indicated by Area (e.g., BedroomArea).

There is one file per home. These datasets represent entries with raw sensor identifiers - a version of the data with mapped sensor names can be found in zenodo dataset 10.5281/zenodo.15708568.  The resource also includes text files that denote the region of the home (or intersection of regions) where each sensor is located.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.
description: Citation: Please cite the following paper when using this dataset:Cook, D., Crandall, A., Thomas, B., & Krishnan, N. (2013). CASAS: A smart home in a box. IEEE Computer, 46(7):62-69, 2013. https://doi.org/10.1109/MC.2012.328
DOI: 10.5281/zenodo.16343843
title: CASAS Smart Home dataset (raw) - free living, motion, door, activity labels

description: This dataset represents ambient data collected longitudinally in 49 community homes.  This is a resource for analyzing naturalistic behavior in a home and building activity recognition models that operate in the wild. 

Data are collected continuously from ambient sensors while residents perform their normal routines. The data fields are date, time, sensor identifier, and message. The sensors consist of PIR (motion) sensors and magnetic door (open/close) sensors. Sensors are attached to ceilings and identified by their location in the home (e.g., Bathroom, Bedroom, DiningRoom, Bed, Bath, OfficeChair). If a home contains more than one room of a given type, the corresponding sensors are distinguished by a trailing letter to differentiate the rooms (e.g., BedroomA, BedroomB). The lens of most motion sensors are constrained to cover a 1 meter diameter area. To detect movement in a larger area, an unconstrained sensor is angled to cover an entire room or region and is indicated by Area (e.g., BedroomArea).

There is one file per home. These datasets represent entries with raw sensor identifiers - a version of the data with mapped sensor names can be found in zenodo dataset 10.5281/zenodo.15708568.  The resource also includes text files that denote the region of the home (or intersection of regions) where each sensor is located.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.
description: Citation: Please cite the following paper when using this dataset:Cook, D., Crandall, A., Thomas, B., & Krishnan, N. (2013). CASAS: A smart home in a box. IEEE Computer, 46(7):62-69, 2013. https://doi.org/10.1109/MC.2012.328
DOI: 10.25318/3310107701-eng
title: Certification under the Canada-United States-Mexico Agreement (CUSMA) of goods that businesses or organizations purchase from or sell to the United States and reasons for not arranging for the certification, fourth quarter of 2025

description: Certification under the Canada-United States-Mexico Agreement (CUSMA) of goods that businesses or organizations purchase from or sell to the United States and reasons for not arranging for the certification, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, fourth quarter of 2025.
DOI: 10.15468/dl.dsvap7
title: Occurrence Download

description: A dataset containing 359 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "DatasetKey is one of (Banco de Datos de la Biodiversidad de la Comunitat Valenciana, Colección de plantas vasculares del herbario de la Universitat de València (VAL), CSIC-Real Jardín Botánico-Colección de Plantas Vasculares (MA))",
 "Geometry POLYGON((-1.0155 43.73647,-9.14358 44.05025,-9.21616 42.03505,-6.2407 42.14276,-6.67613 40.94788,-7.547 37.05824,-6.09555 36.06733,-3.12009 36.06733,0.94395 38.43539,0.50852 39.73098,3.91941 42.03505,3.33883 42.83843,1.52453 42.83843,-1.0155 43.73647))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Senecio angulatus"
 ]
}

 The dataset includes 359 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020703-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.29374.34888
title: A Teia Citatória da Saúde Brasileira: Índice de Atração
DOI: 10.7910/dvn/gghqhu
title: Replication Data for Employers' Language Proficiency Requirements and Hiring of Immigrants

description: This dataset contains the data and Stata do-files used in the analysis for the paper “Employers' Language Proficiency Requirements and Hiring of Immigrants”, accepted for publication in the Journal of Human Resources. The files include cleaned and de-identified data from the field experiment, together with all code required to reproduce the tables and figures in the paper. A README file is provided with detailed instructions for running the do-files.
DOI: 10.17026/ar/ornrcx
title: PAN-00171491 - solid cross pendant group A

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00171491&lt;/p&gt;
description: Modified: 2025-04-25T13:03:05+02:00
DOI: 10.15468/dl.yb2g74
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gynonectria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020704-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.15809156
title: CASAS ArWISE smartwatch dataset - free living, activity labels: Volume 4

description: The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is based on sensor data and activity labels collected from smart watches. Sensor data consist of 10Hz accelerometer, gyroscope, and relative location (distance from home location). Portions of the data are annotated with user-provided labels of their current activity.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.

Csv files for each person’s data are organized into zip files by study collection dates. Volume 4 contains data for 46 participants collected between 2023 and 2024. Each file includes a header with feature names. 

 

Features



stamp: date and time of the sensor reading (string)

yaw, pitch, roll, rotation_rate_x, rotation_rate_y, rotation_rate_z, user_acceleration_x, user_acceleration_y, user_acceleration_z: 3d movement (float)

distance_from_home: Haversine distance from the person's extracted home location (float)

course, speed, horizontal_accuracy, vertical_accuracy: float

battery_state: unplugged or charging (string)

user_activity_label: string



heart_rate: float




 

This dataset currently has 4 volumes:



Volume 1 doi: 10.5281/zenodo.15794726

Volume 2 doi: 10.5281/zenodo.15802788

Volume 3 doi: 10.5281/zenodo.15803341

Volume 4 doi: 10.5281/zenodo.15809156
description: Citation: Please cite the following paper when using this dataset:

Minor, B., Greeley, C., Holder, R., Thomas, B., Holder, L., & Cook, D. (2025). A feature-augmented transformer model to recognize functional activities from in-the-wild smartwatch data. IEEE Journal of Behavioral and Health Informatics. https://doi.org/10.1109/JBHI.2025.3586074
description: A larger version of ArWISE, containing labeled and unlabeled data for 854 participants across 20 cohorts, is available at https://datadryad.org/dataset/doi:10.5061/dryad.jdfn2z3nm. In the larger version, data are aggregated into one-minute intervals and described by extracted features.
DOI: 10.5281/zenodo.15809155
title: CASAS ArWISE smartwatch dataset - free living, activity labels: Volume 4

description: The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is based on sensor data and activity labels collected from smart watches. Sensor data consist of 10Hz accelerometer, gyroscope, and relative location (distance from home location). Portions of the data are annotated with user-provided labels of their current activity.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.

Csv files for each person’s data are organized into zip files by study collection dates. Volume 4 contains data for 46 participants collected between 2023 and 2024. Each file includes a header with feature names. 

 

Features



stamp: date and time of the sensor reading (string)

yaw, pitch, roll, rotation_rate_x, rotation_rate_y, rotation_rate_z, user_acceleration_x, user_acceleration_y, user_acceleration_z: 3d movement (float)

distance_from_home: Haversine distance from the person's extracted home location (float)

course, speed, horizontal_accuracy, vertical_accuracy: float

battery_state: unplugged or charging (string)

user_activity_label: string



heart_rate: float




 

This dataset currently has 4 volumes:



Volume 1 doi: 10.5281/zenodo.15794726

Volume 2 doi: 10.5281/zenodo.15802788

Volume 3 doi: 10.5281/zenodo.15803341

Volume 4 doi: 10.5281/zenodo.15809156
description: Citation: Please cite the following paper when using this dataset:

Minor, B., Greeley, C., Holder, R., Thomas, B., Holder, L., & Cook, D. (2025). A feature-augmented transformer model to recognize functional activities from in-the-wild smartwatch data. IEEE Journal of Behavioral and Health Informatics. https://doi.org/10.1109/JBHI.2025.3586074
description: A larger version of ArWISE, containing labeled and unlabeled data for 854 participants across 20 cohorts, is available at https://datadryad.org/dataset/doi:10.5061/dryad.jdfn2z3nm. In the larger version, data are aggregated into one-minute intervals and described by extracted features.
DOI: 10.5281/zenodo.15803340
title: CASAS ArWISE smartwatch dataset - free living, activity labels: Volume 3

description: Description

The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is based on sensor data and activity labels collected from smart watches. Sensor data consist of 10Hz accelerometer, gyroscope, and relative location (distance from home location). Portions of the data are annotated with user-provided labels of their current activity.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.

Csv files for each person’s data are organized into zip files by study collection dates. Volume 3 contains data for 43 participants collected between 2020 and 2025, divided into 2 distinct data collections. Each file includes a header with feature names. 

 

Features



stamp: date and time of the sensor reading (string)

yaw, pitch, roll, rotation_rate_x, rotation_rate_y, rotation_rate_z, user_acceleration_x, user_acceleration_y, user_acceleration_z: 3d movement (float)

distance_from_home: Haversine distance from the person's extracted home location (float)

course, speed, horizontal_accuracy, vertical_accuracy: float

battery_state: unplugged or charging (string)

user_activity_label: string

heart_rate: float


 

This dataset currently has 4 volumes:



Volume 1 doi: 10.5281/zenodo.15794726

Volume 2 doi: 10.5281/zenodo.15802788

Volume 3 doi: 10.5281/zenodo.15803341

Volume 4 doi: 10.5281/zenodo.15809156
description: Citation: Please cite the following paper when using this dataset:

Minor, B., Greeley, C., Holder, R., Thomas, B., Holder, L., & Cook, D. (2025). A feature-augmented transformer model to recognize functional activities from in-the-wild smartwatch data. IEEE Journal of Behavioral and Health Informatics. https://doi.org/10.1109/JBHI.2025.3586074
description: A larger version of ArWISE, containing labeled and unlabeled data for 854 participants across 20 cohorts, is available at https://datadryad.org/dataset/doi:10.5061/dryad.jdfn2z3nm. In the larger version, data are aggregated into one-minute intervals and described by extracted features.
DOI: 10.5281/zenodo.15803341
title: CASAS ArWISE smartwatch dataset - free living, activity labels: Volume 3

description: Description

The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is based on sensor data and activity labels collected from smart watches. Sensor data consist of 10Hz accelerometer, gyroscope, and relative location (distance from home location). Portions of the data are annotated with user-provided labels of their current activity.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.

Csv files for each person’s data are organized into zip files by study collection dates. Volume 3 contains data for 43 participants collected between 2020 and 2025, divided into 2 distinct data collections. Each file includes a header with feature names. 

 

Features



stamp: date and time of the sensor reading (string)

yaw, pitch, roll, rotation_rate_x, rotation_rate_y, rotation_rate_z, user_acceleration_x, user_acceleration_y, user_acceleration_z: 3d movement (float)

distance_from_home: Haversine distance from the person's extracted home location (float)

course, speed, horizontal_accuracy, vertical_accuracy: float

battery_state: unplugged or charging (string)

user_activity_label: string

heart_rate: float


 

This dataset currently has 4 volumes:



Volume 1 doi: 10.5281/zenodo.15794726

Volume 2 doi: 10.5281/zenodo.15802788

Volume 3 doi: 10.5281/zenodo.15803341

Volume 4 doi: 10.5281/zenodo.15809156
description: Citation: Please cite the following paper when using this dataset:

Minor, B., Greeley, C., Holder, R., Thomas, B., Holder, L., & Cook, D. (2025). A feature-augmented transformer model to recognize functional activities from in-the-wild smartwatch data. IEEE Journal of Behavioral and Health Informatics. https://doi.org/10.1109/JBHI.2025.3586074
description: A larger version of ArWISE, containing labeled and unlabeled data for 854 participants across 20 cohorts, is available at https://datadryad.org/dataset/doi:10.5061/dryad.jdfn2z3nm. In the larger version, data are aggregated into one-minute intervals and described by extracted features.
DOI: 10.7910/dvn/7mjoo0
title: Replication Data for: Fear and Favoritism in the Time of COVID-19

description: When faced with fear, do people favor those in their in-group or out-group? The measurement of this effect is confounded by the fact that some individuals respond to frightening events more strongly than others. We run an online experiment in South Korea in which we experimentally induce fear and examine its impact on a behavioral outcome measure of donations to either an in-group charity or an out-group one. We find that while the self-reported level of fear is negatively correlated with outgroup donations, experimentally induced fear positively affects such donations. We also find that the fear effect depends on social preferences, but is not significantly affected by political views or media exposure.
DOI: 10.5281/zenodo.15802787
title: CASAS ArWISE smartwatch dataset - free living, activity labels: Volume 2

description: Description

The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is based on sensor data and activity labels collected from smart watches. Sensor data consist of 10Hz accelerometer, gyroscope, and relative location (distance from home location). Portions of the data are annotated with user-provided labels of their current activity.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.

Csv files for each person’s data are organized into zip files by study collection dates. Volume 2 contains data for 28 participants collected between 2023 and 2024, divided into 3 distinct data collections. Each file includes a header with feature names. 

 

Features



stamp: date and time of the sensor reading (string)

yaw, pitch, roll, rotation_rate_x, rotation_rate_y, rotation_rate_z, user_acceleration_x, user_acceleration_y, user_acceleration_z: 3d movement (float)

distance_from_home: Haversine distance from the person's extracted home location (float)

course, speed, horizontal_accuracy, vertical_accuracy: float

battery_state: unplugged or charging (string)

user_activity_label: string

(some files) heart_rate: float


 

This dataset currently has 4 volumes:



Volume 1 doi: 10.5281/zenodo.15794726

Volume 2 doi: 10.5281/zenodo.15802788

Volume 3 doi: 10.5281/zenodo.15803341

Volume 4 doi: 10.5281/zenodo.15809156
description: Citation: Please cite the following paper when using this dataset:

Minor, B., Greeley, C., Holder, R., Thomas, B., Holder, L., & Cook, D. (2025). A feature-augmented transformer model to recognize functional activities from in-the-wild smartwatch data. IEEE Journal of Behavioral and Health Informatics. https://doi.org/10.1109/JBHI.2025.3586074
description: A larger version of ArWISE, containing labeled and unlabeled data for 854 participants across 20 cohorts, is available at https://datadryad.org/dataset/doi:10.5061/dryad.jdfn2z3nm. In the larger version, data are aggregated into one-minute intervals and described by extracted features.
DOI: 10.5281/zenodo.15802788
title: CASAS ArWISE smartwatch dataset - free living, activity labels: Volume 2

description: Description

The Activity recognition from in-the-WIld SmartwatchEs (ArWISE) dataset is based on sensor data and activity labels collected from smart watches. Sensor data consist of 10Hz accelerometer, gyroscope, and relative location (distance from home location). Portions of the data are annotated with user-provided labels of their current activity.

Note: Other CASAS smart home and smartwatch datasets are also available, look for more at https://zenodo.org/communities/casas.

Csv files for each person’s data are organized into zip files by study collection dates. Volume 2 contains data for 28 participants collected between 2023 and 2024, divided into 3 distinct data collections. Each file includes a header with feature names. 

 

Features



stamp: date and time of the sensor reading (string)

yaw, pitch, roll, rotation_rate_x, rotation_rate_y, rotation_rate_z, user_acceleration_x, user_acceleration_y, user_acceleration_z: 3d movement (float)

distance_from_home: Haversine distance from the person's extracted home location (float)

course, speed, horizontal_accuracy, vertical_accuracy: float

battery_state: unplugged or charging (string)

user_activity_label: string

(some files) heart_rate: float


 

This dataset currently has 4 volumes:



Volume 1 doi: 10.5281/zenodo.15794726

Volume 2 doi: 10.5281/zenodo.15802788

Volume 3 doi: 10.5281/zenodo.15803341

Volume 4 doi: 10.5281/zenodo.15809156
description: Citation: Please cite the following paper when using this dataset:

Minor, B., Greeley, C., Holder, R., Thomas, B., Holder, L., & Cook, D. (2025). A feature-augmented transformer model to recognize functional activities from in-the-wild smartwatch data. IEEE Journal of Behavioral and Health Informatics. https://doi.org/10.1109/JBHI.2025.3586074
description: A larger version of ArWISE, containing labeled and unlabeled data for 854 participants across 20 cohorts, is available at https://datadryad.org/dataset/doi:10.5061/dryad.jdfn2z3nm. In the larger version, data are aggregated into one-minute intervals and described by extracted features.
DOI: 10.25318/3310107701-fra
title: Certification en vertu de l’Accord Canada-États-Unis-Mexique (ACEUM) des biens que les entreprises ou les organismes achètent ou vendent aux États-Unis et raisons de ne pas prendre les dispositions nécessaires pour obtenir cette certification, quatrième trimestre de 2025

description: Certification en vertu de l’Accord Canada-États-Unis-Mexique (ACEUM) des biens que les entreprises ou les organismes achètent ou vendent aux États-Unis et raisons de ne pas prendre les dispositions nécessaires pour obtenir cette certification, selon le Système de classification des industries de l’Amérique du Nord (SCIAN), l’effectif de l’entreprise, le type d’entreprise, l’activité de l'entreprise et le propriétaire majoritaire, quatrième trimestre de 2025.
DOI: 10.5281/zenodo.14903058
title: Quantitative investigation of internal polarization in a proton exchange membrane water electrolyzer stack using distribution of relaxation times

description: The original data sets used in the paper titled: Quantitative investigation of internal polarization in a proton exchange membrane water electrolyzer stack using distribution of relaxation times

The testing conditions are indicated in the data file's name. For example ``LCWE_25_10_60C_5bara.mat'' suggests that the data are collected under 60 °C and 5 bar.
DOI: 10.5281/zenodo.14903057
title: Quantitative investigation of internal polarization in a proton exchange membrane water electrolyzer stack using distribution of relaxation times

description: The original data sets used in the paper titled: Quantitative investigation of internal polarization in a proton exchange membrane water electrolyzer stack using distribution of relaxation times

The testing conditions are indicated in the data file's name. For example ``LCWE_25_10_60C_5bara.mat'' suggests that the data are collected under 60 °C and 5 bar.