Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
120
datasets available to search
ShareScore release 0.9.0
Dataset results
120 results for “real-world data”
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery
<p><strong>Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery</strong></p> <p>This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the <em>claims.csv</em> file, the <em>AirportCode</em> implies <em>AirportName</em>, as each code should be unique for a given airport.</p> <p>The file <em>ground_truth.csv</em> is a comma separated file containing approximate functional dependencies. <em>table</em> describes the relation we refer to, <em>lhs</em> and <em>rhs</em> reference two columns of those relations where semantically we found that <em>lhs</em> implies <em>rhs</em>.</p> <p>The file <em>excluded_candidates.csv</em> and <em>included_candidates.csv</em> list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the <em>g3_prime</em> value was too small.</p> <p><strong>Dataset References</strong></p> <ul> <li><em>adult.csv</em>: Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.</li> <li><em>claims.csv</em>: TSA Claims Data 2002 to 2006, <a href="https://www.dhs.gov/tsa-claims-data">published by the U.S. Department of Homeland Security</a>.</li> <li><em>dblp10k.csv</em>: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. <a href="https://hpi.de/naumann/projects/repeatability/datasets/dblp-dataset.html">Made available as DBLP Dataset 2</a>.</li> <li><em>hospital.csv</em>: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>t_biocase_...</em> files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>tax.csv</em>: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> </ul>
Real-world grasp data of a dual-arm Yumi robot with a parallel gripper and suction cup end-effectors
<p>The attached txt file contains indexes to a cleaner subset of the data issued in the first version.</p> <p>Note: <br>Version 1 contains samples with failure cases due to environment constraints, which work well for the platform used in GraspAgent 1.0 (https://doi.org/10.1109/LRA.2024.3502066). However, this can degrade the performance if used on another platform with different constraints. To solve this, version 2 reports a subset of the raw data, excluding the failure modes due to environmental causes. </p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution; Supplementary Information"
<p>These files contains datasets from which the Figures appearing in the Supplementary Information have been calculated. </p> <p>Description of datasets:</p> <p>Datasets related to SupplFig1 contain two columns: Frequency in Hz and phase noise in rad^2/Hz</p> <p>Data_SupplFig1_stabilised_fringes: psd of the phase noise calculated from the interference fringes in a stabilised condition</p> <p>Data_SupplFig1_unstabilised_fringes: psd of the phase noise calculated from the interference fringes in an unstabilised condition</p> <p>Data_SupplFig1_roundtrip_sensing_laser: psd of the sensing laser signal after a round-trip in the interferometer, calculated from self-heterodyne beatnote</p> <p>Data_SupplFig1_differential_roundtrip_sensing_vs_reference_laser: psd of the difference between the round-trip self-heterodyne beatnotes at the sensing and reference laser wavelengths</p> <p>Datasets related to SupplFig2 contain two columns: time in seconds and normalised intensity (calculated as detailed in the main publication).</p> <p>Data_SupplFig2_High_power_PD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when no artificial phase drift was applied</p> <p>Data_SupplFig2_High_power_PD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded with a photodiode when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_High_power_SPD_free_evol: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when no artificial phase drift was applied </p> <p>Data_SupplFig2_High_power_SPD_phase_drift: normalised intensity of the interference signal obtained with classical power level at the source. This trace was recorded on an SPD (after suitable attenuation) when an artificial phase drift was applied (8pi/s)</p> <p>Data_SupplFig2_Attenuated_SPD_free_evol: normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when no artificial phase drift was applied </p> <p>Data_SupplFig2_Attenuated_SPD_phase_drift: : normalised intensity of the interference signal obtained with attenuated beams at the source. This trace was recorded on an SPD when an artificial phase drift was applied (8pi/s)</p> <p> </p>
Processed Synthetic Real-World Data for tristate modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset.</p> <p>The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is a tristate quantized version of the health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Processed Synthetic Real-World Data for binary modelling
<p>This model learning dataset is created out of the <a href="https://zenodo.org/record/7409763">Raw Synthetic RWD</a> raw dataset, including some of the original attributes. It is distributed in JOBLIB files, where .joblib files contain the vectors and _ids.joblib contain the ID of the person from which each vector is extracted.</p> <p>This is useful in case it is needed to map the vectors to metadata about the people that are found in the original raw dataset. Note that corresponds to , or , depending on the dataset. The split is roughly 60% of the people are in the training dataset, and 20% in each of the validation and the testing datasets. The input attributes are the age, the short-term averages and the trends of the current week’s BMI, steps walked, calories burned, sleep quality, mood and water consumption, as well as the previous week’s short-term average and trend of the answer to the health self-assessment question.</p> <p>The outcome to be predicted is the binary quantized health self-assessment answer to be given in the current week. The dataset is normalized based on the training set. The means and standard deviations used can be found in the train_statistics.joblib file. Finally, the output_descriptions.joblib file contains descriptions of the outcomes to be predicted (not actually needed, since included here).</p>
Raw Synthetic Real-World Data
<p>Real World Data from a simulator for 1,000 people belonging to any of four behavioural groups: athletic, normal, unfit and feeble, simulated over 2 years and 3 months.</p> <p>The dataset is organised into 4 CSV files:</p> <ul> <li>aggregations.csv contains the daily physiological data, including their short- & long-term averages and their trends. Every row corresponds to a day for a person.</li> <li>answers.csv contains the answers to questions. Each row corresponds to a question answered.</li> <li>activity_summaries.csv contains summaries of the detected activities. Each row corresponds to an activity someone performed.</li> <li>participants_features.csv contains the demographic info of each simulated person. This is static info, every row corresponding to one person. An ML engineer can combine information from any of these files to setup model learning datasets. This is done in the “Processed Synthetic RWD for binary modelling” and the “Processed Synthetic RWD for tristate modelling” datasets that are also shared here.</li> </ul>
OCDetect - A Real-World Dataset to Detect Handwashing in Daily-Life using Wrist Motion Data from Wearables
<p>Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with ~3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.</p>
A Study of Real-world Data Races in Golang (Artifact)
<p>The concurrent programming literature is rich with tools and techniques for data race detection. Less, however, has been known about real-world, industry-scale deployment, experience, and insights about data races. Golang (Go for short) is a modern programming language that makes concurrency a first-class citizen. Go offers both message passing and shared memory for communicating among concurrent threads. Go is gaining popularity in modern microservice-based systems. Data races in Go stand in the face of its emerging popularity.</p> <p>In this paper, using our industrial codebase as an example, we demonstrate that Go developers embrace concurrency and show how the abundance of concurrency alongside language idioms and nuances make Go programs highly susceptible to data races. <br> Google's Go distribution ships with a built-in dynamic data race detector based on ThreadSanitizer. Dynamic race detectors pose scalability and flakiness challenges; we discuss various software engineering trade-offs to scale this detector to work effectively at scale.</p> <p>We have deployed this detector in our 50-million lines of Go codebase hosting 2100 distinct microservices, found over 2000 data races, fixed over 1000 data races, spanning 790 distinct code patches submitted by 210 unique developers over a six-month period. Based on a detailed investigation of these data race patterns in Go, we make seven high-level observations relating to the complex interplay between the Go language paradigm and data races. </p>
Real-world Optimization Benchmark from Vehicle Dynamics - Data and Code
<p>Data and Code of five 2D single-objective optimization problems from vehicle dynamics design for benchmarking</p> <p>Conference Paper at ECTA Real-world Optimization Benchmark from Vehicle Dynamics: Specification of Problems in 2D and Methodology for Transferring (Meta-)Optimized Algorithm Parameters</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
Associated data underlying the publication "Flipped Classroom Real-World Activities for Learning Open Computing Concepts"
<p>Various “open” concepts in Computing, such as open standards, open data, open licenses or system interoperability are becoming more important in the professional lives of software engineers. However, students usually do not receive a systematic education about these concepts ; rather they sporadically learn about a subset of these topics. This paper presents the revised version of the Open Computing course in the University of Zagreb, Faculty of Electrical Engineering and Computing (FER), which teaches a clear set of current topics focused on open data and correlating concepts. The new e-learning course is carried out using the “flipped classroom” educational method ; students construct their knowledge in a set of real-world mini-activities throughout the course, instead of passively learning from the official course resources. In this paper, we discuss our flipped classroom activities, their relation to revised Bloom’s taxonomy of educational objectives, and present the evaluation of the first course instance us- ing this model. Students’ feedback shows they welcome this change in approach, finding the course useful and interesting, and preferring this method to the traditional full lecture setting.</p>
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
<p>Socially-assistive robots (SARs) hold significant potential to transform the management of chronic healthcare conditions (e.g. diabetes, Alzheimer's, dementia) outside the clinic walls. However doing so entails embedding such autonomous robots into people's daily lives and home living environments, which are deeply shaped by the cultural and geographic locations within which they are situated. That begs the question of whether we can design autonomous interactive behaviors between SARs and humans based on universal machine learning (ML) and deep learning (DL) models of robotic sensor data that would work across such diverse environments. To investigate this, we conducted a long-term user study with 26 participants across two diverse locations (the United States and South Korea) with SARs deployed in each user's home for several weeks. We collected robotic sensor data every second of every day, combined with sophisticated ecological momentary assessment (EMA) sampling techniques, to generate a large-scale dataset of over 270 million data points representing 173 hours of randomly-sampled naturalistic interaction data between the human and robot pet. Interaction behaviors included activities like playing, petting, talking, cooking, etc.</p>
Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
Open the record for dataset details and reuse information.
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"
<p>These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity.</p> <p>Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used:</p> <p>Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s</p> <p>Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s</p> <p>Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term.</p> <p>Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.</p>
Fully synthetic longitudinal real-world data from hearing aid wearers for public health policy modeling
<p>Real-world data from hearing aids and Bluetooth connected smartphones. The associated data report can be found here: <a href="https://doi.org/10.3389/fnins.2019.00850">https://doi.org/10.3389/fnins.2019.00850</a></p>
Looking in the medicine cabinet: methods for using real-world data to assess the impact of measles, mumps and rubella (MMR) and recombinant adjuvanted varicella-zoster vaccines on coronavirus disease 2019 (COVID-19) prevention and case fatality
<p>Supplementary File S1. 20210712_vx_off_target_pubdraft_S1 (Tables, Graphs and scripts associated with publication)</p> <p>Data file 1. Basic_Analysis.R (descriptive analysis script in R, for use with cleaned data files 3, 4 and 6)<br> Data file 2. Cleaning (Script demonstrating how Cerner data was cleaned upon download)<br> Data file 3. COVID_all_cleaned (CSV file with all COVID+ subjects in Cerner institutions)<br> Data file 4. COVID_mmr_data_cleaned (CSV file with COVID+ patients between 25-64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history and mortality outcomes)<br> Data file 5. COVID_mmr_data_matched (CSV file matching MMR vaccine-exposed cases to controls based on propensity scores)<br> Data file 6. COVID_zoster_data_cleaned (CSV file with COVID+ patients above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history and mortality outcomes)<br> Data file 7. COVID_zoster_data_matched (CSV file matching zoster vaccine-exposed cases to controls based on propensity scores)<br> Data file 8. General_25_64_data_cleaned (CSV file, all patients in Cerner institutions between 25 – 64 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, MMR vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes ).<br> Data file 9. General_over50_data_cleaned (CSV file, all patients in Cerner institutions above 50 years old, including institution id, age category, gender, whether patient is in emergency department or inpatient, flu vaccine history, zoster vaccine history, SARS-CoV-2 infection and COVID-19 mortality outcomes).<br> Data file 10. Included_tenants (CSV file, institution IDs whose contributed cases comprise at least 0.5% of the aggregate sample size).<br> Data file 11. MMR_ps (R script to run for MMR-related files analysis)<br> Data file 12. Zoster_ps (R script to run for zoster-related files analysis)</p>
Non-interventional Study for Real-world Data of Afatinib Treatment in First-line Setting and of Subsequent Therapies for Patients With Advanced Epidermal Growth Factor Receptor (EGFR) Mutation-positiv
ClinicalTrials.gov study NCT04795245. IPD Sharing: NO. Countries: 1. Publications: 1.
Real-World Data Collection of the GORE® VIABAHN® VBX Balloon Expandable Endoprosthesis When Used as a Bridging Stent With Branched and Fenestrated Endografts in the Treatment of Aortic Aneurysms Invol
ClinicalTrials.gov study NCT05143138. IPD Sharing: Not stated. Countries: 6. Publications: 0.
Real-world Data Analysis of REN Treatment in Adolescence With Migraine
ClinicalTrials.gov study NCT05443659. IPD Sharing: NO. Countries: 2. Publications: 7.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.