This is a peer-reviewed supplementary table for the article 'Healthcare resource utilization, costs and treatment associated with myasthenia gravis exacerbations among patients with myasthenia gravis in the USA: a retrospective analysis of claims data' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: MG treatment definitionsAim: There are limited data on the clinical and economic burden of exacerbations in patients with myasthenia gravis (MG). We assessed patient clinical characteristics, treatments and healthcare resource utilization (HCRU) associated with MG exacerbation. Patients & methods: This was a retrospective analysis of adult patients with MG identified by commercial, Medicare or Medicaid insurance claims from the IBM MarketScan database. Eligible patients had two or more MG diagnosis codes, without evidence of exacerbation or crisis in the baseline period (12 months prior to index [first eligible MG diagnosis]). Clinical characteristics were evaluated at baseline and 12 weeks before each exacerbation. Number of exacerbations, MG treatments and HCRU costs associated with exacerbation were described during a 2-year follow-up period. Results: Among 9352 prevalent MG patients, 34.4% (n = 3218) experienced ≥1 exacerbation after index: commercial, 53.0% (n = 1706); Medicare, 39.4% (n = 1269); and Medicaid, 7.6% (n = 243). During follow-up, the mean (standard deviation) number of exacerbations per commercial and Medicare patient was 3.7 (7.0) and 2.7 (4.1), respectively. At least two exacerbations were experienced by approximately half of commercial and Medicare patients with ≥1 exacerbation. Mean total MGrelated healthcare costs per exacerbation ranged from $26,078 to $51,120, and from $19,903 to $49,967 for commercial and Medicare patients, respectively. AChEI use decreased in patients with multiple exacerbations, while intravenous immunoglobulin use increased with multiple exacerbations. Conclusion: Despite utilization of current treatments for MG,MG exacerbations are associated with a high clinical and economic burden in both commercial and Medicare patients. Additional treatment options and improved disease management may help to reduce exacerbations and disease burden.

About OPCRD

Optimum Patient Care Research Database (OPCRD) is a real-world, longitudinal, research database that provides anonymised data to support scientific, medical, public health and exploratory research. OPCRD is established, funded and maintained by Optimum Patient Care Limited (OPC) – which is a not-for-profit social enterprise that has been providing quality improvement programmes and research support services to general practices across the UK since 2005.

Key Features of OPCRD

OPCRD has been purposefully designed to facilitate real-world data collection and address the growing demand for observational and pragmatic medical research, both in the UK and internationally. Data held in OPCRD is representative of routine clinical care and thus enables the study of ‘real-world’ effectiveness and health care utilisation patterns for chronic health conditions.

OPCRD unique qualities which set it apart from other research data resources: • De-identified electronic medical records of more than 24.4 million patients • OPCRD covers all major UK primary care clinical systems • OPCRD covers approximately 35% of the UK population • One of the biggest primary care research networks in the world, with over 1,175 practices • Linked patient reported outcomes for over 68,000 patients including Covid-19 patient reported data • Linkage to secondary care data sources including Hospital Episode Statistics (HES)

Data Available in OPCRD

OPCRD has received data contributions from over 1,175 practices and currently holds de-identified research ready data for over 24.4 million patients or data subjects. This includes longitudinal primary care patient data and any data relevant to the management of patients in primary care, and thus covers all conditions. The data is derived from both electronic health records (EHR) data and patient reported data from patient questionnaires delivered as part of quality improvement. OPCRD currently holds over 68,000 patient reported questionnaire data on Covid-19, asthma, COPD and rare diseases.

Approvals and Governance

OPCRD has NHS research ethics committee (REC) approval to provide anonymised data for scientific and medical research since 2010, with its most recent approval in 2020 (NHS HRA REC ref: 20/EM/0148). OPCRD is governed by the Anonymised Data Ethics and Protocols Transparency committee (ADEPT). All research conducted using anonymised data from OPCRD must gain prior approval from ADEPT. Proceeds from OPCRD data access fees and detailed feasibility assessments are re-invested into OPC services for the continued free provision of patient quality improvement programmes for contributing practices and patients.

For more information on OPCRD please visit: https://opcrd.co.uk/

Supplemental Material 01: Search query details

WP3 of CORE-MD investigated how to aggregate and extract maximal value for post-market surveillance from medical device registries, big data, clinical practices and experience, and the internet. This data collection was created by the Task 3.2 of the CORE-MD project, as the result of the proposed methodological framework to transform unstructured and dispersed publicly available safety information (Field Safety Notices, recalls, alerts) into a standardized and harmonized database. The databases includes 137,720 historical safety notices (updated to February 2024) safety notices published by different competent national authorities (16 EU Member States and 5 extra EU jurisdictions).

Additional file 1. pubmed_literature_observational_Japan_3_years_human_2020. The list of articles identified through PubMed search includes article title, authors, journal, data source, therapeutic area, patient number, and study design.

This database compiles information from various publically available battery cell datasheets to provide a centralized and accessible repository for technical details of various real-world battery cells, including specifications, performance metrics, and technical characteristics. Our project aims to streamline research efforts, support informed decision-making, and foster advancements in battery technology by collecting these datasheets. We do not assume any liability for the completeness, correctness, and accuracy of the information.

However, it is important to acknowledge the potential challenges of managing such a database given the still early, highly dynamic, and innovative battery market. Among others, ensuring data accuracy, data completeness, and timeliness is critical. Battery cell technologies are constantly evolving, requiring ongoing attention to maintain an up-to-date database with the latest specifications and cells. While we aimed to ensure that all records are complete, incomplete datasheets are limiting this effort and, thus, the full potential of the database. Last, standardization issues may present a challenge due to the absence of standardized reporting formats across manufacturers and countries. See "Notes" columns for comments. Unless otherwise stated, all values and parameters originate exclusively from the datasheets.

Last, we highlight that it is important to consider potential uncertainties when using the information provided in cell datasheets. The values shown are primarily derived from standardized test environments and conditions and may not accurately reflect the actual real-world performance of the cells, which may vary significantly depending on ambient conditions (foremost temperature) and charge-discharge load profiles specific to applications and embedded use cases.

We validated the predictive accuracy of HSUVs estimated by including clinical data from Japanese patients with MBC into the Lloyd model. The study consisted of two phases. In the first phase, we constructed a database of clinical data and HSUVs for Japanese patients with MBC in a real-world setting to evaluate the predictive accuracy of HSUVs calculated using the Lloyd model. In the second phase, we assessed how accurately predicted HSUVs (based on the Lloyd model) correlated with actual HSUVs obtained using preference-based health status measures in Japanese patients with MBC.

Health-state utility values and patient-reported outcomes (PROs)

The first phase of our study involved developing a comprehensive database of HSUVs and PROs for Japanese patients with MBC, which is linked to patients’ social background and treatment history, and PRO surveys of adverse events from anti-cancer agents using a questionnaire. The study sample included patients who attended the Outpatient Breast Clin...

The NNRD is a national resource holding real-world clinical data captured in the course of care on all admissions to NHS neonatal units in England, Wales, Scotland and the Isle of Man. Neonatal units submit data through their Electronic Patient Record system supplier. At present, there is information on around one million babies and 10 million days of care in the NNRD.

The NNRD is available to support audit, evaluations, bench-marking, quality improvement and clinical, epidemiological, health services and policy research to improve patient care and outcomes. Data in the NNRD comprise the Neonatal Data Set (ISB1595), an approved NHS Information Standard and include demographic details, daily records of interventions and treatments throughout the neonatal inpatient stay, information on diagnoses and outcomes, and follow-up health status at age two years.

The Neonatal Data Analysis Unit was founded to support the management and development of the National Neonatal Research Database (NNRD) established in 2007 by Professor Modi, and related research.

More information can be found at https://www.imperial.ac.uk/neonatal-data-analysis-unit

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{ _id:

Technical notes and documentation

The Compendium of Healthcare Costs in Spain (CONCEPT-COSTS Database) is a database of Spanish healthcare unit costs estimated from different national sources.

Version 3.0 of the CONCEPT-COSTS Database contains costs estimates (expresed in EUR 2024) for a core set of service items commonly used in the chronic health problems evaluated in CONCEPT Project.

It is a living document planned to be regularly updated and expanded in terms of the covered service over time.

Aims of CONCEPT-COSTS project:

CONCEPT-COSTS is part of the coordinated CONCEPT Project, which comprises four subprojects whose objective is to analyse the effectiveness and efficiency of care pathways (CP) in three chronic health problems of high prevalence and socioeconomic impact, which are diabetes mellitus type 2, breast cancer and ischemic stroke. As a common denominator, CONCEPT shares the innovative perspective of focusing its analysis on CP as a key determinant of healthcare adequacy, adherence to treatment, health outcomes and economic consequences. CONCEPT-COSTS' first objective is to complement the results produced by each CONCEPT clinical cohort, with a broad proposal of economic analyses based on real-world data (RWD), including incurred costs, avoidable costs and efficiency evaluation of identified CP. These results will be used to inform the clinical and management decisions about those CP to be promoted or avoided. As a second objective, CONCEPT-COSTS will identify the ethodological and logistical challenges faced by economic evaluations based on RWD, to develop a framework that will include recommendations for improvements related to feasibility, validity and transferability of results.

Files included in this publication:

CONCEPT_COSTS_Database_v3.csv

CONCEPT_COSTS_Database_v3.html

Readme_v3.doc

What's new

Costs updated to 2024

Some sources updated

DEMAND: Diverse Environments Multichannel Acoustic Noise Database

A database of 16-channel environmental noise recordings

Introduction

Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.

Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the AURORA-2 corpus, the CHiME background noise data, or the NOISEX-92 database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.

The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.

License

This work, the audio data and the document describing it, is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

The data

A description of the data and the recording equipment is provided in the file DEMAND.pdf. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.

Other information

The MATLAB scripts listed in the documentation can be found in the file scripts.zip.

The Authors

This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program VERSAMUS.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Ireland IE: GDP: Real: External Balance of Goods and Services data was reported at 87,210.660 EUR mn in 2017. This records an increase from the previous number of 49,945.780 EUR mn for 2016. Ireland IE: GDP: Real: External Balance of Goods and Services data is updated yearly, averaging -758.694 EUR mn from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 87,210.660 EUR mn in 2017 and a record low of -5,853.966 EUR mn in 1979. Ireland IE: GDP: Real: External Balance of Goods and Services data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ireland – Table IE.World Bank: Gross Domestic Product: Real. External balance on goods and services (formerly resource balance) equals exports of goods and services minus imports of goods and services (previously nonfactor services). Data are in constant local currency.; ; World Bank national accounts data, and OECD National Accounts data files.; ;

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Database of Communities of …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Database of Communities of National Environmental Significance stores maps, taxonomic, ecological, and management information about Communities of National Environmental Significance listed in the Environment Protection and Biodiversity Conservation (EPBC) Act 1999 as threatened ecological communities. Credit: State and Commonwealth Herbaria, Museums and Conservation Agencies Centre for Plant Biodiversity Research Australian Government Department of the Environment, Environmental Resources Information Network External accuracy: The positional accuracy of spatial data is a statistical estimate of the degree to which planimetric coordinates and elevations of features agree with their real world values. The planimetric accuracy attainable in the vector data will be composed of errors from three sources: The positional accuracy of the source material Errors due to the conversion processes. Errors due to the manipulation processes. This specification cannot prescribe a figure for the planimetric accuracy of the existing source material used for capture of community distributions as it has already been produced. The errors due to the digitising process depend on the accuracy of the digitising table set-up or the scanner resolution, systematic errors in the equipment, errors due to software and errors specific to the operator. An accepted standard for digitising is that the line accuracy should be within half a line width. Non Quantitative accuracy: Tests are undertaken to ensure that there are no errors in attributes: The spatial resolution of the data is reflected in the Presence Categories Presence categories are one of: * Community known to occur within area * Community likely to occur within area * Community may occur within area (general indication only) Conceptual consistency: Tests undertaken for logical consistency: Names of export files and data quality table are correct Table names are valid Item names in coverages are valid Item names are present in coverage attribute files Label points and entity point features have only one coordinate pair The Arc/Info coverages can be generated, have attributes attached and be 'built' In polygon coverages there are no label errors i.e. every polygon has one and only one polygon label point Data format, projection and data type are correct There are no overshoots, i.e. arc overhangs at intersections (1% error acceptable) There are no undershoots, i.e. arcs failing to meet at intersections (0.5% error acceptable) There are no new polygons smaller than the minimum specified area (5% error acceptable) There are no new linear features shorter than the minimum length (5% error acceptable) There are no artefacts such as spikes or deviations visible at 1:125 000 (5% error acceptable) Separate covers have exactly coincident lines where intended (5% error acceptable) Completeness omission: The database is continually being updated as the lists of threatened ecological communities on schedules of the EPBC Act are amended. The Species of National Environmental Significance database is available at https://www.environment.gov.au/science/erin/databases-maps/snes Dataset History This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Spatial information is stored in a geographic information system and links to the Species Profile tables through the community identifier. Source data were provided from a range of government, industry and non-government organisations. Testing is carried out using a combination of expert opinion and on-screen checks. Dataset Citation Department of the Environment (2015) Communities of National Environmental Significance Database - RESTRICTED - Metadata only. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/c01c4693-0a51-4dbc-bbbd-7a07952aa5f6.

This test dataset comprised of 800 24-hour Holter recordings from the test set of the randomized clinical cohort in the paper. Each recording includes the RR-interval data that are extracted from a 24-hour dynamic 12-lead ECG recording captured by a Holter machine (DMS Holter Company, Stateline, NV, USA) at three campuses (Main Campus, Optical Valley Campus, and Sino-French New City Campus) of the Cardiac Function Examination Center (Division of Cardiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China). All the data were initially interpreted by primary cardiologists, then were further reviewed by three senior board-certified cardiologists to ensure the correctness of the base diagnostic labels. The cardiologist committees discussed by consensus the annotated records and provided a reference standard for model evaluation. Each atrial fibrillation(AF) episode included the accurately labeled start time and end time for patients with paroxysmal AF (PAF). The start and end times of each PAF episode were the corresponding time of the first atrial wave with an atrial rate greater than 350 beats/min and the corresponding time of the first P-wave with sinus rhythm after the termination of AF. Specifically, PAF episodes lasting more than 30 s were accurately labeled, and those lasting less than 30 s were labeled as accurately as possible. Moreover, each interval of premature beat or tachycardia was marked as “A” (atrium event) or “V” (ventricle event), and the long RR interval caused by QRS wave dropping was marked as “B” to further label Second-degree atrioventricular block. The 800 recordings included 200 whole-course AF (WAF), 200 PAF and 400 NAF recordings. For WAF patient data, whole recording included only AF signals. For NAF patients, the entire recording data had no AF signals but included normal sinus rhythm, sinus arrhythmia, atrial arrhythmia, ventricular arrhythmia, atrioventricular block and so on. The data of PAF patients included both AF episodes and NAF signals. Both WAF and PAF patients were considered to be AF patients. The type of each recording is indicated by its file name and the comments on the contents can be seen in the picture "Note.jpg".

Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. This same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the SUS data set. SUS data covers all NHS Clinical Commissioning Groups (CCGs) in England, including: • private patients treated in NHS hospitals • patients resident outside of England • care delivered by treatment centres (including those in the independent sector) funded by the NHS Each SUS record contains a wide range of information about an individual patient admitted to an NHS hospital, including: • clinical information about diagnoses and operations • patient information, such as age group, gender and ethnicity • administrative information, such as dates and methods of admission and discharge • geographical information such as where patients are treated and the area where they live NHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published SUS data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.

Who SUS is for SUS provides data for the purpose of healthcare analysis to the NHS, government and others including:

The Secondary Users Service (SUS) database is made up of many data items relating to A&E care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital. • national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC • local Clinical Commissioning Groups (CCGs) • provider organisations • government departments • researchers and commercial healthcare bodies • National Institute for Clinical Excellence (NICE) • patients, service users and carers • the media

Uses of the statistics The statistics are known to be used for: • national policy making • benchmarking performance against other hospital providers or CCGs
• academic research • analysing service usage and planning change • providing advice to ministers and answering a wide range of parliamentary questions • national and local press articles • international comparison More information can be found at https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics https://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity"

The OpenOximetry Repository is a structured database designed to store clinical and laboratory pulse oximetry data and allows for consolidation of data sets held by collaborating organizations. Matched or independent readings of oxygen saturations, arterial blood gas samples, high frequency processed and unprocessed waveforms, and other physiologic metadata are supported (e.g. skin color readings). Contribution of data to the repository is encouraged; a data dictionary and standardized data collection protocols are provided to ensure consistent data archival and interpretation. The goal of this dataset is to enable researchers to interrogate the effects of physiologic and device variables on pulse oximeter performance.

A set of controlled desaturation studies is included in the initial release with ongoing updates planned as more data are available from both controlled lab desaturation studies and real world prospective clinical trials. Data captured during these studies may include paired pulse oximeter and arterial blood gas readings throughout a range of oxygen saturations, patient demographics and comorbidities, longitudinal quantitative and qualitative skin color measurement (using multiple methods at multiple anatomic locations), finger diameter, continuous ECG, arterial blood pressure, EtCO2/EtO2, unprocessed photoplethysmography (PPG), pH, PaCO2, hemoglobin, met-Hgb, CO-Hgb, respiratory rate and multiple other variables depending on the data source.

The "Data Dictionary" EXCEL file summarizes the contents of the database, with units for each parameter and descriptions of all acronyms.

The UVM TRC Research Report, Holmén et al. 2014, is linked below via URL to U.S. Department of Transportation National Transportation Library where PDF of the report may be found if the UVM Transporation Research Center website is not available (https://www.uvm.edu/cems/trc/trc-research-reports).

THE PUBLICATION

The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings". The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice.

THE DATA

This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below.

*** PLEASE NOTE ***

This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com.

    Citation
    Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)
    E.g. 1


    First Author
    Surname of the main author, for reference purposes only.
    E.g. Al-Atrash


    Publication
    Publication year
    E.g. 2020


    Dependent A
    List of variables used to measure thermal perception
    E.g. Neutral temperature/ Thermal sensation


    Dependent B
    Scale used to measure each dependent variable



    Interaction A
    List of interaction effect(s) included in the explanatory/predictive model(s) 
    E.g. Thermal and age/ Thermal and acoustical and personality


    Interaction B
    Is/are the effect(s) statistically significant?
    E.g. yes/ no/ (unknown)


    Crossed A
    List of crossed effect(s) included in the explanatory/predictive model(s) 
    E.g. Acoustical/ Personality/ Age


    Crossed B

    *Note: Temperature is a main effect and is not included in the list


    Explanatory A
    Type of explanatory model
    E.g. Observation/ Statistical/ N/A


    Explanatory B
    Description of the explanatory model
    E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions


    Predictive A
    Does the article include a predictive model?
    E.g. yes/ no


    Predictive B
    Type of predictive algorithm
    E.g. Logistic regression/ N/A


    Predictive C
    Description or formulation of the predictive model
    E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level


    Performance
    Reported predictive performance
    E.g. Accuracy = 80%/ F-score = 0.8/ N/A


    Location
    City in which the measurements take place
    E.g. Paris


    Period
    Period over which the measurements take place
    E.g. Jan-Feb 2020


    Start time
    Time of day at which the measurements begin
    *Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.


    Study type
    Type of building and whether the experimental conditions are controlled by the experiment leader
    E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)


    Building layout
    Building layout
    E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)


    Exposure
    Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time
    *Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.


    Number of buildings/chambers
    Number of different locations used for conducting measurements
    E.g. 1


    Number of participants
    Number of individuals who take part in each experiment
    *Note: Outliers who are subsequently excluded from the modelling phase are not included.


    Survey type
    Description of the type of survey used for subjective measurements
    E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A


    Survey content
    Are the contents of the survey provided in the article?
    E.g. Available/ unavailable


    Survey source
    Is/are the source(s) of the survey items mentioned in the article?
    E.g. Available/ unavailable


    Survey reliability
    Is the reliability of the survey items reported in the article?
    E.g. Available/ unavailable


    Survey duration
    Is the survey duration reported in the article?
    E.g. Available/ unavailable


    Context A
    Overview of the contextual information provided by the authors
    E.g. Room layout/ Room dimennsions


    Context B
    Qualitative/quantitative contextual information
    E.g. Figure containing room layout/ 3m x 3m x 5m


    Contextual variables A
    List of contextual variable(s) measured by the researchers (see Fig. A.)
    *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.


    Contextual variables B
    Range of values included in the experiment and their respective units.
    E.g. figure


    Social variables A
    List of social variable(s) measured by the researchers (see Fig. A.)
    *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.


    Social variables B
    Range of values included in the experiment and their respective units.
    E.g. [1,2,3,4,5]


    Personal variables A
    List of contextual variable(s) measured by the researchers (see Fig. A.)
    *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.


    Personal variables B
    Range of values included in the experiment and their respective units.
    E.g. [red, blue]


    Physical variables A
    List of physical variable(s) measured by the researchers (see Fig. A.)
    *Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.


    Physical variables B
    Range of values included in the experiment and their respective units
    E.g. dB(A)


    Full-factorial
    Is/are the experiment(s) full-factorial?
    *Note: Uncontrolled field experiments are automatically labelled as fractional factorial.


    (Participant) Control
    Do participants have control over one or more experimental conditions?
    E.g. Yes/ No


    With/between subjects
    Are the experimental conditions shared between or within the participants?
    E.g. w/ b


    Fixed variables A
    List of variables reported as constant during the measurements
    E.g. Relative humidity/ Metabolic rate


    Fixed variables A
    (Range of) values and their respective units.
    E.g. 30-40%/ 1.2 met


    Summary
    Description of the research outome (outcome of the explanatory and/or predictive modelling)
    E.g. Lack of perceived control has a significant negative effect on neutral temperatures.


    Evaluation
    Are the participants invited to evaluate their experience once the experiment has been completed? 
    E.g. Yes/ no

Note: The data in v1.1.0 has not yet been optimised for analytics.

This paper evaluates the degree of salliency of texts in natural scenes using visual saliency models. A large scale scene image database with pixel level ground truth is crated for this purpose. Using this scene image database and five stat-of-the-art models, visual saliency maps that represent the degree of saliency of the objects are calculated.