cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/us05trze
title: GBSG

description: The GBSG dataset contains clinical data for breast cancer patients, including features and event times.
DOI: 10.57702/7mpmqfew
title: Mawps: A Math Word Problem Repository

description: Mawps: A math word problem repository. It contains 2,373 problems.
DOI: 10.57702/zh5na9z9
title: A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers

description: A diverse corpus for evaluating and developing English math word problem solvers. It contains 1,213 problems.
DOI: 10.57702/66eg4bqt
title: UCSD Pedestrian 1

description: The UCSD Pedestrian 1 dataset contains video clips of two different walkways.
DOI: 10.57702/2oc34wx4
title: UCSD Pedestrian 2

description: The UCSD Pedestrian 2 dataset contains video clips of two different walkways.
DOI: 10.57702/k9ssg041
title: Leeds Sports Pose dataset

description: The Leeds Sports Pose dataset contains 1000 images for training, which are combined with 4000 images from its extended version.
DOI: 10.57702/owgem9xk
title: TIMSS dataset

description: The dataset used in the paper is from the 2007 Trends in International Mathematics and Science Study (TIMSS). It contains middle school students' test scores in various science subfields and teacher-related variables.
DOI: 10.57702/4v8h8eyp
title: A Novel Term_Class Relevance Measure for Text Categorization

description: The proposed term_class relevance measure is a product of three aspects namely class_term weight, class_term density and class_weight.
DOI: 10.57702/ldgscoix
title: Microgrid Control with Barrier Certiﬁcate-based Simplex

description: The dataset used in this paper is a microgrid model with multiple types of energy sources, including a battery, photovoltaic (PV) DER, and diesel generator DER.
DOI: 10.57702/pa9iemhy
title: KiUT: Knowledge-injected U-Transformer for Radiology Report Generation

description: Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing.
DOI: 10.57702/trpxruay
title: Indiana University X-ray dataset

description: A collection of radiology examinations for distribution and retrieval.
DOI: 10.57702/ze9riysy
title: Drishti-GS and Left Atrial MRI dataset

description: The dataset used in the paper is the 2D optic cup and disk segmentation dataset: Drishti-GS (GS) and the left atrial (LA) 3D gadolinium-enhanced magnetic resonance imaging (MRI) dataset.
DOI: 10.57702/xacaccyf
title: UWB Radar Signal Dataset

description: The dataset used in the paper is a multistatic ultra-wideband (UWB) radar signal dataset for occupancy detection and vital sign estimation.
DOI: 10.57702/ii4g32rd
title: PDSNet: A Deep Learning Approach to Reconstructing Scattered Images Beyond the Memory Effect

description: A convolutional neural network called PDSNet for the reconstruction of scattered images.
DOI: 10.15468/dl.je4eqf
title: Occurrence Download

description: A dataset containing 101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ancistrus temminckii (Valenciennes, 1840))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is less than 1e+05"
 ]
}

 The dataset includes 101 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055403-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/u1n7s5xd
title: IDiffNet: Deep Learning for Image Reconstruction through Scattering Media

description: A deep learning approach to image reconstruction through scattering media.
DOI: 10.57702/nchhrupw
title: Deep Speckle Correlation: A Deep Learning Approach to Scalable Imaging through Scattering Media

description: A CNN network for reconstructing speckle images generated by untrained ground glass.
DOI: 10.57702/xvi6izbk
title: Image Reconstruction through Dynamic Scattering Media using Generative Adversarial Networks

description: A GAN network for reconstructing dynamic scattering images.
DOI: 10.57702/il1txvh3
title: Curriculum Design for Teaching via Demonstrations: Theory and Applications

description: The dataset used in this paper is a synthetic car driving environment, consisting of 8 different types of tasks, and a navigation-based environment, consisting of tasks inspired by the travelling salesman problem.
DOI: 10.57702/i24mlcee
title: 1 Year, 1000km: The Oxford RobotCar Dataset

description: 1 Year, 1000km: The Oxford RobotCar Dataset
DOI: 10.57702/xxn8912v
title: Vision meets robotics: The KITTI dataset

description: This paper presents a benchmark for visual odometry and SLAM.
DOI: 10.57702/nufubi9l
title: Distral: Robust Multitask Reinforcement Learning

description: The Distral dataset is a multitask reinforcement learning dataset, where agents learn to solve multiple related problems, either simultaneously or sequentially.
DOI: 10.57702/boh2jxyb
title: Amazon Cell Phones and Accessories 5-core

description: The dataset includes 27,879 users (sequences), 10,429 items, and 194,439 ratings with density as 0.06%.
DOI: 10.57702/cmvo03fv
title: EgoObjects dataset

description: EgoObjects dataset for object-centric scene understanding
DOI: 10.57702/sxz172rr
title: OAK dataset

description: OAK dataset for egocentric video understanding
DOI: 10.57702/udgdz5tr
title: Bangla Literature Dataset (6 authors)

description: The dataset used in this paper is a 6-author dataset for authorship attribution of Bangla literature.
DOI: 10.57702/8x5eot3x
title: Water solubility (log S) data for 1,290 compounds

description: Water solubility (log S) data for 1,290 compounds
DOI: 10.57702/gqrwg7il
title: Bivariate artificial data

description: Bivariate artificial data
DOI: 10.57702/qct80nve
title: Patent corpus

description: A dataset of over 100,000 patent documents from the Cooperative Patent Classification scheme (CPC) category A61.
DOI: 10.57702/jl1tbhmx
title: World Robot Challenge (WRC) 2020 Assembly Challenge

description: The dataset used in the paper is a set of assembly tasks and their corresponding planning and execution plans.
DOI: 10.57702/gu6w8l1k
title: NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry

description: Malnutrition among newborns is a top public health concern in developing countries. Identification and subsequent growth monitoring are key to successful interventions.
DOI: 10.57702/orxg0rk2
title: SE-GAN: Skeleton Enhanced Generative Adversarial Network for Brush Handwriting Font Generation

description: A large-scale brush handwriting Chinese font dataset with six styles and 15,000 high-resolution images.
DOI: 10.57702/i1nggvwu
title: Automated Solubility Analysis System and Method Using Computer Vision and Machine Learning

description: A novel active solubility sensing device using computer vision is proposed to improve separation purification performance and prevent malfunctions of separation equipment.
DOI: 10.6073/pasta/e2c4fde5c0568398c9d8d107bcf907ec
title: Water Quality Data (Extensive) from the Shark River Slough, Everglades National Park (FCE LTER), Florida, USA, October 2000 - ongoing

description: Water quality samples are being collected using ISCO autosamplers at all wetland sites (that is, all sites except TS/Ph-9, 10, and 11). The autosamplers contain 24 1L bottles. Water is sampled by programming the autosamplers to take composite samples once every 3 days. These samples are a composite of four 250mL subsamples drawn every 18 hours (a sampling scheme that captures a dawn, noon, dusk, and midnight sample in every three day composite). The samples are collected every 3-4 weeks and analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. When sites are visited to collect these samples, we also collect a grab sample that is immediately put on ice. A portion of these grab samples is filtered through a Whatman GF/F filter immediately upon return to the lab, and the filtered samples are analyzed for inorganic nutrients such as NO2-, NO3-, NH4+, SRP, and DOC. The unfiltered fraction of these grab samples is analyzed for TP and TN. We use these monthly grab samples to generate relationships between TP and SRP, and between TN and NO2- + NO3- + NH4+. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques. TP is analyzed with a modified Solorzano and Sharp (1980) technique. TN is measured with an Antec TN analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with a YSI conductivity meter. In January 2007 a 6 days interval began for SRS1d, SRS2 & SRS3; the ISCOs did not change their sampling scheduled, they kept collecting water sample every 18 hours and switching bottles every three days; once in the lab, the 3 days was turned into 6 days by combining two bottles into 2 liters container. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwater sites to trigger water sampling after rain events exceed a given threshold of duration and/or intensity. As currently programmed, when the threshold of = 2.5 cm of rain per hour is passed, the autosampler at that site collects a 1000mL sample 30 minutes after the threshold has been reached. Rain event samples are collected, treated, and analyzed as all other water quality samples.
DOI: 10.57702/lk1lkhzp
title: MaskCorrect: An Embarrassingly Simple Approach for Error Correction

description: Text error correction aims to correct the errors in text sequences such as those typed by humans or generated by speech recognition models.
DOI: 10.57702/is0zfm0n
title: SIGHAN

description: SIGHAN 2014 and SIGHAN 2015 are Chinese spell check bake-offs.
DOI: 10.57702/ulk40cr4
title: Arcade Learning Environment (ALE) and Gym MuJoCo benchmark

description: The dataset used in the paper is the Arcade Learning Environment (ALE) and the Gym MuJoCo benchmark.
DOI: 10.6073/pasta/a4fb94408a8798c99b5c1449c7559582
title: Water Quality Data (Extensive) from the Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, April 1996 - ongoing

description: Water quality samples are being collected using ISCO autosamplers at all wetland sites (that is, all sites except TS/Ph-9, 10, and 11). The autosamplers contain 24 1L bottles. Water is sampled by programming the autosamplers to take composite samples once every 3 days. These samples are a composite of four 250mL subsamples drawn every 18 hours (a sampling scheme that captures a dawn, noon, dusk, and midnight sample in every three day composite). The samples are collected every 3-4 weeks and analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. When sites are visited to collect these samples, we also collect a grab sample that is immediately put on ice. A portion of these grab samples is filtered through a Whatman GF/F filter immediately upon return to the lab, and the filtered samples are analyzed for inorganic nutrients such as NO2-, NO3-, NH4+, SRP, and DOC. The unfiltered fraction of these grab samples is analyzed for TP, TN, and TOC. We use these monthly grab samples to generate relationships between TP and SRP, and between TN and NO2- + NO3- + NH4+. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques. TP is analyzed with a modified Solorzano and Sharp (1980) technique. TN is measured with an Antec TN analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with an YSI conductivity meter. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwater sites to trigger water sampling after rain events exceed a given threshold of duration and/or intensity. As currently programmed, when the threshold of = 2.5 cm of rain per hour is passed, the autosampler at that site collects a 500mL sample 30 minutes after the threshold has been reached. Rain event samples are collected, treated, and analyzed as all other water quality samples.
DOI: 10.57702/k5kkbbg3
title: MERCURY: Accelerating DNN Training By Exploiting Input Similarity

description: MERCURY accelerates DNN training by exploiting input similarity.
DOI: 10.15468/e4rews
title: Insects of the Hetman National Nature Park registered in 2024

description: The dataset contains data on insects registered on the territory of the Hetman National Nature Park (“Hetmanskyi” National Nature Park) during two expeditions in 2024.
The dataset contains data on finds of about 280 species.
DOI: 10.57702/q2vj83rn
title: NIST Chinese-English

description: The dataset used for the experiments of simultaneous neural machine translation.
DOI: 10.6073/pasta/1d53e1d8535de6789e9ba53b14926297
title: Water Quality Data (Extensive) from the Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, July 1999 - ongoing

description: Water quality samples are being collected using ISCO autosamplers at all wetland sites (that is, all sites except TS/Ph-9, 10, and 11). The autosamplers contain 24 1L bottles. Water is sampled by programming the autosamplers to take composite samples once every 3 days. These samples are a composite of four 250mL subsamples drawn every 18 hours (a sampling scheme that captures a dawn, noon, dusk, and midnight sample in every three day composite). The samples are collected every 3-4 weeks and analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. When sites are visited to collect these samples, we also collect a grab sample that is immediately put on ice. A portion of these grab samples is filtered through a Whatman GF/F filter immediately upon return to the lab, and the filtered samples are analyzed for inorganic nutrients such as NO2-, NO3-, NH4+, SRP, and DOC. The unfiltered fraction of these grab samples is analyzed for TP, TN, and TOC. We use these monthly grab samples to generate relationships between TP and SRP, and between TN and NO2- + NO3- + NH4+. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques. TP is analyzed with a modified Solorzano and Sharp (1980) technique. TN is measured with an Antec TN analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with an YSI conductivity meter. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwater sites to trigger water sampling after rain events exceed a given threshold of duration and/or intensity. As currently programmed, when the threshold of = 2.5 cm of rain per hour is passed, the autosampler at that site collects a 500mL sample 30 minutes after the threshold has been reached. Rain event samples are collected, treated, and analyzed as all other water quality samples.
DOI: 10.57702/6hc8rpk0
title: White Matter Hyperintensities (WMH) dataset

description: White matter hyperintensities (WMH) dataset for medical image segmentation
DOI: 10.57702/w9rff3f7
title: Sales Forecast in E-commerce using Convolutional Neural Network

description: Sales forecast is an essential task in E-commerce and has a crucial impact on making informed business decisions. It can help us to manage the workforce, cash flow and resources such as optimizing the supply chain of manufacturers etc. Sales forecast is a challenging problem in that sales is affected by many factors including promotion activities, price changes, and user preferences etc. Traditional sales forecast techniques mainly rely on historical sales data to predict future sales and their accuracies are limited. Some more recent learning-based methods capture more information in the model to improve the forecast accuracy. However, these methods require case-by-case manual feature engineering for specific commercial scenarios, which is usually a difficult, time-consuming task and requires expert knowledge. To overcome the limitations of existing methods, we propose a novel approach in this paper to learn effective features automatically from the structured data using the Convolutional Neural Network (CNN). When fed with raw log data, our approach can automatically extract effective features from that and then forecast sales using those extracted features. We test our method on a large real-world dataset from CaiNiao.com and the experimental results validate the effectiveness of our method.
DOI: 10.57702/l7y8rqiq
title: RESACT: REINFORCING LONG-TERM ENGAGEMENT

description: Long-term engagement is preferred over immediate engagement in sequential recommendation as it directly affects product operational metrics such as daily active users (DAUs) and dwell time.
DOI: 10.57702/2p3ebg33
title: Fair Overlap Number of Balls (Fair-ONB)

description: The Fair-ONB method is a novel undersampling proposal based on data morphology.
DOI: 10.57702/kxksbhsc
title: Luxembourg SUMO Trafﬁc (LuST) Scenario

description: Luxembourg SUMO Trafﬁc (LuST) Scenario: Trafﬁc Demand Evaluation
DOI: 10.57702/str85edj
title: Monaco SUMO trafﬁc scenario

description: Towards multimodal mobility simulation of C-ITS: The Monaco SUMO trafﬁc scenario
DOI: 10.5281/zenodo.14532867
title: Progress Towards Satellite Requirements to Capture Water Propagation in Earth's Rivers

description: Corresponding article

This repository corresponds to the datasets that were used in the study reported in:



Cerbelaud, A., David, C. H., Pavelsky, T., Biancamaria, S., et al. (2025). Progress Towards Satellite Requirements to Capture Water Propagation in Earth’s Rivers. In Review.


When making use of any of the files of this dataset, please cite both the aforementioned article and the dataset herein. 

Main goals of the publication

The corresponding dataset aims to provide the "estimated revisit period (in days) at 145,000 reaches globally (Q_mean > 150 m3.s-1) required to capture peak flow event (PFE) durations in rivers within ~90% accuracy using the rule derived by Cerbelaud et al. (2024), by upstream area and river width. PFE durations values are from Sikder et al. (2021). Upstream area is from the MERIT-Hydro Basins river network. River widths are from the SWORD database."
DOI: 10.5281/zenodo.14532868
title: Progress Towards Satellite Requirements to Capture Water Propagation in Earth's Rivers

description: Corresponding article

This repository corresponds to the datasets that were used in the study reported in:



Cerbelaud, A., David, C. H., Pavelsky, T., Biancamaria, S., et al. (2025). Progress Towards Satellite Requirements to Capture Water Propagation in Earth’s Rivers. In Review.


When making use of any of the files of this dataset, please cite both the aforementioned article and the dataset herein. 

Main goals of the publication

The corresponding dataset aims to provide the "estimated revisit period (in days) at 145,000 reaches globally (Q_mean > 150 m3.s-1) required to capture peak flow event (PFE) durations in rivers within ~90% accuracy using the rule derived by Cerbelaud et al. (2024), by upstream area and river width. PFE durations values are from Sikder et al. (2021). Upstream area is from the MERIT-Hydro Basins river network. River widths are from the SWORD database."
DOI: 10.57702/kpy6hecb
title: Turin Vehicular trafﬁc simulation

description: Vehicular trafﬁc simulation in the city of turin from raw data
DOI: 10.57702/a92eqnu7
title: BCI: Breast Cancer Immunohistochemical Image Generation

description: The Breast Cancer Immunohistochemical Image Generation dataset, a paired H&E to IHC-stained image translation dataset.
DOI: 10.57702/4aw7gk27
title: PYTHIA 8.2

description: An introduction to PYTHIA 8.2
DOI: 10.6073/pasta/f45fbf88dcf1f78f0d74c1dbdaaa8c7d
title: Surface Water Quality Monitoring Data collected in South Florida Coastal Waters (FCE LTER), Florida, USA, June 1989-ongoing

description: The Southeast Environmental Research Center at Florida International University operates a network of 331 fixed sampling sites distributed throughout the estuarine and coastal ecosystems of south Florida. The purpose of this network is to address concerns in regional water quality which cross and overlap separate political boundaries. Funding has come from different sources with individual programs being added as funding became available. Biscayne Bay, Florida Bay, Whitewater Bay, Ten Thousand Islands, Rookery Bay, Estero Bay, and Pine Island Sound are sampled monthly while the Florida Keys National Marine Sanctuary (FKNMS) and the southwest shelf are sampled quarterly. Variables currently being measured include surface and bottom temperature, salinity, dissolved oxygen, nitrate, nitrite, ammonium, total nitrogen, total organic nitrogen, total phosphorus, soluble reactive phosphorus, total organic carbon, total silicate, chlorophyll a, alkaline phosphatase activity, turbidity, and light extinction. The purpose of this network is to address concerns in regional water quality which cross and overlap separate political boundaries. One of the products is a quasi-synoptic big picture of nutrient and phytoplankton biomass distributions over the South Florida Coastal Waters. The SERC network will, in time, provide us with the data necessary to determine whether conditions within the estuaries and sanctuary are improving or declining.
DOI: 10.57702/ejbwkj95
title: Event Ticket Price Prediction with Deep Neural Network, on Spatial-Temporal Sparse Data

description: Event ticket price prediction is important to marketing strategy for any sports team or musical ensemble. An accurate prediction model can help the marketing team to make promotion plan more effectively and efficiently.
DOI: 10.57702/8l22hjal
title: Disentangling Active and Passive Cosponsorship in the U.S. Congress

description: The dataset contains information on all bills and legislators from the 112th to 115th U.S. Congress.
DOI: 10.57702/4c82ufeq
title: Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization

description: The dataset used in this paper is a production-scale on-device natural language understanding model.
DOI: 10.57702/79f9dgi3
title: UT Dallas Stress (UTD) Data and Alan Walks Wales (AWW) Data

description: Two publicly available data sets with EDA and 3-axis accelerometer data, both collected using an Affectiva Q sensor worn on the wrist, totaling about 23 hours of data.
DOI: 10.57702/bzvvg19s
title: CODIA

description: CODIA dataset is a collection of code submissions from students.
DOI: 10.57702/plt8ndsm
title: SRMP

description: The dataset used in the paper is a set of real-world problems with promising results.
DOI: 10.57702/bmzf457n
title: Protein-related datasets

description: The dataset used in the paper is a set of protein-related datasets.
DOI: 10.6073/pasta/e74f71a429fc6d9f8db15fb5debcabc5
title: Dissolved CO2 and CH4 dynamics in Delmarva headwater wetlands, 2020-2022

description: This data product includes measurements of dissolved CO2 and CH4 in surface water and groundwater, along with hydrological and biogeochemical variables, across 20 headwater wetlands in the Delmarva Peninsula in the Mid-Atlantic region of the United States. Greenhouse gas samples and water chemistry samples were collected every 1-3 months over a period of 2 years. We also monitored water level at each of the wetlands with a pressure transducer.
DOI: 10.57702/60btw326
title: Resettable Stochastic Clocks

description: The dataset used in this paper is a family of resettable stochastic clocks, where the agent is tasked with behaving as a clock with stochastic tick events, that may be reset by an external stimulus.
DOI: 10.57702/5aq0ukgh
title: Clothing and Makeup

description: Two Chinese sales customer service dialogue datasets, namely Clothing and Makeup, which are collected from Taobao, one of the largest decentralized E-commerce platforms in the world.
DOI: 10.57702/q6tqwgtn
title: Breast Cancer Wisconsin (Diagnostic) and Landsat Satellite Data Sets

description: The dataset used in this paper is the Breast Cancer Wisconsin (Diagnostic) and the Landsat Satellite Data Sets from the UCI repository.
DOI: 10.57702/oplhmqld
title: CIFAR-10-LT and CIFAR-100-LT

description: We create the long-tailed CIFAR versions following [7,11] with controllable degrees of data imbalance, and keep the test set unchanged.