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IntroductionWhen datasets are collected mainly for administrative rather than research purposes, data quality checks are necessary to ensure robust findings and to avoid biased results due to incomplete or inaccurate data.
When done manually, data quality checks are time-consuming. We introduced automation to speed up the process and save effort.
Objectives and ApproachWe have devised a set of automated generic quality checks and reporting, which can be run on any dataset in a relational database without any dataset-specific knowledge or configuration.
The code is written in Python. Checks include: linkage quality, agreement with a population data source, comparison with previous data version, duplication checks, null count, value distribution and range, etc.
Where dataset metadata is available, checks for validity against lookup tables are included, and the output report includes documentation on data contents. An HTML report with dynamic datatables and interactive graphs, allowing easy exploration of the results, is produced using RMarkdown.
ResultsThe automation of the generic data quality check provides an easy and quick tool to report on data issues with minimal effort. It allows comparison with reference tables, lookups and previous versions of the same table to highlight differences. Moreover, this tool can be provided for researchers as a means to get more detailed understanding about their data.
While other research data quality tools exist, this tool is distinguished by its features specific to linked data research, as well as implementation in a relational database environment. It has been successfully tested on datasets of over two billion rows.
The tool was designed for use within the SAIL Databank, but could easily be adapted and used in other settings.
Conclusion/ImplicationsThe effort spent on automating generic testing and reporting on data quality of research datasets is more than compensated by its outputs. Benefits include quick detection and scrutiny of many sources of invalid and incomplete data. This process can easily be expanded to accommodate more standard tests.

ObjectiveThe Welsh Longitudinal General Practice (WLGP) dataset contains over 4 billion records. Due to the size and the need to filter the dataset to get the necessary general practice interaction results, query performance is poor. To overcome this, a 'cleaned' version of the dataset needed to be created.
ApproachAn R package was created that would be run when a new refresh of the WLGP data is provided. Two new tables would be created based on the original WLGP Events table.
ResultsA WLGP Cleaned Dataset R Package was produced and creates a reformatted events table and events look up table. The reformatted GP events table reduces the original events table from 14 columns to 8 key columns. The events look up table takes distinct events from the original WLGP events table and produces a new table including columns such as the event description, type and hierarchy levels. Both tables are then linked using an event code ID.
ConclusionThe R package now creates a new reformatted events table as well as an events look up table which is run after a WLGP refresh is provided. The new tables are then provisioned to any projects that have access to the dataset.
ImplicationsThe new tables has improved query performance for our researchers, providing them with a table with all the necessary information and an easy way to decipher events codes. This has improved the amount of time researchers have spent manipulating and querying the tables.

ObjectiveWe're introducing a comprehensive approach to enhance sensitive data flagging within our Data Quality (DQ) tool. Within de-identified health records, sensitive data such as user ids, post codes, phone numbers, and addresses pose significant privacy risks if exposed in their raw form. To address this challenge, we propose using all necessary regex patterns and free text field checks for sensitive data flagging with the objective of efficient detection within large datasets and ultimately reporting such data.
ApproachA regex map has been developed along free text field thresholds, distribution counts and percentage checks to allow a generic approach. This ensures that any new regexes can be added to the flagging process without significant code changes.
Once identified, sensitive data instances, including fields that contain free text are flagged and displayed in an html report that is human readable by DQ reviewers.
ResultsThrough a combination of SQL and R regex based text processing, our approach allows a seamless identification and flagging of sensitive data within datasets. Sensitive data for large tables of +25 million records with over 50 columns are getting flagged in less than 150 seconds (approx. 2 minutes).
ConclusionOur development offers a practical solution for sensitive data flagging in complex datasets where team members can implement robust sensitive data flagging by adding more regex formulas in the codebase.
ImplicationsDQ reviewers can inspect the sensitive flagged fields in a human readable way, providing an extra measure of confidence when determining the quality of de-identified health records.

Performance, scalability, and resilience to variability of Si SOI FinFETs and gate-all-around (GAA) nanowires (NWs) are studied using in-house-built 3-D simulation tools. Two experimentally based devices, a 25-nm gate length FinFET and a 22-nm GAA NW are modeled and then scaled down to 10.7and 10-nm gate lengths, respectively. A TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability affecting OFF and ON characteristics are investigated and compared. In the OFF-region, the FinFETs have over an order of magnitude larger OFF-current that those of the equivalent GAA NWs. In the ON-region, the 25/10.7-nm gate length FinFETs deliver 20/58% larger ON-current than the 22/10-nm gate length GAA NWs. The FinFETs are more resilient to the MGG and LER variability in the subthreshold compared to the GAA NWs. However, the MGG ON-current variability is larger for the 10.7-nm FinFET than that for the 10-nm GAA NW. The LER ON-current variability depends largely on the RMS height; whereas a 0.6-nm RMS height yields a similar variability for both FinFETs and GAA NWs. Finally, the industry preferred (110) channel orientation is more resilient to the MGG and LER variability in both architectures. ; This work was supported in part by the Spanish Government under Project TIN2013-41129-P and Project TIN2016-76373-P, in part by the Xunta de Galicia and FEDER under Grant GRC 2014/008, in part by the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019) under Grant ED431G/08, and in part by the Spanish Ministry of Economy and Competitiveness and FEDER under Grant TEC2014-59402-JIN. The work of G. Indalecio was supported by the Programa de Axudas Etapa Posdoutoral da Xunta de Galicia under Grant 2017/077 ; SI

Variability of semiconductor devices is seriously limiting their performance at nanoscale. The impact of variability can be accurately and effectively predicted by computer-aided simulations in order to aid future device designs. Quantum corrected (QC) drift-diffusion (DD) simulations are usually employed to estimate the variability of state-of-the-art non-planar devices but require meticulous calibration. More accurate simulation methods, such as QC Monte Carlo (MC), are considered time consuming and elaborate. Therefore, we predict TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability on a 10-nm gate length gate-all-around Si nanowire FET and perform a rigorous comparison of the QC DD and MC results. In case of the MGG, we have found that the QC DD predicted variability can have a difference of up to 20% in comparison with the QC MC predicted one. In case of the LER, we demonstrate that the QC DD can overestimate the QC MC simulation produced variability by a significant error of up to 56%. This error between the simulation methods will vary with the root mean square (RMS) height and maximum source/drain $n$ -type doping. Our results indicate that the aforementioned QC DD simulation technique yields inaccurate results for the ON-current variability ; This work was supported in part by the Spanish Government under Project TIN2013-41129-P and Project TIN2016-76373-P, in part by Xunta de Galicia and FEDER funds under Grant GRC 2014/008, in part by the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019), under Grant ED431G/08, and in part by the Centro de Supercomputación de Galicia (CESGA) for the computer resources provided. The work of G. Indalecio was supported by the Programa de Axudas á Etapa Posdoutoral da Xunta de Galicia under Grant 2017/077. The work of N. Seoane was supported by the RyC program of the Spanish Ministerio de Ciencia, Innovación y Universidades under Grant RYC-2017-23312 ; SI

Background/RationaleLinked administrative datasets offer great potential for research, but also present major challenges—including the preparation of operational data into a form suitable for efficient research, complex and computationally demanding analysis, and the need to capture and share information about dataset contents and research methods.
Main AimThe analytical services team in the Secure Anonymised Information Linkage (SAIL) Databank is creating interconnected tools and systems to automate the preparation and analysis of research data and to curate information about datasets and research methods. Our underlying goal is to make linked data research orders of magnitude faster and cheaper, as well as improve its consistency and quality.
MethodsSeveral key developments are ongoing:

Automation of data quality checking.
Management of dataset metadata.
Processing of raw source datasets into cleaned, research-ready data assets.
The Concept Library, an application for creating, using, and sharing knowledge about research definitions and methods.
A suite of R packages for analysis.

Web Application Programming Interfaces will allow these pieces to work together as an integrated system enabling efficient research.
ResultsInitial versions of dataset quality checking, cleaned datasets, and R code to implement common tasks are already in day-to-day use by researchers within SAIL. An advisory group has been convened to help guide the work.
For example, shared library code that flags conditions within health data has been used across multiple projects; a cleaned dataset measuring follow-up within primary care has been used by more than 100 projects.
ConclusionOur proof-of-concept work demonstrates the ability of shared code and cleaned data to meet needs across multiple projects, saving effort and standardizing results. Ongoing work to develop and integrate these tools should further streamline the research process, increasing the output and public benefit of SAIL and other data sources.

ObjectiveThe volume and frequency of refreshed data within the [organisation] has increased significantly since the beginning of the COVID-19 pandemic. Therefore, a more efficient data quality (DQ) checking process was necessary.
Approach Having previously developed an automated DQ checking tool, the focus was on re-engineering the process of DQ task allocation and communication of results.
Results 5 analysts were trained in DQ checking. A JIRA workflow tracks the management of data loading. When a dataset is ready for DQ, the Data Manager allocates a ticket to the DQ Lead who then allocates it onto one of the 5 analysts. Via a DQ Slack channel, the analyst is informed and acknowledges receipt of the task. On completion of DQ, the analyst updates the ticket and transfers it to the appropriate workflow stage. Passed DQ tickets are transferred to the Data Manager for data release, whereas failed ones are placed "On Hold". The DQ Lead triages the issues and liaises with relevant parties for resolution, which may require data amendments. On receipt of amended data, the DQ ticket is transferred back to the queue and the analyst is notified to re-check the data.
ConclusionsThe team now complete a high volume of DQ checks efficiently. In 2023, 405 datasets, containing 1716 tables, were quality checked, with the initial DQ taking, on average, 2.6 days.
Implications The improved speed of DQ checking ensures projects access the latest available data whilst maintaining the expected DQ levels, integrity and reputation of the TRE.

ObjectiveThe HDRUK Phenotype Library (phenotypes.healthdatagateway.org) shares definitions used to measure concepts of interest (such as diagnoses or treatments) within health datasets. It already holds more than 1000 phenotypes, with researchers able to contribute their work via an API. We aimed to create a more user-friendly method of contributing to the Library.
ApproachWe designed a phenotype creation workflow enabling users to create and submit new content via web interface. Goals included clarity and ease of use for a broad range of users.
ResultsA home page shows researchers' own content. Researchers can create new phenotypes using a web form, entering metadata such as name, authors, description, and publications. Code lists are defined via one or more rules, including search terms or referring to another phenotype, or by CSV upload. Users can make their phenotypes accessible to a research group or to all authenticated users, as well as publish content on the web. Publication requests are reviewed to ensure content is complete and appropriate. Editing, with full history and version control, is also supported.
ConclusionsWe implemented and released the new features. We are currently engaging researchers to get feedback and invite content submission.
ImplicationsThe benefit of tools to support research transparency and repeatability is only realized when they are adopted. We hope that a GUI to support phenotype creation will broaden the Library's user base help it serve as an enabler of higher-quality, more efficient research across the worldwide health data research community.