Trusted Research Environments (TREs) enable analysis of sensitive data under strict security assertions that protect the data with technical organizational and legal measures from (accidentally) being leaked outside the facility. While many TREs exist in Europe, little information is available publicly on the architecture and descriptions of their building blocks & their slight technical variations. To shine light on these problems, we give an overview of existing, publicly described TREs and a bibliography linking to the system description. We further analyze their technical characteristics, especially in their commonalities & variations and provide insight on their data type characteristics and availability. Our literature study shows that 47 TREs worldwide provide access to sensitive data of which two-thirds provide data themselves, predominantly via secure remote access. Statistical offices make available a majority of available sensitive data records included in this study.

Methodology

We performed a literature study covering 47 TREs worldwide using scholarly databases (Scopus, Web of Science, IEEE Xplore, Science Direct), a computer science library (dblp.org), Google and grey literature focusing on retrieving the following source material:

• Peer-reviewed articles where available,
• TRE websites,
• TRE metadata catalogs.

The goal for this literature study is to discover existing TREs, analyze their characteristics and data availability to give an overview on available infrastructure for sensitive data research as many European initiatives have been emerging in recent months.

Technical details

This dataset consists of five comma-separated values (.csv) files describing our inventory:

• countries.csv: Table of countries with columns id (number), name (text) and code (text, in ISO 3166-A3 encoding, optional)
• tres.csv: Table of TREs with columns id (number), name (text), countryid (number, refering to column id of table countries), structureddata (bool, optional), datalevel (one of [1=de-identified, 2=pseudonomized, 3=anonymized], optional), outputcontrol (bool, optional), inceptionyear (date, optional), records (number, optional), datatype (one of [1=claims, 2=linked records]), optional), statistics_office (bool), size (number, optional), source (text, optional), comment (text, optional)
• access.csv: Table of access modes of TREs with columns id (number), suf (bool, optional), physical_visit (bool, optional), external_physical_visit (bool, optional), remote_visit (bool, optional)
• inclusion.csv: Table of included TREs into the literature study with columns id (number), included (bool), exclusion reason (one of [peer review, environment, duplicate], optional), comment (text, optional)
• major_fields.csv: Table of data categorization into the major research fields with columns id (number), life_sciences (bool, optional), physical_sciences (bool, optional), arts_and_humanities (bool, optional), social_sciences (bool, optional).

Additionally, a MariaDB (10.5 or higher) schema definition .sql file is needed, properly modelling the schema for databases:

• schema.sql: Schema definition file to create the tables and views used in the analysis.

The analysis was done through Jupyter Notebook which can be found in our source code repository: https://gitlab.tuwien.ac.at/martin.weise/tres/-/blob/master/analysis.ipynb

The following document contains the material produced for the testing of the "caring prototype" MyTISSE, an interactive user experience on Matisse's painting Bathers by a river (Art Institute of Chicago). Focusing on the change of the pigments made by the artist during the gestation of this canvas, the UX exploits creative engagement to catalyze meaning creation processes.

This pubblication contains the data collected through the form. All the participants agreed to the reuse and sharing of these data for academic purposes, filling in an ad hoc privacy form.

Hybrid poplars have demonstrated high biomass productivity in the North Central USA as short rotation woody crops (SRWCs). However, our ability to quantitatively predict productivity for sites that are not currently in SRWCs is limited. In this study, the Physiological Processes Predicting Growth (3-PG) model was (1) assigned parameters for hybrid poplars using species-specific physiological data and allometric relationships from previously published studies, (2) calibrated for the North Central region using previously-published biomass data from eight plantations along with site-specific climate and soils data, (3) validated against previously published biomass data from four other plantations using linear regression of actual versus predicted total aboveground dry biomass, (4) evaluated for sensitivity of the model to manipulation of the parameter for age at full canopy cover (fullCanAge) and the fertility rating growth modifier, and (5) combined with soil and climate data layers to produce a map of predicted biomass productivity for the states of Minnesota and Wisconsin. This package contains the polygon feature layer and tabular data that correspond to 'Using a process-based model (3-PG) to predict and map hybrid poplar biomass productivity in Minnesota and Wisconsin, USA.' (Headlee et al. 2013). The tabular data for mean annual biomass for hybrid poplar including the STATSGO soil and NARR climate values were used to generate the biomass values. The WTAvg_DM values represent the overall predicted biomass productivity for hybrid poplars.

This data release contains two tabular data files. PMRW_Daily_Runoff_Precipitation_WY86to19.csv contains daily mean runoff total daily precipitation from the Panola Mountain Research Watershed, Stockbridge, Ga. for water years 1986-2019, and the associated data quality and edit codes for each day. Detailed descriptions of each edit code for precipitation and stream stage data are included separately in the PMRW_EditCodes_Daily_Runoff_Precipitation_WY86to19.csv dataset.

This dataset (WH_D1_4_meanmonthly.csv) contains mean monthly water table depth data for 211 point locations, for which the data were originally captured at a higher temporal resolution and were additionally clipped to the temporal window (2015 onwards) of the available Earth Observations in the Sentinel-1 and Sentinel-2 archive. Links to higher resolution/longer time series of these source data, where these are already in the public domain, have been identified in the data submission in case future data users require more detailed water table datasets.Information on site co-ordinates, data period, condition class, and other details, are provided in the associated metadata file (WH_D1_4_metadata.csv). Further links to 165 additional water table dynamics data have been provided for future users, but were not summarised as monthly means in this data submission in case the source data are updated in future. Please refer to the README file for methodological details and important disclaimers.

This dataset (WH_D1_4_meanmonthly.csv) contains mean monthly water table depth data for 118 point locations, for which the data were originally captured at a higher temporal resolution and were additionally clipped to the temporal window (2015 onwards) of the available Earth Observations in the Sentinel-1 and Sentinel-2 archive. Links to higher resolution/longer time series of these source data, where these are already in the public domain, have been identified in the data submission in case future data users require more detailed water table datasets.Information on site co-ordinates, data period, condition class, and other details, are provided in the associated metadata file (WH_D1_4_metadata.csv). Please refer to the README file for methodological details and important disclaimers.

The Travel Time to Work indicator compares the mean, or average, commute time for Champaign County residents to the mean commute time for residents of Illinois and the United States as a whole. On its own, mean travel time of all commuters on all mode types could be reflective of a number of different conditions. Congestion, mode choice, changes in residential patterns, changes in the location of major employment centers, and changes in the transit network can all impact travel time in different and often conflicting ways. Since the onset of the COVID-19 pandemic in 2020, the workplace location (office vs. home) is another factor that can impact the mean travel time of an area. We don’t recommend trying to draw any conclusions about conditions in Champaign County, or anywhere else, based on mean travel time alone.

However, when combined with other indicators in the Mobility category (and other categories), mean travel time to work is a valuable measure of transportation behaviors in Champaign County.

Champaign County’s mean travel time to work is lower than the mean travel time to work in Illinois and the United States. Based on this figure, the state of Illinois has the longest commutes of the three analyzed areas.

The year-to-year fluctuations in mean travel time have been statistically significant in the United States since 2014, and in Illinois most recently in 2021 and 2022. Champaign County’s year-to-year fluctuations in mean travel time were statistically significant from 2021 to 2022, the first time since this data first started being tracked in 2005.

Mean travel time data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Travel Time to Work.

Sources: U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 November 2025).; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (16 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (17 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (29 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).

$(\mathrm{d}N_{\phi}/\mathrm{d}y)/\left\langle N_\mathrm{part}\right\rangle$ as a function of $\left\langle N_\mathrm{part}\right\rangle$ measured in the muon decay channel at forward rapidity in pp and...

This dataset is for the study of task decomposition effects in time estimation: the role of future boundaries and thought focus, and supplementary materials. Due to the previous research on the impact of task decomposition on time estimation, the role of time factors was often overlooked. For example, with the same decomposition, people subjectively set different time boundaries when facing difficult and easy tasks. Therefore, taking into account the time factor is bound to improve and integrate the research conclusions of decomposition effects. Based on this, we studied the impact of task decomposition and future boundaries on time estimation. Experiment 1 passed 2 (task decomposition/no decomposition) × Design an inter subject experiment with/without future boundaries, using the expected paradigm to measure the time estimation of participants; Experiment 2 further manipulates the time range of future boundaries based on Experiment 1, using 2 (task decomposition/non decomposition) × 3 (future boundaries of longer/shorter/medium range) inter subject experimental design, using expected paradigm to measure time estimation of subjects; On the basis of Experiment 2, Experiment 3 further verified the mechanism of the influence of the time range of future boundaries under decomposition conditions on time estimation. Through a single factor inter subject experimental design, a thinking focus scale was used to measure the thinking focus of participants under longer and shorter boundary conditions. Through the above experiments and measurements, we have obtained the following dataset. Experiment 1 Table Data Column Label Meaning: Task decomposition into grouped variables: 0 represents decomposition; 1 indicates no decomposition The future boundary is a grouping variable: 0 represents existence; 1 means it does not exist Zsco01: Standard score for estimating total task time A logarithm: The logarithmic value of the estimated time for all tasks Experiment 2 Table Data Column Label Meaning: The future boundary is a grouping variable: 7 represents shorter, 8 represents medium, and 9 represents longer The remaining data labels are the same as Experiment 1 Experiment 3 Table Data Column Label Meaning: Zplan represents the standard score for the focus plan score Zbar represents the standard score for attention barriers The future boundary is a grouping variable: 0 represents shorter, 1 represents longer

Streamflow data and statistics are vitally important for proper protection and management of both the water quality and water quantity of Alabama streams. Such data and statistics are available at U.S. Geological Survey streamflow-gaging stations, also referred to as streamgages or stations, but are often needed at ungaged stream locations. To address this need, the U.S. Geological Survey, in cooperation with numerous Alabama state agencies and organizations, developed regional regression equations for estimating selected low-flow frequency statistics and mean annual flow for ungaged locations in Alabama that are not substantially affected by tides, regulation, diversions, or other anthropogenic influences. This data release comprises the geographic information systems (GIS) layers and tabular data used to create the new low-flow and mean annual flow regression equations and implement them for the U.S. Geological Survey StreamStats application (https://streamstats.usgs.gov).

Many websites operate using the data in the IANA tz database. "What Is Daylight Saving Time" from timeanddate.com is a good place to start to find interesting information about time zones, such as the strange case of Lord Howe Island, Australia.

Data Dictionary

transitions.csv

Changes in the conversion of a given time zone to UTC (for example for daylight savings or because the definition of the time zone changed).

timezones.csv

Descriptions of time zones from the IANA time zone database.

timezone_countries.csv

Countries (or other place names) that overlap with each time zone.

countries.csv

Names of countries and other places.

Methane hydrates are present in marine seep systems and occur within the gas hydrate stability zone. Very little is known about their crystallite sizes and size distributions because they are notoriously difficult to measure. Crystal size distributions are usually considered as one of the key petrophysical parameters because they influence mechanical properties and possible compositional changes, which may occur with changing environmental conditions. Variations in grain size are relevant for gas substitution in natural hydrates by replacing CH4 with CO2 for the purpose of carbon dioxide sequestration. Here we show that crystallite sizes of gas hydrates from some locations in the Indian Ocean, Gulf of Mexico and Black Sea are in the range of 200–400 µm; larger values were obtained for deeper-buried samples from ODP Leg 204. The crystallite sizes show generally a log-normal distribution and appear to vary sometimes rapidly with location.

QUT Research Data Respository Dataset Resource available for download

Dataset extracted from the post 10 Important Questions on Fundamental Analysis of Stocks – Meaning, Parameters, and Step-by-Step Guide on Smart Investello.

This dataset is about: (Table 13-2) Ground temperature data (annual mean, minimum, maximum) of the polygon wall POK-01. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.858643 for more information.

DISPERSION IS SQRT(MEAN(N**2)-MEAN(N)**2). GAMMA2 IS MEAN((N-MEAN(N))**2)/MEAN(N)**2. GAMMA3 IS MEAN((N-MEAN(N))**3)/MEAN(N)**3. GAMMA IS MEAN((N-MEAN(N))**4)/MEAN(N)**4-3(GAMMA2)**2.

No description is available. Visit https://dataone.org/datasets/ed07bae5e056f183052d39a9c4dd53cf for complete metadata about this dataset.

A table of means and standard deviations of actual data used for model development.

2013-2023 Virginia Disability Characteristics by Census Tract. Contains estimates and margins of error.

Special data considerations: Large negative values do exist (more detail below) and should be addressed prior to graphing or aggregating the data. A null value in the estimate means there is no data available for the requested geography.

A value of -888,888,888 indicates that the estimate or margin of error is not applicable or not available.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table S1810 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

This dataset is about: (Table 4) Mean annual sea surface temperature estimates of sediment core A179-15. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.726686 for more information.