cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6645006
title: Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments

description: Snow depth, snow water equivalent and basal ice thickness measurements were taken during the SIOS SnowPilot campaign in Spring 2022. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe.
DOI: 10.5281/zenodo.10204742
title: Supplementary material to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" : Pleiades snow depth maps analysis

description: This Folder contains :
     - necessary codes to reprduce Fig. 5, 12 and 13 of the third version of the submitted manuscript SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme"
     - necessary data to run these codes, including extracts of simulation outputs
    - A readme.txt which explains how to run everything
DOI: 10.5281/zenodo.10204743
title: Supplementary material to "SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme" : Pleiades snow depth maps analysis

description: This Folder contains :
     - necessary codes to reprduce Fig. 5, 12 and 13 of the third version of the submitted manuscript SnowPappus v1.0, a blowing-snow model for large-scale applications of Crocus snow scheme"
     - necessary data to run these codes, including extracts of simulation outputs
    - A readme.txt which explains how to run everything
DOI: 10.5281/zenodo.10204780
title: Arabidopsis thaliana real dataset
DOI: 10.5281/zenodo.10204779
title: Arabidopsis thaliana real dataset
DOI: 10.6084/m9.figshare.24632541
title: ASE2021F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members of ASE 2021 conference.
DOI: 10.6084/m9.figshare.24632541.v1
title: ASE2021F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members of ASE 2021 conference.
DOI: 10.6084/m9.figshare.24632700
title: ESEC2021F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ESEC 2021 conference. Data was collected from 2019-2023 editions of the conference.
DOI: 10.6084/m9.figshare.24632700.v1
title: ESEC2021F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ESEC 2021 conference. Data was collected from 2019-2023 editions of the conference.
DOI: 10.6084/m9.figshare.24632670
title: ICSE2022F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2022 conference.
DOI: 10.6084/m9.figshare.24632670.v1
title: ICSE2022F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2022 conference.
DOI: 10.6084/m9.figshare.24632685
title: ICSE2020F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2022 conference.
DOI: 10.6084/m9.figshare.24632685.v1
title: ICSE2020F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2022 conference.
DOI: 10.6084/m9.figshare.24632694
title: ASE2020F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ASE 2020 conference.
DOI: 10.6084/m9.figshare.24632586
title: ASE2023F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ASE 2023 conference.
DOI: 10.6084/m9.figshare.24632667
title: ESEC2019F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ESEC 2019 conference.
DOI: 10.6084/m9.figshare.24632667.v1
title: ESEC2019F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ESEC 2019 conference.
DOI: 10.6084/m9.figshare.24632619
title: ESEC2023F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ESEC 2023 conference.
DOI: 10.6084/m9.figshare.24632619.v1
title: ESEC2023F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ESEC 2023 conference.
DOI: 10.6084/m9.figshare.24632595
title: ICSE2019F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2019 conference.
DOI: 10.6084/m9.figshare.24632595.v1
title: ICSE2019F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2019 conference.
DOI: 10.6084/m9.figshare.24632643
title: ICSE2021F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2021 conference.
DOI: 10.6084/m9.figshare.24632643.v1
title: ICSE2021F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ICSE 2021 conference.
DOI: 10.6084/m9.figshare.24632559
title: ASE2022F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ASE 2022 conference.
DOI: 10.6084/m9.figshare.24632559.v1
title: ASE2022F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ASE 2022 conference.
DOI: 10.6084/m9.figshare.24632547
title: ESEC2022F.xlsx

description: This file presents data about gender, geographical area, role, appartenance to academic/industry of chairs and commitee members from ESEC 2022 conference.
DOI: 10.34740/kaggle/ds/2764127
title: Motherboard Production Defects
DOI: 10.5281/zenodo.10070610
title: Gridded active layer thickness across northern permafrost regions from 2003 to 2020

description: This dataset provides the annual gridded active layer thickness (ALT) across northern permafrost regions (NPR) at 1 km resolution for the period 2003–2020. The ALT in permafrost regions refers to the upper layer of soil that thaws and refreezes annually as a result of seasonal temperature variations. It is a critical parameter in permafrost studies because it determines the depth to which plant roots can penetrate and influences various ecological and engineering processes. This dataset was produced based on the relationship between available ALT site measurements (2966 site-years) and satellite observations of annual gridded predictors, including vegetation, temperature, soil, and topography, using a Random Forest (RF) approach. The annual ALT map for the NPR was generated using the ensemble mean of ALT from the ten best RF predictions. Extensive uncertainty analysis was also conducted. 
The dataset can be viewed at https://liuzh833.users.earthengine.app/view/altv1
DOI: 10.5281/zenodo.10070609
title: Gridded active layer thickness across northern permafrost regions from 2003 to 2020

description: This dataset provides the annual gridded active layer thickness (ALT) across northern permafrost regions (NPR) at 1 km resolution for the period 2003–2020. The ALT in permafrost regions refers to the upper layer of soil that thaws and refreezes annually as a result of seasonal temperature variations. It is a critical parameter in permafrost studies because it determines the depth to which plant roots can penetrate and influences various ecological and engineering processes. This dataset was produced based on the relationship between available ALT site measurements (2966 site-years) and satellite observations of annual gridded predictors, including vegetation, temperature, soil, and topography, using a Random Forest (RF) approach. The annual ALT map for the NPR was generated using the ensemble mean of ALT from the ten best RF predictions. Extensive uncertainty analysis was also conducted. 
The dataset can be viewed at https://liuzh833.users.earthengine.app/view/altv1
DOI: 10.5281/zenodo.10203720
title: Organ-on-a-Chip (OOC) Image Dataset

description: Overview: This dataset contains 3000+ images generated from OOC (organ-on-a-chip) setup with different cell types. The images were generated by an automated brightfield microscopy setup; for each image, such parameters as cell type, time after seeding, and class label ('good' or 'bad' sample quality as assessed by a biology expert) are provided. Furthermore, for some images, seeding density and flow rate are given as well. The dataset can be used for training machine learning classifiers for the automated analysis of the data generated with OOC setup, allowing to create more reliable tissue models and automate decision making processes for growing OOC.
The dataset comprises images of OOC samples from the following cell lines:

A549 (human lung adenocarcinoma alveolar basal epithelial cells, CCL-185, ATTC)
Caco-2 (colorectal adenocarcinoma epithelial cells, HTB-37, ATCC)
HPMEC (human pulmonary microvascular endothelial cells; 3000, ScienCell)
HUVEC (human umbilical vein endothelial cells, CRL-1730, ATCC)
NHBE (normal human bronchial epithelial cells, CC-2541, Lonza)
HSAEC (human small airway epithelial cells, PCS-301-010, ATCC)
Structure of the dataset: The dataset is split into three main folders that correspond to the data split for training machine learning models, i.e., 'train', 'val', and 'test'. The train/val/test split is done proportionally with respect to the class labels, cell lines, and time after seeding (see below), yet the data can be split or merged in other ways to suit the needs of prospective users of the dataset. Within each of the main folders, there are a 'bad' and a 'good' folder with the images corresponding to the respective class labels (see 'Overview' above). The images in 'bad' / 'ģood' folders are further subdivided into folders corresponding to respective cell lines, which are in their turn subdivided into folders corresponding to the different times after seeding. Therefore, it is easy to find images of interest, e.g., '4+ days' 'good' images of the cell line A549 from the 'train' dataset. Further information about the images is available in the file 'OOC_datasheet.xlsx'. 
Acknowledgement: The work presented in this paper was supported by the project 'AI-improved organ on chip cultivation for personalised medicine (AimOOC)' (contract with Central Finance and Contracting Agency of Republic of Latvia no. 1.1.1.1/21/A/079; the project is co-financed by REACT-EU funding for mitigating the consequences of the pandemic crisis).
DOI: 10.5281/zenodo.10203721
title: Organ-on-a-Chip (OOC) Image Dataset

description: Overview: This dataset contains 3000+ images generated from OOC (organ-on-a-chip) setup with different cell types. The images were generated by an automated brightfield microscopy setup; for each image, such parameters as cell type, time after seeding, and class label ('good' or 'bad' sample quality as assessed by a biology expert) are provided. Furthermore, for some images, seeding density and flow rate are given as well. The dataset can be used for training machine learning classifiers for the automated analysis of the data generated with OOC setup, allowing to create more reliable tissue models and automate decision making processes for growing OOC.
The dataset comprises images of OOC samples from the following cell lines:

A549 (human lung adenocarcinoma alveolar basal epithelial cells, CCL-185, ATTC)
Caco-2 (colorectal adenocarcinoma epithelial cells, HTB-37, ATCC)
HPMEC (human pulmonary microvascular endothelial cells; 3000, ScienCell)
HUVEC (human umbilical vein endothelial cells, CRL-1730, ATCC)
NHBE (normal human bronchial epithelial cells, CC-2541, Lonza)
HSAEC (human small airway epithelial cells, PCS-301-010, ATCC)
Structure of the dataset: The dataset is split into three main folders that correspond to the data split for training machine learning models, i.e., 'train', 'val', and 'test'. The train/val/test split is done proportionally with respect to the class labels, cell lines, and time after seeding (see below), yet the data can be split or merged in other ways to suit the needs of prospective users of the dataset. Within each of the main folders, there are a 'bad' and a 'good' folder with the images corresponding to the respective class labels (see 'Overview' above). The images in 'bad' / 'ģood' folders are further subdivided into folders corresponding to respective cell lines, which are in their turn subdivided into folders corresponding to the different times after seeding. Therefore, it is easy to find images of interest, e.g., '4+ days' 'good' images of the cell line A549 from the 'train' dataset. Further information about the images is available in the file 'OOC_datasheet.xlsx'. 
Acknowledgement: The work presented in this paper was supported by the project 'AI-improved organ on chip cultivation for personalised medicine (AimOOC)' (contract with Central Finance and Contracting Agency of Republic of Latvia no. 1.1.1.1/21/A/079; the project is co-financed by REACT-EU funding for mitigating the consequences of the pandemic crisis).
DOI: 10.34740/kaggle/dsv/489236
title: Artificial Lunar Landscape Dataset
DOI: 10.34740/kaggle/dsv/7045859
title: Sri Lankan Rupee Rhythms: 13-Year Forex Dance
DOI: 10.5281/zenodo.10159038
title: Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset

description: Dataset description
This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the OpenDR agricultural dataset generator tool.
It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.
Folder configuration
The dataset contains 4 folders for different lighting conditions:

noon cloudy
noon stormy
dawn cloudy
dusk
Each contains UAV and UGV folders. UAV folder includes:

annotations: contains segmented images in JPG files and annotations in TXT files.
camera: contains generated RGB images.
gps: contains the three-axis location of global positioning sensor saved in TXT files.
inertial unit: contains the inertial unit date in TXT files.
UGV folder includes:

annotations: contains segmented images in JPG files and annotations in TXT files.
front_bottom_camera: contains generated RGB images.
Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.
imu_robotti: contains the inertial unit date in TXT files.
velodyne: contains lidar data in PCD files.
Data format
The dataset includes

The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.
The GPS measurement TXT files include the robot position in x, y, and z order.
Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.
File naming convention
Each data is named "s_i{_segmented, _annotation}.ext", where:

s denotes the simulated time in seconds.
i denotes the index counting every 10ms of simulated time.
ext denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.
Labels _segmented and _annotation appended to the name for segmentation image and object annotations, respectively.
Each segmented image uses the following RGB color mapping:

Tree: 0.1, 0.4, 0.0
Apple Tree: 0.85, 0.49, 0.57
Cow: 0.380, 0.220, 0.137
Sheep: 0.937, 0.921, 0.862
Fox: 0.992, 0.376, 0.086
Barn: 0.625, 0.293, 0.226
Cat: 0.870, 0.580, 0.0
Deer: 0.415, 0.364, 0.302
Human: 1.0, 0.855, 0.672
DOI: 10.5281/zenodo.10159039
title: Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset

description: Dataset description
This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the OpenDR agricultural dataset generator tool.
It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.
Folder configuration
The dataset contains 4 folders for different lighting conditions:

noon cloudy
noon stormy
dawn cloudy
dusk
Each contains UAV and UGV folders. UAV folder includes:

annotations: contains segmented images in JPG files and annotations in TXT files.
camera: contains generated RGB images.
gps: contains the three-axis location of global positioning sensor saved in TXT files.
inertial unit: contains the inertial unit date in TXT files.
UGV folder includes:

annotations: contains segmented images in JPG files and annotations in TXT files.
front_bottom_camera: contains generated RGB images.
Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.
imu_robotti: contains the inertial unit date in TXT files.
velodyne: contains lidar data in PCD files.
Data format
The dataset includes

The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.
The GPS measurement TXT files include the robot position in x, y, and z order.
Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.
File naming convention
Each data is named "s_i{_segmented, _annotation}.ext", where:

s denotes the simulated time in seconds.
i denotes the index counting every 10ms of simulated time.
ext denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.
Labels _segmented and _annotation appended to the name for segmentation image and object annotations, respectively.
Each segmented image uses the following RGB color mapping:

Tree: 0.1, 0.4, 0.0
Apple Tree: 0.85, 0.49, 0.57
Cow: 0.380, 0.220, 0.137
Sheep: 0.937, 0.921, 0.862
Fox: 0.992, 0.376, 0.086
Barn: 0.625, 0.293, 0.226
Cat: 0.870, 0.580, 0.0
Deer: 0.415, 0.364, 0.302
Human: 1.0, 0.855, 0.672
DOI: 10.6084/m9.figshare.22811900
title: Physiological measurements

description: DescriptionThe data are included into an Excel spreadsheet.<br>The "Heat exposure" sheet contains data collected at the "Pre-acclimation visit" and "Post-acclimation visit", and "Normothermic" and "Hyperthermic" measurements are reported. The following variables are available.<b>Tcore:</b> core temperature (°C)<b>Tskin:</b> skin temperature (°C)<b>Tbody:</b> body temperature (°C)<b>LSR:</b> local sweat rate (mg/min/cm2)<b>LDF</b>: laser-Doppler flux / skin perfusion units (units)<b>CVC:</b> cutaneous vascular conductance (units/mmHg)<b>HR:</b> heart rate (bpm)<b>SBP:</b> systolic blood pressure (mmHg)<b>DBP:</b> diastolic blood pressure (mmHg)<b>MAP:</b> mean arterial blood pressure (mmHg)<b>SSNA:</b> skin sympathetic nerve activity (�seline)<b>Change (post-pre) in nude body weight </b>(kg)<b>Heat exposure duration </b>(s)<b>Sweat rate </b>(L/h)
DOI: 10.6084/m9.figshare.22811900.v2
title: Physiological measurements

description: Description The data are included into an Excel spreadsheet. <br> The "Heat exposure" sheet contains data collected at the "Pre-acclimation visit" and "Post-acclimation visit", and "Normothermic" and "Hyperthermic" measurements are reported. The following variables are available. <strong>Tcore:</strong> core temperature <strong>Tskin:</strong> skin temperature <strong>Tbody:</strong> body temperature <strong>LSR:</strong> local sweat rate <strong>CVC:</strong> cutaneous vascular conductance <strong>HR:</strong> heart rate <strong>SBP:</strong> systolic blood pressure <strong>DBP:</strong> diastolic blood pressure <strong>MBP:</strong> mean arterial blood pressure <strong>SSNA:</strong> skin sympathetic nerve activity
DOI: 10.5522/04/22128671.v1
title: OCT5k: A dataset of multi-disease and multi-graded annotations for retinal layers

description: The thickness and appearance of retinal layers are essential markers for diagnosing and studying eye diseases. Despite the increasing availability of imaging devices to scan and store large amounts of data, analyzing retinal images and generating trial endpoints has remained a manual, error-prone, and time-consuming task. In particular, the lack of large amounts of high-quality labels for different diseases hinders the development of automated algorithms. Therefore, we have compiled 5016 pixel-wise manual labels for 1672 optical coherence tomography (OCT) scans featuring two different diseases as well as healthy subjects to help democratize the process of developing novel automatic techniques. We also collected 4698 bounding box annotations for a subset of 566 scans across 9 classes of disease biomarker. Due to variations in retinal morphology, intensity range, and changes in contrast and brightness, designing segmentation and detection methods that can generalize to different disease types is challenging. While machine learning-based methods can overcome these challenges, high-quality expert annotations are necessary for training. Publicly available annotated image datasets typically contain few images and/or only cover a single type of disease, and most are only annotated by a single grader. To address this gap, we present a comprehensive multi-grader and multi-disease dataset fortraining machine learning-based algorithms. The proposed dataset covers three subsets of scans (Age-related Macular Degeneration, Diabetic Macular Edema, and healthy) and annotations for two types of tasks (semantic segmentation and object detection).
DOI: 10.5281/zenodo.10204015
title: Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage: podaci s vjetroelektrane - dio 4

description: Prikupljeni podaci korišteni u sklopu doktorske disertacije "Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage". Podaci uključuju mjerenja radne snage svakog vjetroagregata u vjetroelektrani i korigirane brzine vjetra mjerena na gondoli vjetroagregata u periodu 16.10.2021. do 31.12.2021. Vremenska rezolucija podataka je 1 s.
DOI: 10.5281/zenodo.10204016
title: Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage: podaci s vjetroelektrane - dio 4

description: Prikupljeni podaci korišteni u sklopu doktorske disertacije "Analiza odbačene proizvodnje hibridnih elektrana uslijed ograničenja priključne snage". Podaci uključuju mjerenja radne snage svakog vjetroagregata u vjetroelektrani i korigirane brzine vjetra mjerena na gondoli vjetroagregata u periodu 16.10.2021. do 31.12.2021. Vremenska rezolucija podataka je 1 s.