cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17864/1947.000332
title: Images supporting 'Fully automated platelet differential interference contrast image analysis via deep learning'

description: This dataset supports the publication 'Fully automated platelet differential interference contrast image analysis via deep learning' submitted to the journal Scientific Reports. All data was gathered or generated at the University of Reading from 2020 to 2021. This dataset consists of i) 120 training images used to train a convolutional neural network (CNN) to automate platelet analyses, ii) 12 test images independent from the training images to test the CNN performance, and iii) a total of 225 8-bit images (representative of 3 independent experiments, with five fields of view captured per condition) of platelets spread over three different substrates (CRP-XL, fibrinogen, and vWF) and in the presence or absence of inhibitors (dasatinib, ibrutinib and PRT-060318) or an agonist (thrombin). These inhibitors and agonist are known to impact platelet morphology, and were used to assess the CNNï¿½s performance on morphological extremes. The original 16-bit images with dimensions 2424x2424 were rescaled and converted to 970x970 8-bit images to reduce the file size using ImageJ. All images within this dataset are the 8-bit images.
description: All images were captured by Köhler illuminated Nomarski differential interference contrast (DIC) optics using a Nikon eclipse Ti2 inverted microscope, equipped with a Nikon DS-Qi2 camera, and visualised using a 100x oil immersion objective lens. NIS Elements software was used for image capture and each original image was acquired as .ND2 image files.
description: All original images were rescaled and resized to 8-bit .TIF image files prior to processing using Fiji ImageJ. Training images were manually annotated by drawing round the perimeter of each platelet in each image, and labelled images generated by using the LOCI plugin for Fiji ImageJ as per instructions detailed for the Usiigaci pipeline.
DOI: 10.57967/hf/0668
title: testdata
DOI: 10.5281/zenodo.7949582
title: Dataset TrainBatch1 for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)

description: Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22) This is the TrainBatch1. We provide the casename correspondence information in the TrainBatch1_CaseInfo.csv file. It is convinient for researchers to locate the original cases in LIDC-IDRI with our provided full airway annotation for other research purposes. If you use this dataset in your research, you must cite the papers in the <strong>References </strong>below !!!
description: {"references": ["Zhang M, Wu Y, Zhang H, et al. Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation[J]. arXiv preprint arXiv:2303.05745, 2023.", "Zhang M, Zhang H, Yang G Z, et al. CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs[C]//Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part I. Cham: Springer Nature Switzerland, 2022: 506-516.", "Zheng H, Qin Y, Gu Y, et al. Alleviating class-wise gradient imbalance for pulmonary airway segmentation[J]. IEEE Transactions on Medical Imaging, 2021, 40(9): 2452-2462.", "Yu W, Zheng H, Zhang M, et al. BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022: 1-5.", "Qin Y, Chen M, Zheng H, et al. Airwaynet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019: 212-220."]}
DOI: 10.5281/zenodo.6590744
title: Dataset TrainBatch1 for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22)

description: Dataset for the MICCAI-2022-Challenge: Airway Tree Modeling (ATM'22) This is the TrainBatch1. We provide the casename correspondence information in the TrainBatch1_CaseInfo.csv file. It is convinient for researchers to locate the original cases in LIDC-IDRI with our provided full airway annotation for other research purposes. If you use this dataset in your research, you must cite the papers in the <strong>References </strong>below !!!
description: {"references": ["Zhang M, Wu Y, Zhang H, et al. Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation[J]. arXiv preprint arXiv:2303.05745, 2023.", "Zhang M, Zhang H, Yang G Z, et al. CFDA: Collaborative Feature Disentanglement and Augmentation for Pulmonary Airway Tree Modeling of COVID-19 CTs[C]//Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part I. Cham: Springer Nature Switzerland, 2022: 506-516.", "Zheng H, Qin Y, Gu Y, et al. Alleviating class-wise gradient imbalance for pulmonary airway segmentation[J]. IEEE Transactions on Medical Imaging, 2021, 40(9): 2452-2462.", "Yu W, Zheng H, Zhang M, et al. BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding[C]//2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE, 2022: 1-5.", "Qin Y, Chen M, Zheng H, et al. Airwaynet: a voxel-connectivity aware approach for accurate airway segmentation using convolutional neural networks[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019: 212-220."]}
DOI: 10.13140/rg.2.2.16979.76326
title: ERA-Interim backup GMMIP-work
DOI: 10.5255/ukda-sn-6533-8
title: National Diet and Nutrition Survey Years 1-8, 2008/09-2015/16
DOI: 10.5066/p9a7j0an
title: Helicopter-based videos and field measurements of flow depth and velocity from the Tanana River, Alaska, acquired on July 24, 2019

description: This data release includes videos acquired from a helicopter and field measurements of flow depth and velocity from the Tanana River near Nenana, Alaska, obtained on July 24, 2019. This parent data release includes links to child pages for two data sets produced during the study: 1. Acoustic Doppler Current Profiler (ADCP) field measurements of flow depth and velocity from the Tanana River, Alaska, collected on July 24, 2019. 2. Videos of the Tanana River, Alaska, acquired from a helicopter on July 24, 2019. Please refer to the individual child pages for further detail about each data set. Overall, these data were used to assess the potential to estimate surface flow velocities and water depths in sediment-laden rivers from helicopter-based, hovering image sequences using particle image velocimetry (PIV) techniques and flow resistance equations. The field measurements of flow velocity and water depth were used for accuracy assessment. Remote sensing of flow velocities and depths could provide a more efficient, cost-effective alternative to conventional field-based methods of measuring channel hydraulics and thus become an important component of non-contact approaches to streamgaging.
DOI: 10.5066/p9ij20o4
title: Field measurements of flow velocity and optical image sequences acquired from the Salcha and Tanana Rivers in Alaska in 2018 and 2019 and used for particle image velocimetry (PIV)

description: This data release includes field measurements of flow velocity and optical image sequences used to derive remotely sensed estimates of surface flow velocities via particle image velocimetry (PIV) from two rivers in Alaska. These data were acquired from the Salcha River on August 31, 2018, and the Tanana River on July 24, 2019. The purpose of this study was to assess the feasibility of inferring flow velocities from optical image sequences acquired from a helicopter hovering above each river by tracking water surface features via various PIV algorithms and to develop a modular workflow for performing this type of analysis. Remote sensing of flow velocity could provide a more efficient, cost-effective alternative to conventional field-based methods of measuring velocity and become an important component of non-contact approaches to streamgaging. This parent data release includes links to child pages for several data sets produced during the study: Acoustic Doppler Current Profiler (ADCP) field measurements of flow velocity from the Salcha River collected on August 31, 2018. ADCP field measurements of flow velocity from the Tanana River collected on July 24, 2019. High resolution optical image sequence acquired from the Salcha River on August 31, 2018, and used to perform PIV. High frame rate video acquired from the Tanana River on July 24, 2019, and used to perform PIV. Please refer to the individual child pages for further detail about each data set. Overall, these data were used to assess the potential to estimate surface flow velocities in sediment-laden rivers from helicopter-based, hovering image sequences using particle image velocimetry (PIV) techniques.
DOI: 10.17632/v4wnvzkgvm.1
title: Floods1951-2020

description: 1951-2020 flood data based on EM-DAT database
DOI: 10.25740/wv398fc4383
title: Next Generation Spacecraft Pose Estimation Dataset (SPEED+)

description: SPEED+ is the next-generation dataset for spacecraft pose estimation with specific emphasis on the robustness of Machine Learning (ML) models across the domain gap. Similar to its predecessor, SPEED+ consists of images of the Tango spacecraft from the PRISMA mission. SPEED+ consists of three different domains of imageries from two distinct sources. The first source is the OpenGL-based Optical Stimulator camera emulator software of Stanford’s Space Rendezvous Laboratory (SLAB), which is used to create the synthetic domain comprising 59,960 synthetic images. The labeled synthetic domain is split into 80:20 train/validation sets and is intended to be the main source of training of an ML model. The second source is the Testbed for Rendezvous and Optical Navigation (TRON) facility at SLAB, which is used to generate two simulated Hardware-In-the-Loop (HIL) domains with different sources of illumination: lightbox and sunlamp. Specifically, these two domains are constructed using realistic illumination conditions using lightboxes with diffuser plates for albedo simulation and a sun lamp to mimic direct high-intensity homogeneous light from the Sun. Compared to synthetic imagery, they capture corner cases, stray lights, shadowing, and visual effects in general which are not easy to obtain through computer graphics. The lightbox and sunlamp domains are unlabeled and thus intended mainly for testing, representing a typical scenario in developing a spaceborne ML model in which the labeled images from the target space domain are not available prior to deployment. SPEED+ is made publicly available to the aerospace community and beyond as part of the second international Satellite Pose Estimation Competition (SPEC2021) co-hosted by SLAB and the Advanced Concepts Team (ACT) of the European Space Agency.

The construction of the TRON testbed was partly funded by the U.S. Air Force Office of Scientific Research (AFOSR) through the Defense University Research Instrumentation Program (DURIP) contract FA9550-18-1-0492, titled High-Fidelity Verification and Validation of Spaceborne Vision-Based Navigation. The SPEED+ dataset is created using the TRON testbed by SLAB at Stanford University. The post-processing of the raw images is reviewed by ACT to meet the quality requirement of SPEC2021.
DOI: 10.5066/f73f4nh4
title: Repeat microgravity data from Albuquerque and Bernalillo County, New Mexico, 2016-2021 (ver. 3.0, March 2023)

description: This dataset represents the network-adjusted results of relative- and absolute-gravity surveys. Relative-gravity surveys were carried out using a Zero Length Spring, Inc. Burris relative-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software GSadjust (https://github.com/jkennedy-usgs/sgp-gsadjust).
DOI: 10.5066/p90ngknr
title: The distribution and structure of mangroves (Avicennia germinans and Rhizophora mangle) near a rapidly changing range limit in the northeastern Gulf of Mexico

description: In coastal wetlands, one of the most striking examples of climate change is the poleward range expansion of mangrove forests in response to warming winters. In North America, the Cedar Key region has often been considered the range limit for mangroves along the western coast of Florida (USA). However, within the past several decades, robust stands of Avicennia germinans and Rhizophora mangle have been observed in the Apalachicola Bay region, which is 200 km northwest of Cedar Key. This dataset characterizes the distribution and structure of the mangroves in the Apalachicola Bay area of Florida identified via extensive ground surveys and photointerpretation of aerial imagery from 2018 to 2019.
DOI: 10.5066/p9zy5lk1
title: Satellite video and field measurements of flow velocity acquired from the Tanana River in Alaska and used for particle image velocimetry (PIV)

description: This data release includes a video acquired from a satellite and field measurements of flow velocity from the Tanana River in Alaska that were used to derive remotely sensed estimates of surface flow velocities via particle image velocimetry (PIV). The field data were collected on July 24, 2019, in cooperation with the USGS Alaska Science Center, and the satellite video was obtained on July 14, 2020. The purpose of this study was to assess the feasibility of inferring flow velocities from video acquired from a satellite in orbit above the river by tracking water surface features via a Particle Image Velocimetry (PIV) algorithm. Remote sensing of flow velocity could provide a more efficient, cost-effective alternative to conventional field-based methods of measuring velocity and become an important component of non-contact approaches to streamgaging. This parent data release includes links to child pages for several data sets produced during the study: Field measurements of flow velocity from the Tanana River, Alaska, July 24, 2019 - these data were collected with an Acoustic Doppler Current Profiler (ADCP) Geo-referenced orthophotos of the Tanana River, Alaska, acquired July 24, 2019 - these high spatial resolution orthophotos were used as a base for geo-referencing images extracted from the video Satellite video and individual image frames from the Tanana River, July 14, 2020, for Particle Image Velocimetry - these data were used to infer surface flow velocities. Please refer to the individual child pages for further detail about each data set. Overall, these data were used to assess the potential to estimate surface flow velocities in sediment-laden rivers from satellite video using particle image velocimetry (PIV) techniques.
DOI: 10.5281/zenodo.3582932
title: Data set for initializing and forcing of high-resolution local area model implementations in two ATLAS case study areas, Rockall Bank and Condor Seamount

description: The data set includes all necessary data for set up, initial and boundary conditions of high-resolution local area model implementations using the ROMS-AGRIF model in two case study areas, Rockall Bank and Condor Seamount, conducted as part of the EU ATLAS project. The data include all computational grids, initialization fields (temperature, salinity) and boundary conditions (temperature salinity, currents, sea surface height) for each case study area.
DOI: 10.5281/zenodo.7893943
title: Asphalt profile temperatures and weather data of CyPaTs test track

description: This dataset includes asphalt temperature measurements and weather data of CyPaTs test track located at Campus Groenenborger, University of Antwerp, Antwerp, Belgium. The recorded asphalt temperature measurements and weather data is between March 17<sup>th</sup>, 2021, and March 14<sup>th</sup>, 2022. There are five sheets in each excel file, containing asphalt temperatures at various depths (near-surface, 4cm, 7cm and 10 below asphalt surface), and corresponding weather data. The total number of data points is 371707, with each of these data points including information on weather parameters and 45 sensors embedded in different layers of the asphalt pavement. Date (time): time of the recorded data TC_x-x: label of the temperature sensor embedded in asphalt pavement, in °C Ta: ambient air temperature, in °C RH: relative humidity, in % FF: wind speed, in m/s SR: solar radiation, in W/m<sup>2</sup>
DOI: 10.17632/tr3y82y2jy.2
title: Socioeconomic Status and Treatment Outcomes for Women with Genito-Pelvic Pain Penetration Disorder: A dataset.

description: Dyspareunia, a common sexual disorder, can be effectively treated through multimodal physiotherapy approaches, including education. However, it's unclear whether socioeconomic factors influence the effectiveness of educational therapies for dyspareunia. This article presents a dataset used in a pilot randomized controlled trial that aimed to investigate any potential correlation between socioeconomic status and the outcomes of a therapeutic educational program for dyspareunia. The study evaluated the impact of the program on 69 women with dyspareunia and measured pain intensity, pain-related outcomes, and sexual functioning over time. Socioeconomic status measurements were collected in February 2022, and the study found no significant correlation between any of the outcomes of the intervention and socioeconomic status measurements. These findings suggest that education is a powerful tool that can improve sexuality outcomes for patients with dyspareunia, regardless of their socioeconomic background.
DOI: 10.3886/icpsr01204.v1
title: Inflation-Target Design: Changing Inflation Performance and Persistence in Industrial Countries
title: Version 1

description: This research explores the behavior of inflation in countries that formally target inflation relative to countries with historically good inflation records but which do not target the rate of change in the consumer price index (CPI). The author examines the design of inflation targets and the circumstances under which they were introduced. The author also asks, via some simple econometric tests, whether the introduction of targets had any statistically discernible impact on inflation persistence, and additionally, whether inflation persistence changed in the select countries that have no formal inflation targets. Finally, the author assesses, via a discussion of the behavior of private sector inflation forecasts, the credibility of inflation targets. Descriptive and econometric evidence suggest that the mere adoption of an inflation target is insufficient in delivering a consistently better inflation performance, nor does it significantly affect inflationary expectations. It remains to be seen whether inflation targeting can withstand pressures stemming from sustained breaches of targets, should inflation policies in the major industrialized countries begin to diverge. After all, the current disinflation, as this study has shown, is an international phenomenon.
description: Datasets: 
 
 DS1: Dataset
description: The file submitted for release is 9903PS.DAT.
These data are part of ICPSR's Publication-Related Archive and are distributed exactly as they arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.
DOI: 10.5281/zenodo.7619104
title: A long-term monthly dataset of cloud fraction over the Arctic based on multiple satellite products using cumulative distribution function matching and Bayesian maximum entropy

description: The low accuracy of satellite Cloud fraction (CF) over the Arctic seriously restricts accurate assessment of regional and global radiant energy balance under the changing climate. Previous studies have reported that not a single satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties is an effective way to produce more spatiotemporally complete and accurate CF data record. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and Bayesian maximum entropy (BME) method to produce a syncretic 1°×1° CF dataset in the Arctic during 2000-2020. The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM. The fused CF product is more consistent with the active satellite data GEWEX-CALIPSO and the ground-based observation data CRU TS4.05.
DOI: 10.5281/zenodo.7508694
title: The observed and simulated datasets of "21·7" Henan extremely heavy rainfall event

description: The uploaded files are the observed and simulated datasets of “21·7” Henan extremely heavy rainfall event, including OTT disdrometers, Radar, national and regional gauges, and WRFOUT files.
DOI: 10.6084/m9.figshare.22801268
title: Datasets used in the usage demonstration of DeSide

description: The dataset in this folder was used to demonstrate the usage of DeSide (https://github.com/OnlyBelter/DeSide).
DOI: 10.6084/m9.figshare.22801268.v1
title: Datasets used in the usage demonstration of DeSide

description: The dataset in this folder was used to demonstrate the usage of DeSide (https://github.com/OnlyBelter/DeSide).
DOI: 10.17632/nt9gjckk62.2
title: Optimization Data

description: Multi-objective Differential Evaluation optimization results which are obtained from Pareto optimal sets.
DOI: 10.17864/1947.283
title: An eddy tracking dataset for N512-12, N216-12 and N216-025 model configurations of HadGEM3-GC3.1

description: Closed coherent mesoscale eddies are identified and tracked daily in the global ocean for 20 years from sea surface height (SSH) using an eddy tracking algorithm, described by Moreton et al., 2020. The dataset provided here supplies the properties associated with each eddy: the latitude, longitude, radius, amplitude, number of days the eddy is tracked for and model time. Eddies are tracked in three coupled climate model HadGEM3-GC3.1 configurations: N512-12, N216-12 and N216-12.
description: Eddy tracking algorithm
DOI: 10.17632/n4w7kcyvpk.1
title: Infant vaccination uptake before and after COVID-19 pandemic in Ibadan, Nigeria

description: A data showing infant vaccination from 2 rural and three urban vaccination clinics in Ibadan, Nigeria about infant vaccination uptake one year before and a year after the onset of COVID-19 pandemic. data were extracted from the infant vaccination records of the clinics. Timeliness was defined as receiving the scheduled vaccine within 2 weeks of the appointment date.
DOI: 10.17864/1947.000339
title: Human Connectome Project thalamic parcellation

description: This dataset is associated with the following publication: Williams, Brendan., Roesch, Etienne., Christakou, Anastasia. (2022): Systematic validation of an automated thalamic parcellation technique using anatomical data at 3T. NeuroImage. https://doi.org/10.1016/j.neuroimage.2022.119340. The dataset contains thalamic segmentation data for Human Connectome Project [1] participants using the FreeSurfer ThalamicSegmentation tool [2]. Thalamic segmentation data are available in subject space for individual participants and MNI152_T1_1mm space for group-level mean probability maps. This dataset also contains registration files to convert data from subject space to MNI152_T1_1mm space using ANTS [3] and the scripts used for data processing and analysis.