cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/19768273.v1
title: Data underlying the publication: Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma

description: Raw data of the research "<strong>Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma</strong>". <br> Copper-induced cell death has been widely investigated in human diseases as a form of programmed cell death (PCD). The newly recognized mechanism underlying copper-induced cell death provided us creative insights into the copper-related toxicity in cells, and this form of PCD was termed cuproptosis. <br> <br>
DOI: 10.4121/19768273
title: Data underlying the publication: Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma

description: Raw data of the research "<strong>Comprehensive investigation into cuproptosis in the characterization of clinical features, molecular characteristics, and immune situations of clear cell renal cell carcinoma</strong>". <br> Copper-induced cell death has been widely investigated in human diseases as a form of programmed cell death (PCD). The newly recognized mechanism underlying copper-induced cell death provided us creative insights into the copper-related toxicity in cells, and this form of PCD was termed cuproptosis. <br> <br>
DOI: 10.4121/20016194.v1
title: EULA for ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

description: This is the End-User License Agreement associated with the ConfLab dataset. <br> Dataset contains pseudonymized information. Users need to fill in the form at: https://doi.org/10.4121/20016194 and submit the form to SPCLabDatasets-insy@tudelft.nl, in order to get access to the dataset.
DOI: 10.4121/20016194
title: EULA for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

description: This is the End-User License Agreement associated with the ConfLab dataset. <br> Dataset contains pseudonymized information. Users need to fill in the form at: https://doi.org/10.4121/20016194 and submit the form to SPCLabDatasets-insy@tudelft.nl, in order to get access to the dataset.
DOI: 10.4121/20017682.v1
title: Samples for ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

description: This file contains some sample data for the ConfLab dataset to provide a general idea. <strong>sample_video.MP4</strong>: a sample video recording from one of the overhead cameras capturing part of the interaction floor. <strong>sample_sensor.csv</strong> contains columns corresponding to below (interpolated to have consistent 50Hz sampling frequency): acceleration (tri-axial accelerometer) of a subject angular velocity (tri-axial gyroscope) of a subject magnetic field (tri-axial magnetometer) of a subject the orientation of a subject in quarternions RSSI proximity measurements of a subject <br> <strong>sample_audio</strong>: a sample audio file from a subject, corresponding wav is playable <br>
DOI: 10.4121/20017682.v2
title: Samples for ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions in the Wild

description: This file contains some sample data for the ConfLab dataset to provide a general idea. <strong>sample_video.mp4</strong>: a sample video recording from one of the overhead cameras capturing part of the interaction floor <strong>sample_keypoints_annotations.mp4</strong>: visualization of the keypoint annotations corresponding to a short video segment <strong>sample_sensor.csv:</strong> sensor data with the following columns (interpolated to have consistent 50Hz sampling frequency): &lt;<em>acceleration (tri-axial accelerometer) of a subject, angular velocity (tri-axial gyroscope) of a subject, magnetic field (tri-axial magnetometer) of a subject, the orientation of a subject in quarternions, RSSI proximity measurements of a subject&gt;</em> <strong>sample_audio, sample_audio.wav</strong>: sample audio files from a subject; the wav version is playable in a standard media player <strong>README.txt</strong>: A file containing this description for convenience <br>
DOI: 10.4121/20017682
title: Samples for ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions in the Wild

description: This file contains some sample data for the ConfLab dataset to provide a general idea. <strong>sample_video.mp4</strong>: a sample video recording from one of the overhead cameras capturing part of the interaction floor <strong>sample_keypoints_annotations.mp4</strong>: visualization of the keypoint annotations corresponding to a short video segment <strong>sample_sensor.csv:</strong> sensor data with the following columns (interpolated to have consistent 50Hz sampling frequency): &lt;<em>acceleration (tri-axial accelerometer) of a subject, angular velocity (tri-axial gyroscope) of a subject, magnetic field (tri-axial magnetometer) of a subject, the orientation of a subject in quarternions, RSSI proximity measurements of a subject&gt;</em> <strong>sample_audio, sample_audio.wav</strong>: sample audio files from a subject; the wav version is playable in a standard media player <strong>README.txt</strong>: A file containing this description for convenience <br>
DOI: 10.4121/20017559.v1
title: Datasheet for ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

description: This is a self-contained documentation for the ConfLab dataset that provides a comprehensive record that summarizes the dataset.
DOI: 10.4121/20017559.v2
title: Datasheet for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

description: This datasheet provides a comprehensive documentation of the ConfLab dataset.
DOI: 10.4121/20017559
title: Datasheet for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

description: This datasheet provides a comprehensive documentation of the ConfLab dataset.
DOI: 10.4121/20017664.v1
title: Annotations for ConfLab A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

description: This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations. ------------------ <strong>./actions/speaking_status:</strong> ./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at: https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status The processed annotations consist of: ./speaking: The first row contains person IDs matching the sensor IDs, The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames). ./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation. To load these files with pandas: pd.read_csv(p, index_col=False) <br> ./raw.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. -------------------- <strong>./pose:</strong> ./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json')) The skeleton structure (limbs) is contained within each file in: f['categories'][0]['skeleton'] and keypoint names at: f['categories'][0]['keypoints'] ./raw.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. --------------------- <strong>./f_formations:</strong> seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8. First column: time stamp Second column: "()" delineates groups, "&lt;&gt;" delineates subjects, cam X indicates the best camera view for which a particular group exists. <br> phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone
DOI: 10.4121/20017664.v2
title: Annotations for ConfLab A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

description: This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations. ------------------ ./actions/speaking_status: ./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at: https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status The processed annotations consist of: ./speaking: The first row contains person IDs matching the sensor IDs, The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames). ./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation. To load these files with pandas: pd.read_csv(p, index_col=False) <br> ./raw-covfee.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. -------------------- ./pose: ./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json')) The skeleton structure (limbs) is contained within each file in: f['categories'][0]['skeleton'] and keypoint names at: f['categories'][0]['keypoints'] ./raw-covfee.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. --------------------- ./f_formations: seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8. First column: time stamp Second column: "()" delineates groups, "&lt;&gt;" delineates subjects, cam X indicates the best camera view for which a particular group exists. <br> phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone
DOI: 10.4121/20017664.v3
title: Annotations for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

description: This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations. ------------------ ./actions/speaking_status: ./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at: https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status The processed annotations consist of: ./speaking: The first row contains person IDs matching the sensor IDs, The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames). ./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation. To load these files with pandas: pd.read_csv(p, index_col=False) <br> ./raw-covfee.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. -------------------- ./pose: ./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json')) The skeleton structure (limbs) is contained within each file in: f['categories'][0]['skeleton'] and keypoint names at: f['categories'][0]['keypoints'] ./raw-covfee.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. --------------------- ./f_formations: seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8. First column: time stamp Second column: "()" delineates groups, "&lt;&gt;" delineates subjects, cam X indicates the best camera view for which a particular group exists. <br> phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone
DOI: 10.4121/20017664
title: Annotations for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

description: This file contains the annotations for the ConfLab dataset, including actions (speaking status), pose, and F-formations. ------------------ ./actions/speaking_status: ./processed: the processed speaking status files, aggregated into a single data frame per segment. Skipped rows in the raw data (see https://josedvq.github.io/covfee/docs/output for details) have been imputed using the code at: https://github.com/TUDelft-SPC-Lab/conflab/tree/master/preprocessing/speaking_status The processed annotations consist of: ./speaking: The first row contains person IDs matching the sensor IDs, The rest of the row contains binary speaking status annotations at 60fps for the corresponding 2 min video segment (7200 frames). ./confidence: Same as above. These annotations reflect the continuous-valued rating of confidence of the annotators in their speaking annotation. To load these files with pandas: pd.read_csv(p, index_col=False) <br> ./raw-covfee.zip: the raw outputs from speaking status annotation for each of the eight annotated 2-min video segments. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. -------------------- ./pose: ./coco: the processed pose files in coco JSON format, aggregated into a single data frame per video segment. These files have been generated from the raw files using the code at: https://github.com/TUDelft-SPC-Lab/conflab-keypoints To load in Python: f = json.load(open('/path/to/cam2_vid3_seg1_coco.json')) The skeleton structure (limbs) is contained within each file in: f['categories'][0]['skeleton'] and keypoint names at: f['categories'][0]['keypoints'] ./raw-covfee.zip: the raw outputs from continuous pose annotation. These were were output by the covfee annotation tool (https://github.com/josedvq/covfee) Annotations were done at 60 fps. --------------------- ./f_formations: seg 2: 14:00 onwards, for videos of the form x2xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). seg 3: for videos of the form x3xxx.MP4 in /video/raw/ for the relevant cameras (2,4,6,8,10). Note that camera 10 doesn't include meaningful subject information/body parts that are not already covered in camera 8. First column: time stamp Second column: "()" delineates groups, "&lt;&gt;" delineates subjects, cam X indicates the best camera view for which a particular group exists. <br> phone.csv: time stamp (pertaining to seg3), corresponding group, ID of person using the phone
DOI: 10.4121/20017805.v1
title: Processed-Data for ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions In-the-Wild

description: This folder contains processed data (video and wearable sensors) for 16 mins<br> of interaction that were annotated for the Conflab dataset.<br> <br> <strong>./cameras</strong><br> ./video_segments contains the overhead video recordings for 5 cameras<br> in MP4 files. These are split into 2 min segments, down-scaled to 960x540px,<br> and denoised. The resulting files are the ones that were used to annotate poses<br> and actions.<br> <br> For applications that require higher resolutions, the original video files in the<br> "data_raw" folder are at 1920x1080 resolution and a script is provided to extract<br> the same 2min segments.<br> <br> ./camera-calibration contains the camera instrinsic files obtained from:<br> https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters<br> can be calculated using the existing intrinsic parameters and the instructions<br> in the multicamera-calibration repo. The coordinates in the image are provided<br> by the crosses marked on the floor, which are visible in the video recordings.<br> The crosses are 1m apart (=100cm). <br> <br> <strong>./wearables</strong><br> contains one single dataframe with aggregated sensor information<br> (accelerometer, gyroscope, magnetometer, rotation, proximity) per each person.<br> The data has been interpolated and imputed at 50Hz (fixed). The code used to obtain<br> these files from the raw data can be found at:<br> https://github.com/TUDelft-SPC-Lab/conflab/blob/master/preprocessing/midge/preprocess.ipynb<br> <br> This code can be used to preprocess larger segments of the werable data<br> from the "data_raw" folder.<br> <br> Audio files are not part of this folder, but are provided in the "data_raw" file.
DOI: 10.4121/20017805.v2
title: Processed Data for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

description: This folder contains processed data (video and wearable sensors) for 16 mins<br> of interaction that were annotated for the Conflab dataset.<br> <br> <strong>./cameras</strong><br> ./video_segments contains the overhead video recordings for 5 cameras<br> in MP4 files. These are split into 2 min segments, down-scaled to 960x540px,<br> and denoised. The resulting files are the ones that were used to annotate poses<br> and actions.<br> <br> For applications that require higher resolutions, the original video files in the<br> "data_raw" folder are at 1920x1080 resolution and a script is provided to extract<br> the same 2min segments.<br> <br> ./camera-calibration contains the camera instrinsic files obtained from:<br> https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters<br> can be calculated using the existing intrinsic parameters and the instructions<br> in the multicamera-calibration repo. The coordinates in the image are provided<br> by the crosses marked on the floor, which are visible in the video recordings.<br> The crosses are 1m apart (=100cm). <br> <br> <strong>./wearables</strong><br> contains one single dataframe with aggregated sensor information<br> (accelerometer, gyroscope, magnetometer, rotation, proximity) per each person.<br> The data has been interpolated and imputed at 50Hz (fixed). The code used to obtain<br> these files from the raw data can be found at:<br> https://github.com/TUDelft-SPC-Lab/conflab/blob/master/preprocessing/midge/preprocess.ipynb<br> <br> This code can be used to preprocess larger segments of the werable data<br> from the "data_raw" folder.<br> <br> Audio files are not part of this folder, but are provided in the "data_raw" file.
DOI: 10.4121/20017805
title: Processed Data for ConfLab: A Data Collection Concept, Dataset, and Benchmark for Machine Analysis of Free-Standing Social Interactions in the Wild

description: This folder contains processed data (video and wearable sensors) for 16 mins<br> of interaction that were annotated for the Conflab dataset.<br> <br> <strong>./cameras</strong><br> ./video_segments contains the overhead video recordings for 5 cameras<br> in MP4 files. These are split into 2 min segments, down-scaled to 960x540px,<br> and denoised. The resulting files are the ones that were used to annotate poses<br> and actions.<br> <br> For applications that require higher resolutions, the original video files in the<br> "data_raw" folder are at 1920x1080 resolution and a script is provided to extract<br> the same 2min segments.<br> <br> ./camera-calibration contains the camera instrinsic files obtained from:<br> https://github.com/idiap/multicamera-calibration. Camera extrinsic parameters<br> can be calculated using the existing intrinsic parameters and the instructions<br> in the multicamera-calibration repo. The coordinates in the image are provided<br> by the crosses marked on the floor, which are visible in the video recordings.<br> The crosses are 1m apart (=100cm). <br> <br> <strong>./wearables</strong><br> contains one single dataframe with aggregated sensor information<br> (accelerometer, gyroscope, magnetometer, rotation, proximity) per each person.<br> The data has been interpolated and imputed at 50Hz (fixed). The code used to obtain<br> these files from the raw data can be found at:<br> https://github.com/TUDelft-SPC-Lab/conflab/blob/master/preprocessing/midge/preprocess.ipynb<br> <br> This code can be used to preprocess larger segments of the werable data<br> from the "data_raw" folder.<br> <br> Audio files are not part of this folder, but are provided in the "data_raw" file.
DOI: 10.4121/21294714
title: Replication data for: Optimizing Entanglement Generation and Distribution Using Genetic Algorithms

description: This is the data corresponding to the publication "Optimizing Entanglement Generation and Distribution Using Genetic Algorithms" (https://doi.org/10.1088/2058-9565/abfc93). It contains the raw data resulting from all the optimization runs performed (see publication for details), from parameter scans and scripts used to generate plots. <br>
DOI: 10.4121/21294714.v1
title: Replication data for: Optimizing Entanglement Generation and Distribution Using Genetic Algorithms

description: This is the data corresponding to the publication "Optimizing Entanglement Generation and Distribution Using Genetic Algorithms" (https://doi.org/10.1088/2058-9565/abfc93). It contains the raw data resulting from all the optimization runs performed (see publication for details), from parameter scans and scripts used to generate plots. <br>
DOI: 10.4121/21276624.v1
title: Dashboard COVID-19 Early Warning System – documentation and data

description: This is the documentation and the data for the interactive dashboard that predicts the spreading of COVID-19 on municipality level. The predictions are based on epidemic model that uses mobility information between municipalities. the mobility information is not a part of this dataset.