Abstract This paper analyzes questions and answers - a type of sequence that is constitutive of police interrogations. By means of Multimodal Conversation Analysis, it investigates how the safeguarding of information concerning crimes unfolds in police interrogations. A fine-grained sequential and multimodal analysis of the audio and/or video recorded interrogations reveals that the safeguarding of information is accomplished not only by the interrogated suspects in their responsive actions, but also ensued by the police officers by means of their question design. Interrogated suspects safeguard facts about crimes by resisting in providing the information requested in responsive turns that do not answer but that instead claim lack of knowledge, remembrance or awareness. Police officers, on the other hand, afford and initiate suspects’ information safeguarding by designing questions with verbs as to know and to remember. Such question design vouchsafes suspects to negate knowledge and remembrance of the requested information while aligning with the preference of the question format and presenting no resistance.

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 FIO program encompasses a wide range of interactions between the Boston Police Department (BPD) and private individuals. By releasing the records of these interactions, BPD hopes to add transparency to the execution of the program while still protecting the privacy of the individuals involved. These records are now sourced from three different record management systems titled: (OLD RMS) (NEW RMS) and (MARK43). The differences between the resulting files are described below.

About the FIO Records (Mark43) Files (Sept 29 2023 - Dec 31 2024)

These records are compiled from the BPD’s new Records Management System (RMS) on the BPD's FIO program. MARK43 went live September 29, 2019 and the FIO information has been structured into two separate tables. These tables are the same titles as (NEW RMS) but include new or different data points as retrieved from MARK43.

• FieldContact, which lists each contact between BPD and one or more individuals
• FieldContact_Name, which lists each individual involved in these contacts.

A FIO Data Key has also been created and posted to help distinguish the data categories (Data Key (Mark43)).

Lastly, FIOs are maintained in a live database and information related to each individual may change overtime. The data provided here should be considered a static representation of the Field Interaction and/or Observation that occurred in 2019.

NULL indicates no entry was made for an optional field.

About the FIO Records 2015 (New RMS) and 2016, 2017, 2018, 2019, and 2020 (Jan 1 - Sept 29 2020) Files

These records are compiled from the BPD’s new Records Management System (RMS) on the BPD's FIO program. The new RMS, which went live in June, 2015, structures the FIO information into two separate tables:

• FieldContact, which lists each contact between BPD and one or more individuals
• FieldContact_Name, which lists each individual involved in these contacts

While these two tables align on the field contact number (fc_num) column, it is not methodologically correct to join the two datasets for the purpose of generating aggregate statistics on columns from the FieldContact table. Doing so would lead to incorrect estimates stemming from contacts with multiple individuals. As noted in the Data Key (New RMS) file, several of the columns in the FieldContact table apply to the contact as a whole, but may not necessarily apply to each individual involved in the contact. These include:

• frisked
• searchperson
• summonsissued
• circumstances
• basis
• contact_reason

For example, the frisked column contains a value of Y if any of the individuals involved in a contact were frisked, but it would be inaccurate to assume that all individuals were frisked during that contact. As such, extrapolating from the frisked column for a contact to each individual and then summing across them would give an artificially high estimate of the number of people frisked in total. Likewise, the summonsissued column indicates when someone involved in a contact was issued a summons, but this does not imply that everyone involved in a contact was issued a summons.

For a detailed listing of columns in each table, see both tables of the Data Key (New RMS) file below.

About the FIO Records 2011 - 2015 (Old RMS) File

These records are sourced from BPD's older RMS, which was retired in June, 2015. This system (which stored all records in a single table, rather than the two tables in the newer system) captures similar information to the new RMS, but users should note that the fields are not identical and exercise care when comparing or combining records from each system.

Additional Notes

• The data provided is FIO information entered into the new system from June, 2015 through December, 2016, which includes some interactions which occurred before June, 2015 which were entered after the transition from the old system. For comprehensive analyses of interactions prior to the introduction of the new RMS, users will need to include data on interactions prior to June, 2015 from the 2015 (New RMS) file.
• These files are extracted from live databases which may have records added or updated at any time. As such, the number and content of records shared here may differ slightly from versions used to produce analyses such as those linked below, due to subsequent revisions to the underlying database records.
• A contact can consist of an observation of a vehicle, without direct contact with a person. This would create a record where no person-level details are recorded.

For more information on the FIO Program, please visit:

Boston Police Commissioner Announces Field Interrogation and Observation (FIO) Study Results

Commissioner Evans Continues Efforts to Increase Transparency and Accountability of Policing Activities to the Public

Boston Police Department Releases Latest Field Interrogation Observation Data

The monthly means of ECMWF ERA-40 reanalysis isentropic level analysis data are in this dataset.

The modeled data in these archives are in the NetCDF format (https://www.unidata.ucar.edu/software/netcdf/). NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for C, C++, Java, and Fortran. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl. Data in netCDF format is: • Self-Describing. A netCDF file includes information about the data it contains. • Portable. A netCDF file can be accessed by computers with different ways of storing integers, characters, and floating-point numbers. • Scalable. Small subsets of large datasets in various formats may be accessed efficiently through netCDF interfaces, even from remote servers. • Appendable. Data may be appended to a properly structured netCDF file without copying the dataset or redefining its structure. • Sharable. One writer and multiple readers may simultaneously access the same netCDF file. • Archivable. Access to all earlier forms of netCDF data will be supported by current and future versions of the software. Pub_figures.tar.zip Contains the NCL scripts for figures 1-5 and Chesapeake Bay Airshed shapefile. The directory structure of the archive is ./Pub_figures/Fig#_data. Where # is the figure number from 1-5. EMISS.data.tar.zip This archive contains two NetCDF files that contain the emission totals for 2011ec and 2040ei emission inventories. The name of the files contain the year of the inventory and the file header contains a description of each variable and the variable units. EPIC.data.tar.zip contains the monthly mean EPIC data in NetCDF format for ammonium fertilizer application (files with ANH3 in the name) and soil ammonium concentration (files with NH3 in the name) for historical (Hist directory) and future (RCP-4.5 directory) simulations. WRF.data.tar.zip contains mean monthly and seasonal data from the 36km downscaled WRF simulations in the NetCDF format for the historical (Hist directory) and future (RCP-4.5 directory) simulations. CMAQ.data.tar.zip contains the mean monthly and seasonal data in NetCDF format from the 36km CMAQ simulations for the historical (Hist directory), future (RCP-4.5 directory) and future with historical emissions (RCP-4.5-hist-emiss directory). This dataset is associated with the following publication: Campbell, P., J. Bash, C. Nolte, T. Spero, E. Cooter, K. Hinson, and L. Linker. Projections of Atmospheric Nitrogen Deposition to the Chesapeake Bay Watershed. Journal of Geophysical Research - Biogeosciences. American Geophysical Union, Washington, DC, USA, 12(11): 3307-3326, (2019).

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🌆 About This Dataset

This synthetic dataset simulates 300 global cities across 6 major geographic regions, designed specifically for unsupervised machine learning and clustering analysis. It explores how economic status, environmental quality, infrastructure, and digital access shape urban lifestyles worldwide.

🎯 Perfect For:

• 📊 K-Means, DBSCAN, Agglomerative Clustering
• 🔬 PCA & t-SNE Dimensionality Reduction
• 🗺️ Geospatial Visualization (Plotly, Folium)
• 📈 Correlation Analysis & Feature Engineering
• 🎓 Educational Projects (Beginner to Intermediate)

📦 What's Inside?

🔥 Key Features

✅ Realistic Correlations: Income strongly predicts rent (+0.8), internet access (+0.7), and happiness (+0.6)
✅ Regional Diversity: Each region has distinct economic and environmental characteristics
✅ Clustering-Ready: Naturally separable into 4-5 lifestyle archetypes
✅ Beginner-Friendly: No data cleaning required, includes example code
✅ Documented: Comprehensive README with methodology and use cases

🚀 Quick Start Example

import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

# Load and prepare
df = pd.read_csv('city_lifestyle_dataset.csv')
X = df.drop(['city_name', 'country'], axis=1)
X_scaled = StandardScaler().fit_transform(X)

# Cluster
kmeans = KMeans(n_clusters=5, random_state=42)
df['cluster'] = kmeans.fit_predict(X_scaled)

# Analyze
print(df.groupby('cluster').mean())

🎓 Learning Outcomes

After working with this dataset, you will be able to: 1. Apply K-Means, DBSCAN, and Hierarchical Clustering 2. Use PCA for dimensionality reduction and visualization 3. Interpret correlation matrices and feature relationships 4. Create geographic visualizations with cluster assignments 5. Profile and name discovered clusters based on characteristics

📚 Ideal For These Projects

• 🏆 Kaggle Competitions: Practice clustering techniques
• 📝 Academic Projects: Urban planning, sociology, environmental science
• 💼 Portfolio Work: Showcase ML skills to employers
• 🎓 Learning: Hands-on practice with unsupervised learning
• 🔬 Research: Urban lifestyle segmentation studies

🌍 Expected Clusters

🛠️ Technical Details

• Format: CSV (UTF-8)
• Size: ~300 rows × 10 columns
• Missing Values: 0%
• Data Types: 2 categorical, 8 numerical
• Target Variable: None (unsupervised)
• Correlation Strength: Pre-validated (r: 0.4 to 0.8)

📖 What Makes This Dataset Special?

Unlike random synthetic data, this dataset was carefully engineered with: - ✨ Realistic correlation structures based on urban research - 🌍 Regional characteristics matching real-world patterns - 🎯 Optimal cluster separability (validated via silhouette scores) - 📚 Comprehensive documentation and starter code

🏅 Use This Dataset If You Want To:

✓ Learn clustering without data cleaning hassles
✓ Practice PCA and dimensionality reduction
✓ Create beautiful geographic visualizations
✓ Understand feature correlation in real-world contexts
✓ Build a portfolio project with clear business insights

📊 Acknowledgments

This dataset was designed for educational purposes in machine learning and data science. While synthetic, it reflects real patterns observed in global urban development research.

Happy Clustering! 🎉

This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.

An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

In-depth proteome exploration of complex body fluids is a challenging task that requires optimal sample preparation and analysis in order to reach novel and meaningful insights. Analysis of follicular fluids is similarly difficult as that of blood serum due to the ubiquitous presence of several highly abundant proteins and a wide range of protein concentrations. Therefore, the accessibility of this complex body fluid for liquid chromatography-tandem mass spectrometry (LC/MS/MS) analysis is a challenging opportunity to gain insights into the physiological status or to identify new diagnostic and prognostic markers for e.g. the treatment of infertility. We compared different sample preparation methods (FASP, eFASP and in-solution digestion) and three different data analysis software packages (Proteome Discoverer with SEQUEST and Mascot, Maxquant with Andromeda) in conjunction with semi- and full-tryptic databank search approaches in order to obtain a maximum coverage of the proteome.

Background Gene expression profiling among different tissues is of paramount interest in various areas of biomedical research. We have developed a novel method (DADA, Digital Analysis of cDNA Abundance), that calculates the relative abundance of genes in cDNA libraries. Results DADA is based upon multiple restriction fragment length analysis of pools of clones from cDNA libraries and the identification of gene-specific restriction fingerprints in the resulting complex fragment mixtures. A specific cDNA cloning vector had to be constructed that governed missing or incomplete cDNA inserts which would generate misleading fingerprints in standard cloning vectors. Double stranded cDNA was synthesized using an anchored oligo dT primer, uni-directionally inserted into the DADA vector and cDNA libraries were constructed in E. coli. The cDNA fingerprints were generated in a PCR-free procedure that allows for parallel plasmid preparation, labeling, restriction digest and fragment separation of pools of 96 colonies each. This multiplexing significantly enhanced the throughput in comparison to sequence-based methods (e.g. EST approach). The data of the fragment mixtures were integrated into a relational database system and queried with fingerprints experimentally produced by analyzing single colonies. Due to limited predictability of the position of DNA fragments on the polyacrylamid gels of a given size, fingerprints derived solely from cDNA sequences were not accurate enough to be used for the analysis. We applied DADA to the analysis of gene expression profiles in a model for impaired wound healing (treatment of mice with dexamethasone). Conclusions The method proved to be capable of identifying pharmacologically relevant target genes that had not been identified by other standard methods routinely used to find differentially expressed genes. Due to the above mentioned limited predictability of the fingerprints, the method was yet tested only with a limited number of experimentally determined fingerprints and was able to detect differences in gene expression of transcripts representing 0.05% of the total mRNA population (e.g. medium abundant gene transcripts).

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

This data set contains National Centers for Environmental Prediction (NCEP) re-analysis monthly mean data 2001-2004 for the SBI domain in Matlab format.

This dataset is associated with the paper Knoop et al. (2019) titled "A generic gust definition and detection method based on wavelet-analysis" published in "Advances in Science and Research (ASR)" within the Special Issue: 18th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018. It contains the data and analysis software required to recreate all figures in the publication.

Attached please find the data and analysis code of "Regional and tele-connected impacts of the Tibetan Plateau surface darkening" (https://www.nature.com/articles/s41467-022-35672-w) Please cite the article if you want to use these data and analysis code ( Tang, S., Vlug, A., Piao, S. et al. Regional and tele-connected impacts of the Tibetan Plateau surface darkening. Nat Commun 14, 32 (2023). https://doi.org/10.1038/s41467-022-35672-w )

Above data and analysis codes include: 1) AnalysisData_Climate.m: Analysis code for climate experiments. Use Matlab to open and run this file. Note: some pathways in this code file are absolute pathways and should be changed, otherwise errors will be reported. 2) AnalysisData_Glacier.m: Analysis code for glacier simulations. Use Matlab to open and run this file. Results of the analysis part have been stored at Glacier_difference.mat. Note: some pathways in this code file are absolute pathways and should be changed, otherwise errors will be reported. 3) Basedata.mat: Store the base dataset used for the analysis in AnalysisData_Climate.m. 4) Climate_difference.mat: All the variables in this mat are derived from the climate experiments. GlobalX means the annual mean results, SummerX means the boreal summer results. The size of these variables: longitude*latitude*(atmospheric layer)*3 (CTL results, SCE results, significant). This data is used for AnalysisData_Climate.m. 5) Glacier_difference,mat: The meaning of each variables in this data is list in AnalysisData_Glacier.m. This data is used for AnalysisData_Glacier.m. 6) AnalysisData.zip: store the 99-year simulation results of some variables used in the AnalysisData_Climate.m. 7) Wilcoxon.m: Judge whether the difference is significant ( non-parametric Wilcoxon signed-rank test). 8) TPPolygon.shp: the shp file of the Tibetan Plateau. 9) brewermap.m: generate the colormap of each figure. 10) 00_rgi60_regions_xxx: shp file of the 13 (or 15) second-order regions over the Tibetan Plateau and its surrounding regions (Pfeffer et al., 2014; RGI Consortium et al., 2017). 11) draw_radar2.m: this file is used to draw figure 2(b). 12) p50_degree_glacier_volume_km3.tif: This tif is provided by Farinotti et al., 2019. 13) suppl_GlacierMIP_results.nc: this nc file is provided by Marzeion et al., 2020

If you have any questions or suggestions, please contact shuchangtang@pku.edu.cn

The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable. This data release and other associated products represent an expansion of the USGS national-scale shoreline database to include Puerto Rico and its islands, Vieques and Culebra. The United States Geological Survey (USGS) in cooperation with the Coastal Research and Planning Institute of Puerto Rico (CoRePI, part of the Graduate School of Planning at the University of Puerto Rico, Rio Piedras Campus) has derived and compiled a database of historical shoreline positions using a variety of methods. These shorelines are used to measure the rate of shoreline change over time.

The Software Defined Data Center Market size was valued at USD 32.73 USD billion in 2023 and is projected to reach USD 107.39 USD billion by 2032, exhibiting a CAGR of 18.5 % during the forecast period. The Software-Defined Data Center (SDDC) market refers to a restoring data centers mechanism by the virtualization and automation of infrastructure elements. It does blurring of resources such as computing, storing, and networking from hardware, and software is therefore configured to rationally manage and provide resources dynamically. SDDCs are currently the industry's common practice to focus on infrastructure scalability, faster response in case of exceeding the promised workload, and IT cost optimization. The main selling point is that it can be used in the cloud, big data analytics, and DevOps environments. Concerning the trends, the usage of consultancy services and distributed models and systems is increasingly preferred. Therefore, the demand for SDDCs that can have a holistic integration among the various cloud platforms is having a promising future. Furthermore, developments on the horizon in terms of software like containerization and the edge are expected to lead to a change in the paradigm of SDDC. Key drivers for this market are: Increasing Deployment of Data Center Infrastructure and Cloud Video Streaming Services to Aid Growth of Market. Potential restraints include: Lack of Acceptance of Virtualization Standards Globally to Obstruct Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record.

This dataset contains ERA5.1 surface level analysis parameter data ensemble means over the period 2000-2006. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). The ensemble means are calculated from the ERA5.1 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). The main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store.

The size of the Software Defined Data Centers Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 26.60% during the forecast period. Recent developments include: July 2022 -DartPoints, a cutting-edge digital infrastructure provider, has announced a groundbreaking technical collaboration with the University of South Carolina. DartPoints will deliver a customized Software-Defined Data Center (SDDC) solution to replace the university's existing data center., August 2022 - VMware Explore 2022, VMware Aria, a multi-cloud management portfolio, delivers a collection of end-to-end solutions for controlling the cost, performance, configuration, and delivery of infrastructure and cloud-native apps. VMware Aria is powered by VMware Aria Graph, a graph-based data store that captures the complexity of customers' multi-cloud environments., January 2023 - Rackspace Technology, a leading provider of end-to-end multi-cloud technology solutions, has launched Rackspace Technology Software-Defined Data Center (SDDC) Rackspace SDDC Enterprise, Rackspace SDDC Business, and Rackspace SDDC Flex. These new products will give enterprises specialized solutions to bridge the gap between the cloud and data centers.. Key drivers for this market are: Cost Reduction in Hardware and Other Resources is Driving the Growth of the Market.. Potential restraints include: Data Security While Deploying SDDC is a Major Challenge. Notable trends are: Software-Defined Storage to Dominate the Market.

The Software Defined Data Center Market size is expected to reach a valuation of USD 369.91 billion in 2033 growing at a CAGR of 19.20%. The Software Defined Data Center Market research report classifies market by share, trend, demand, forecast and based on segmentation.

The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.