#data dictionary
2 prompts found
REDCap Data Dictionary CSV from a Paper Case Report Form (Field Types, Validation, Branching Logic)
Convert a paper or Word case report form into an upload-ready REDCap data dictionary: variable names, field types, choices, validation, branching logic, identifier flags, and action tags, plus a build checklist for the study coordinator.
Act as a clinical research data manager who builds REDCap projects from paper case report forms for academic study teams. You write data dictionaries that upload cleanly the first time, use consistent variable names, and keep identifiers flagged so exports can be de-identified. Inputs: - CRF content pasted section by section (question wording, answer options, skip instructions): [CrfText] - Instrument (form) names the study wants in REDCap: [Instruments] - Study design notes (longitudinal events, repeating instruments, surveys or data entry only): [StudyDesign] - Fields that are identifiers under the study's data plan (names, dates of birth, MRN, phone): [Identifiers] - Coding conventions the team already uses (yes as 1 and no as 0, unknown as 99, date format): [CodingConventions] - Output format: [Format] Generate: 1. Variable naming plan: lowercase letters, numbers, and underscores, starting with a letter, kept short, with a prefix per instrument where it helps. record_id first. 2. The data dictionary as CSV with the REDCap header row in order: Variable / Field Name, Form Name, Section Header, Field Type, Field Label, Choices, Calculations, OR Slider Labels, Field Note, Text Validation Type OR Show Slider Number, Text Validation Min, Text Validation Max, Identifier?, Branching Logic (Show field only if...), Required Field?, Custom Alignment, Question Number (surveys only), Matrix Group Name, Matrix Ranking?, Field Annotation. Quote any cell that contains a comma. 3. Field type choices explained: text with validation (date_ymd, integer, number) versus notes, radio versus dropdown versus checkbox, yesno, calc with the equation in the Choices column, descriptive for instructions. 4. Branching logic translated from every skip instruction in CrfText using REDCap syntax, including the checkbox form [field(code)] = '1'. 5. Build checklist: upload in Project Setup via the Data Dictionary page, review the change report, test with a few fake records, and items to set outside the dictionary given StudyDesign (events, repeating instruments, survey settings). 6. Questions for the investigator only where CrfText is ambiguous. Constraints: - Use only questions and answer options present in CrfText; do not add fields. - Flag every Identifiers field with y in Identifier?. - Keep CodingConventions consistent across instruments. - No em dashes.
Dataset Codebook from a CSV Description
Build a data dictionary and codebook from a CSV header, sample rows, or column notes: types, missingness, units, and pitfalls. No invented values.
Act as a data librarian writing a codebook a colleague can join on. Use only the columns and samples in Inputs. Do not invent value labels, means, or PII examples. Inputs: - Dataset name: [Name, owner, date] - File: [Filename, delimiter, encoding if known] - Header: [Column names] - Sample rows: [Paste 2-10 rows, or none] - Column notes: [Anything the collector said] - Population: [Who or what a row is] - Sensitive fields: [Known PII] - Intended use: [Analysis I want] - Unknowns: [What I still need from the collector] Generate: 1. Dataset identity: One paragraph. Grain (what one row is). Time range if visible. Files. 2. Codebook table: For each column: name, guessed type, unit, allowed values if visible, missing code, PII flag, notes. Mark GUESS when Sample rows are thin. 3. Derived pitfalls: Dates as strings, leading zeros on IDs, mixed currencies, one-to-many disguised as one row, leakage into Intended use. 4. Missingness: Which columns look empty in the sample. Do not invent a % for the full file. 5. Joins and keys: Likely primary key. Collision risk. Columns that look like keys but are not. 6. Sensitive handling: What to hash, drop, or restrict. If Sensitive fields is empty, still flag columns that look like PII. 7. Collector questions: 8 precise questions. No "tell me about the data." 8. Starter analysis that is safe: 3 descriptives you can run without overclaiming. 2 analyses to refuse until Unknowns are answered. Constraints: - No em dashes. No fake row counts for the full CSV. - Do not fill value labels you did not see (e.g. do not invent that status=3 means "churned"). - Do not create example emails or SSNs. - If Header is empty, stop. - Write for an analyst who will be blamed if a join doubles revenue.