This template covers section 2.5 Resource Fields: Entity and Attribute Information of the Data Discovery Form cited in the Open Data DC Handbook (2022). It completes documentation elements that are required for publication. Each field column (attribute) in the dataset needs a description clarifying the contents of the column. Data originators are encouraged to enter the code values (domains) of the column to help end-users translate the contents of the column where needed, especially when lookup tables do not exist.

Data Dictionary template for Tempe Open Data.

This is a data dictionary example we will use in the MVP presentation. It can be deleted after 13/9/18.

This data dictionary describes most of the possible output options given in the Probe for EPMA software package developed by Probe Software. Examples of the data output options include sample identification, analytical conditions, elemental weight percents, atomic percents, detection limits, and stage coordinates. Many more options are available and the data that is output will depend upon the end use.

VIKING II was made possible thanks to Medical Research Council (MRC) funding. We aim to better understand what might cause diseases such as heart disease, eye disease, stroke, diabetes and others by inviting 4,000 people with 2 or more grandparents from Orkney and Shetland to complete a questionnaire and provide a saliva sample. This data dictionary outlines what volunteers were asked and indicates the data you can access. To access the data, please e-mail viking@ed.ac.uk.

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

LScD (Leicester Scientific Dictionary)April 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk/suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny Mirkes[Version 3] The third version of LScD (Leicester Scientific Dictionary) is created from the updated LSC (Leicester Scientific Corpus) - Version 2*. All pre-processing steps applied to build the new version of the dictionary are the same as in Version 2** and can be found in description of Version 2 below. We did not repeat the explanation. After pre-processing steps, the total number of unique words in the new version of the dictionary is 972,060. The files provided with this description are also same as described as for LScD Version 2 below.* Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2** Suzen, Neslihan (2019): LScD (Leicester Scientific Dictionary). figshare. Dataset. https://doi.org/10.25392/leicester.data.9746900.v2[Version 2] Getting StartedThis document provides the pre-processing steps for creating an ordered list of words from the LSC (Leicester Scientific Corpus) [1] and the description of LScD (Leicester Scientific Dictionary). This dictionary is created to be used in future work on the quantification of the meaning of research texts. R code for producing the dictionary from LSC and instructions for usage of the code are available in [2]. The code can be also used for list of texts from other sources, amendments to the code may be required.LSC is a collection of abstracts of articles and proceeding papers published in 2014 and indexed by the Web of Science (WoS) database [3]. Each document contains title, list of authors, list of categories, list of research areas, and times cited. The corpus contains only documents in English. The corpus was collected in July 2018 and contains the number of citations from publication date to July 2018. The total number of documents in LSC is 1,673,824.LScD is an ordered list of words from texts of abstracts in LSC.The dictionary stores 974,238 unique words, is sorted by the number of documents containing the word in descending order. All words in the LScD are in stemmed form of words. The LScD contains the following information:1.Unique words in abstracts2.Number of documents containing each word3.Number of appearance of a word in the entire corpusProcessing the LSCStep 1.Downloading the LSC Online: Use of the LSC is subject to acceptance of request of the link by email. To access the LSC for research purposes, please email to ns433@le.ac.uk. The data are extracted from Web of Science [3]. You may not copy or distribute these data in whole or in part without the written consent of Clarivate Analytics.Step 2.Importing the Corpus to R: The full R code for processing the corpus can be found in the GitHub [2].All following steps can be applied for arbitrary list of texts from any source with changes of parameter. The structure of the corpus such as file format and names (also the position) of fields should be taken into account to apply our code. The organisation of CSV files of LSC is described in README file for LSC [1].Step 3.Extracting Abstracts and Saving Metadata: Metadata that include all fields in a document excluding abstracts and the field of abstracts are separated. Metadata are then saved as MetaData.R. Fields of metadata are: List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.Step 4.Text Pre-processing Steps on the Collection of Abstracts: In this section, we presented our approaches to pre-process abstracts of the LSC.1.Removing punctuations and special characters: This is the process of substitution of all non-alphanumeric characters by space. We did not substitute the character “-” in this step, because we need to keep words like “z-score”, “non-payment” and “pre-processing” in order not to lose the actual meaning of such words. A processing of uniting prefixes with words are performed in later steps of pre-processing.2.Lowercasing the text data: Lowercasing is performed to avoid considering same words like “Corpus”, “corpus” and “CORPUS” differently. Entire collection of texts are converted to lowercase.3.Uniting prefixes of words: Words containing prefixes joined with character “-” are united as a word. The list of prefixes united for this research are listed in the file “list_of_prefixes.csv”. The most of prefixes are extracted from [4]. We also added commonly used prefixes: ‘e’, ‘extra’, ‘per’, ‘self’ and ‘ultra’.4.Substitution of words: Some of words joined with “-” in the abstracts of the LSC require an additional process of substitution to avoid losing the meaning of the word before removing the character “-”. Some examples of such words are “z-test”, “well-known” and “chi-square”. These words have been substituted to “ztest”, “wellknown” and “chisquare”. Identification of such words is done by sampling of abstracts form LSC. The full list of such words and decision taken for substitution are presented in the file “list_of_substitution.csv”.5.Removing the character “-”: All remaining character “-” are replaced by space.6.Removing numbers: All digits which are not included in a word are replaced by space. All words that contain digits and letters are kept because alphanumeric characters such as chemical formula might be important for our analysis. Some examples are “co2”, “h2o” and “21st”.7.Stemming: Stemming is the process of converting inflected words into their word stem. This step results in uniting several forms of words with similar meaning into one form and also saving memory space and time [5]. All words in the LScD are stemmed to their word stem.8.Stop words removal: Stop words are words that are extreme common but provide little value in a language. Some common stop words in English are ‘I’, ‘the’, ‘a’ etc. We used ‘tm’ package in R to remove stop words [6]. There are 174 English stop words listed in the package.Step 5.Writing the LScD into CSV Format: There are 1,673,824 plain processed texts for further analysis. All unique words in the corpus are extracted and written in the file “LScD.csv”.The Organisation of the LScDThe total number of words in the file “LScD.csv” is 974,238. Each field is described below:Word: It contains unique words from the corpus. All words are in lowercase and their stem forms. The field is sorted by the number of documents that contain words in descending order.Number of Documents Containing the Word: In this content, binary calculation is used: if a word exists in an abstract then there is a count of 1. If the word exits more than once in a document, the count is still 1. Total number of document containing the word is counted as the sum of 1s in the entire corpus.Number of Appearance in Corpus: It contains how many times a word occurs in the corpus when the corpus is considered as one large document.Instructions for R CodeLScD_Creation.R is an R script for processing the LSC to create an ordered list of words from the corpus [2]. Outputs of the code are saved as RData file and in CSV format. Outputs of the code are:Metadata File: It includes all fields in a document excluding abstracts. Fields are List_of_Authors, Title, Categories, Research_Areas, Total_Times_Cited and Times_cited_in_Core_Collection.File of Abstracts: It contains all abstracts after pre-processing steps defined in the step 4.DTM: It is the Document Term Matrix constructed from the LSC[6]. Each entry of the matrix is the number of times the word occurs in the corresponding document.LScD: An ordered list of words from LSC as defined in the previous section.The code can be used by:1.Download the folder ‘LSC’, ‘list_of_prefixes.csv’ and ‘list_of_substitution.csv’2.Open LScD_Creation.R script3.Change parameters in the script: replace with the full path of the directory with source files and the full path of the directory to write output files4.Run the full code.References[1]N. Suzen. (2019). LSC (Leicester Scientific Corpus) [Dataset]. Available: https://doi.org/10.25392/leicester.data.9449639.v1[2]N. Suzen. (2019). LScD-LEICESTER SCIENTIFIC DICTIONARY CREATION. Available: https://github.com/neslihansuzen/LScD-LEICESTER-SCIENTIFIC-DICTIONARY-CREATION[3]Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4]A. Thomas, "Common Prefixes, Suffixes and Roots," Center for Development and Learning, 2013.[5]C. Ramasubramanian and R. Ramya, "Effective pre-processing activities in text mining using improved porter’s stemming algorithm," International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, no. 12, pp. 4536-4538, 2013.[6]I. Feinerer, "Introduction to the tm Package Text Mining in R," Accessible en ligne: https://cran.r-project.org/web/packages/tm/vignettes/tm.pdf, 2013.

Traveller Genes is a research study supported by the Traveller community. We're looking at the genetics, origins and health of over 200 volunteers who have at least two grandparents who are or were Travellers. This includes Scottish Travellers, Irish Travellers, Romanichal or Romany, or Welsh Kale. We aim to identify the genetic origins and relationships of the Scottish Traveller community e.g. Highland Travellers, Lowland Travellers, Borders Romanichal Travellers. We also want to understand how Scottish Travellers are related to other communities and their overall patterns of health. Participants are asked to complete a questionnaire and provide a saliva sample. This data dictionary outlines what volunteers were asked and indicates the data you can access. To access the data, please e-mail travellergenes@ed.ac.uk.

This data includes the location of cooling towers registered with New York State. The data is self-reported by owners/property managers of cooling towers in service in New York State. In August 2015 the New York State Department of Health released emergency regulations requiring the owners of cooling towers to register them with New York State. In addition the regulation includes requirements: regular inspection; annual certification; obtaining and implementing a maintenance plan; record keeping; reporting of certain information; and sample collection and culture testing. All cooling towers in New York State, including New York City, need to be registered in the NYS system. Registration is done through an electronic database found at: www.ny.gov/services/register-cooling-tower-and-submit-reports. For more information, check http://www.health.ny.gov/diseases/communicable/legionellosis/, or go to the “About” tab.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been nine attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137) and the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The NCDS linked Scottish Medical Records (SMR) datasets include data files from the NHS Digital Hospital Episode Statistics (HES) database for those cohort members who provided consent to health data linkage in the Age 50 sweep, and had ever lived in Scotland.

The SMR database contains information about all hospital admissions in Scotland. The following datasets are available:

• SN 8761: National Child Development Study: Linked Administrative Data, Outpatient Attendance, Scottish Medical Records, 1996-2015: Secure Access (SMR00)
• SN 8762: (this study) National Child Development Study: Linked Administrative Data, Inpatient Attendance, Scottish Medical Records, 1981-2015: Secure Access (SMR01)
• SN 8763: National Child Development Study: Linked Administrative Data, Maternity Records, Scottish Medical Records, 1981-2002: Secure Access (SMR02)
• SN 8764: National Child Development Study: Linked Administrative Data, Prescribing Information System, Scottish Medical Records, 2009-2015: Secure Access (PIS)

Researchers who require access to more than one dataset need to apply for them individually.

Further information about the SMR database can be found on the https://www.ndc.scot.nhs.uk/Data-Dictionary/SMR-Datasets/">Information Services Division Scotland SMR Datasetswebpage.

CLS/SMR Digital Sub-licence agreement:

The linked SMR data have been processed by CLS and supplied to the UK Data Service (UKDS) under Secure Access Licence. Applicants wishing to access these data need to establish the necessary agreement with the UKDS and abide by the terms and conditions of the UKDS Secure Access licence. An additional condition of the licensing is that it is not permitted to link SMR data to NCDS data that include Scottish geographies.

Non-straightforward requests to include additional data not held by UKDS would be handled by the CLS Data Access Committee and referred to the Public Benefit and Privacy Panel (PBPP) if necessary.

A disorganized toy spreadsheet used for teaching good data organization. Learners are tasked with identifying as many errors as possible before creating a data dictionary and reconstructing the spreadsheet according to best practices.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been nine attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137) and the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under End User Licence (EUL) (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The NCDS linked Scottish Medical Records (SMR) datasets include data files from the NHS Digital Hospital Episode Statistics (HES) database for those cohort members who provided consent to health data linkage in the Age 50 sweep, and had ever lived in Scotland.

The SMR database contains information about all hospital admissions in Scotland. The following datasets are available:

• SN 8761 (this study): National Child Development Study: Linked Administrative Data, Outpatient Attendance, Scottish Medical Records, 1996-2015: Secure Access (SMR00)
• SN 8762: National Child Development Study: Linked Administrative Data, Inpatient Attendance, Scottish Medical Records, 1981-2015: Secure Access (SMR01)
• SN 8763: National Child Development Study: Linked Administrative Data, Maternity Records, Scottish Medical Records, 1981-2002: Secure Access (SMR02)
• SN 8764: National Child Development Study: Linked Administrative Data, Prescribing Information System, Scottish Medical Records, 2009-2015: Secure Access (PIS)

Researchers who require access to more than one dataset need to apply for them individually.

Further information about the SMR database can be found on the https://www.ndc.scot.nhs.uk/Data-Dictionary/SMR-Datasets/">Information Services Division Scotland SMR Datasets webpage.

CLS/SMR Digital Sub-licence agreement:

The linked SMR data have been processed by CLS and supplied to the UK Data Service (UKDS) under Secure Access Licence. Applicants wishing to access these data need to establish the necessary agreement with the UKDS and abide by the terms and conditions of the UKDS Secure Access licence. An additional condition of the licensing is that it is not permitted to link SMR data to NCDS data that include Scottish geographies.

Non-straightforward requests to include additional data not held by UKDS would be handled by the CLS Data Access Committee and referred to the Public Benefit and Privacy Panel (PBPP) if necessary.

Contains compressed file, "Proxy Advisory Firms and Corporate Shareholder Engagement.zip", which contains Stata code, Stata pseudo-datasets (to demonstrate format of data), and a data dictionary. Review of Financial Studies, forthcoming. (2024)

Description and PurposeThese data include the individual responses for the City of Tempe Annual Community Survey conducted by ETC Institute. These data help determine priorities for the community as part of the City's on-going strategic planning process. Averaged Community Survey results are used as indicators for several city performance measures. The summary data for each performance measure is provided as an open dataset for that measure (separate from this dataset). The performance measures with indicators from the survey include the following (as of 2022):1. Safe and Secure Communities1.04 Fire Services Satisfaction1.06 Crime Reporting1.07 Police Services Satisfaction1.09 Victim of Crime1.10 Worry About Being a Victim1.11 Feeling Safe in City Facilities1.23 Feeling of Safety in Parks2. Strong Community Connections2.02 Customer Service Satisfaction2.04 City Website Satisfaction2.05 Online Services Satisfaction Rate2.15 Feeling Invited to Participate in City Decisions2.21 Satisfaction with Availability of City Information3. Quality of Life3.16 City Recreation, Arts, and Cultural Centers3.17 Community Services Programs3.19 Value of Special Events3.23 Right of Way Landscape Maintenance3.36 Quality of City Services4. Sustainable Growth & DevelopmentNo Performance Measures in this category presently relate directly to the Community Survey5. Financial Stability & VitalityNo Performance Measures in this category presently relate directly to the Community SurveyMethodsThe survey is mailed to a random sample of households in the City of Tempe. Follow up emails and texts are also sent to encourage participation. A link to the survey is provided with each communication. To prevent people who do not live in Tempe or who were not selected as part of the random sample from completing the survey, everyone who completed the survey was required to provide their address. These addresses were then matched to those used for the random representative sample. If the respondent’s address did not match, the response was not used. To better understand how services are being delivered across the city, individual results were mapped to determine overall distribution across the city. Additionally, demographic data were used to monitor the distribution of responses to ensure the responding population of each survey is representative of city population. Processing and LimitationsThe location data in this dataset is generalized to the block level to protect privacy. This means that only the first two digits of an address are used to map the location. When they data are shared with the city only the latitude/longitude of the block level address points are provided. This results in points that overlap. In order to better visualize the data, overlapping points were randomly dispersed to remove overlap. The result of these two adjustments ensure that they are not related to a specific address, but are still close enough to allow insights about service delivery in different areas of the city. This data is the weighted data provided by the ETC Institute, which is used in the final published PDF report.The 2022 Annual Community Survey report is available on data.tempe.gov. The individual survey questions as well as the definition of the response scale (for example, 1 means “very dissatisfied” and 5 means “very satisfied”) are provided in the data dictionary.Additional InformationSource: Community Attitude SurveyContact (author): Wydale HolmesContact E-Mail (author): wydale_holmes@tempe.govContact (maintainer): Wydale HolmesContact E-Mail (maintainer): wydale_holmes@tempe.govData Source Type: Excel tablePreparation Method: Data received from vendor after report is completedPublish Frequency: AnnualPublish Method: ManualData Dictionary

This is the metadata for a clinical dataset entitled the The ARTERIAL US Study (A pReTERm Infants’ cArdiovascular deveLopment: An Ultrasound Study). We collected cardiovascular ultrasound data on the geometry, heart size, blood vessel diameters) and function (Doppler flow waveforms) of term and preterm hearts and vasculature. Study design: The ARTERIAL US Study is a single-centre prospective observational cohort study. Study synopsis Participants: 1. Term group: babies born at or after 37+0 weeks gestation 2. Late preterm group: babies born at or after 34+0 and before 37+0 weeks gestation Primary Outcome(s): Haemodynamic status as computed by the computational model of the neonatal cardiovascular system Sample Size: 15 term and 10 late preterm Study Setting: Auckland City Hospital, Te Toka Tumai Auckland (formerly Auckland District Health Board) Eligibility criteria Inclusion criteria: Born at or after at or after 37+0 weeks gestation (term group) or born at or after 34+0 and before 37+0 weeks gestation (late preterm group), Parental consent Exclusion criteria: Known medical conditions or cardiovascular abnormalities. Data collection Methods: Babies will have an ultrasound examination within 48 hours of birth and again three to six weeks later weeks later (i.e., at term equivalent postmenstrual age). Data collection included clinical data collection (data from the medical records about the following clinical factors: antenatal admission to hospital, gestational diabetes mellitus, antenatal infection, placental:fetal weight ratio, exposure to antenatal corticosteroids and magnesium sulphate, risk factors and primary reason for preterm birth (including pre-eclampsia, chorioamnionitis and fetal growth restriction), age at scan, sex, gestational age at birth, birth weight and length, head circumference at birth, APGARs, delayed cord clamping, postnatal steroid administration), anthropometric measurements and ultrasound measurements. Data availability Data and associated documentation from participants who have consented to future re-use of their data are available to other users under the data sharing arrangements provided by the University of Auckland’s Human Health Research Services (HHRS) platform (https://research-hub.auckland.ac.nz/subhub/human-health-research-services-platform). The data dictionary and metadata are published on the here. Researchers are able to use this information and the provided contact address (dataservices@auckland.ac.nz) to request a de-identified dataset through the HHRS Data Access Committee. Data will be shared with researchers who provide a methodologically sound proposal and have appropriate ethical approval, where necessary, to achieve the research aims in the approved proposal. Data requestors are required to sign a Data Access Agreement that includes a commitment to using the data only for the specified proposal, not to attempt to identify any individual participant, a commitment to secure storage and use of the data, and to destroy or return the data after completion of the project. The HHRS platform reserves the right to charge a fee to cover the costs of making data available, if needed, for data requests that require additional work to prepare.

Since 2013, the Dutch Migration Chain has had a chain-wide data dictionary, the Data Dictionary Migration Chain (GMK). The Migration Chain consists of the following organisations: - Central Agency for the Reception of Asylum Seekers - Correctional Institutions Agency, Ministry of Justice and Security - Repatriation and Departure Service, Ministry of Justice and Security - Directorate-General for Migration, Ministry of Justice and Security - Immigration and Naturalization Service , Ministry of Justice and Security - International Organization for Migration - Royal Netherlands Marechaussee - Ministry of Foreign Affairs - National Police - Council of State - Council for the Judiciary - Netherlands Council for Refugees - Seaport Police. One of the principles in the basic starting architecture of the migration chain is that there is no difference of opinion about the meaning of the information that can be extracted from an integrated customer view. A uniform conceptual framework goes further than a glossary of the most important concepts: each shared data can be related to a concept in the conceptual framework; in the description of the concepts, the relations to each other are named. Chain parties have aligned their own conceptual frameworks with the uniform conceptual framework in the migration chain. The GMK is an overview of the common terminology used within the migration chain. This promotes a correct interpretation of the information exchanged within or reported on the processes of the migration chain. A correct interpretation of information prevents miscommunication, mistakes and errors. For users in the migration chain, the GMK is available on the non-public Rijksweb (gmk.vk.rijksweb.nl). In the context of openness and transparency, it has been decided to make the description of concepts and management information from the GMK accessible as open data. This means that the data via Data.overheid.nl is available and reusable for everyone. By making the data transparent, the ministry also hopes that publications by and about the work in the migration chain, for example the State of Migration, are easier to explain and provide context.

The 1940 Census Public Use Microdata Sample Project was assembled through a collaborative effort between the United States Bureau of the Census and the Center for Demography and Ecology at the University of Wisconsin. The collection contains a stratified 1-percent sample of households, with separate records for each household, for each 'sample line' respondent, and for each person in the household. These records were encoded from microfilm copies of original handwritten enumeration schedules from the 1940 Census of Population. Geographic identification of the location of the sampled households includes Census regions and divisions, states (except Alaska and Hawaii), standard metropolitan areas (SMAs), and state economic areas (SEAs). Accompanying the data collection is a codebook that includes an abstract, descriptions of sample design, processing procedures and file structure, a data dictionary (record layout), category code lists, and a glossary. Also included is a procedural history of the 1940 Census. Each of the 20 subsamples contains three record types: household, sample line, and person. Household variables describe the location and condition of the household. The sample line records contain variables describing demographic characteristics such as nativity, marital status, number of children, veteran status, wage deductions for Social Security, and occupation. Person records also contain variables describing demographic characteristics including nativity, marital status, family membership, education, employment status, income, and occupation.

Abstract: How can we measure and explain the precision of international organizations’ (IOs) founding treaties? We define precision by its negative – imprecision – as indeterminate language that intentionally leaves a wide margin of interpretation for actors after agreements enter into force. Compiling a “dictionary of imprecision” from almost 500 scholarly contributions and leveraging insight from linguists that a single vague word renders the whole sentence vague, we introduce a dictionary-based measure of imprecision (DIMI) that is replicable, applicable to all written documents, and yields a continuous measure bound between zero and one. To demonstrate that DIMI usefully complements existing approaches and advances the study of (im-)precision, we apply it to a sample of 76 IOs. Our descriptive results show high face validity and closely track previous characterizations of these IOs. Finally, we explore patterns in the data, expecting that imprecision in IO treaties increases with the number of states, power asymmetries, and the delegation of authority, while it decreases with the pooling of authority. In a sample of major IOs, we find robust empirical support for the power asymmetries and delegation propositions. Overall, DIMI provides exciting new avenues to study precision in International Relations and beyond. The files uploaded entail the material necessary to replicate the results from the article and Online appendix published in: Gastinger, M. and Schmidtke, H. (2022) ‘Measuring precision precisely: A dictionary-based measure of imprecision’, The Review of International Organizations, available at Doi: 10.1007/s11558-022-09476-y. Please let us know if you spot any mistakes or if we may be of any further assistance!

Vulnerability to anxiety and depressive disorders is affected by risk and resilience factors, such as personality, use of emotion regulation strategies, and affective cognition. Previous research has identified personality constructs best explaining variance in anxiety and depression (Lyon et al, 2020; 2021), however the mediating mechanisms are unknown. This study aimed to investigate the mediating roles of emotion regulation strategies and affective cognition in the relationship between personality constructs and affective disorders. Data were collected from a sample of 276 students and staff at the University of Manchester. Measures included both broad and narrow Big Five personality constructs; COPE Inventory strategies; a dot-probe task to measure attentional biases to emotional information; both a questionnaire and a computerised cognitive to measure interpretation of emotional images; and measures of anxiety and depression.

This data collection is a component of Summary Tape File (STF) 3, which consists of four sets of computer-readable data file containing detailed tabulations of the nation's population and housing characteristics produced from the 1980 Census. The STF 3 files contain sample data inflated to represent the total United States population. The files also contain 100-percent counts and unweighted sample counts of persons and housing units. All files in the STF 3 series are identical, containing 321 substantive data variables organized in the form of 150 "tables," as well as standard geographic identification variables. Population items tabulated for each person include demographic data and information on schooling, ethnicity, labor force status, and number of children, as well as details on occupation and income. Housing items include size and condition of the housing unit as well as information on value, age, water, sewage and heating, vehicles, and monthly owner costs. Each dataset provides different geographic coverage. STF 3D provides summaries for state or state equivalent, congressional district (as constituted for the 98th Congress), county or county equivalent, places of 10,000 or more people, and minor civil division/census county division. There are 51 separate files, one for each state and the District of Columbia. The Census Bureau's machine-readable data dictionary for STF 3 is also available through CENSUS OF POPULATION AND HOUSING, 1980 [UNITED STATES]: CENSUS SOFTWARE PACKAGE (CENSPAC) VERSION 3.2 WITH STF4 DATA DICTIONARIES (ICPSR 7789), the software package designed specifically by the Census Bureau for use with the 1980 Census data files.