--- name: uk-biobank description: Search UK Biobank's openly published field catalogue — 11,821 variables in 410 categories with participant counts, units, the instanced/arrayed shape that decides how many columns a field becomes, and the 172 retired fields the website hides but the download still carries — and report what an access application requires, applications being paused as of August 2026. Never fetches participant data. category: data license: CC-BY-4.0 author: Heureka Labs version: 1.1.0 tags: [uk-biobank, cohort, epidemiology, controlled-access, public-data] covers: [uk biobank, biobank, cohort, nmr metabolomics, plasma proteomics, olink, ukb-ppp, gwas, imaging, brain mri, cardiac mri, abdominal mri, liver fat, dxa, accelerometer, spirometry, blood biochemistry, whole exome sequencing, whole genome sequencing, polygenic risk score, icd10, hospital inpatient records, cancer registry, death registry, type 2 diabetes, dementia, coronary artery disease, breast cancer, aging, data dictionary] papers: [PMID:25826379, PMID:30305743, PMID:32457287, PMID:34662886, PMID:36737450, PMID:37794186, PMID:38057571] access: [open, controlled] datasets: [https://biobank.ndph.ox.ac.uk/showcase/scdown.cgi?fmt=txt&id=1] allowed-tools: Read, Write, Edit, Bash verified: date: 2026-08-18 against: Showcase schema 1 (field catalogue) max version 2025-09-20, 11,821 fields, 4,164,390 bytes / schema 3 (categories) 410 rows / schema 9 (instancing) 12 rows / coding 143 (Olink assays) 2,923 rows / shape arithmetic checked against the Showcase's own declared Instances, Array, Participants and Item count on 19 fields (23400 41270 30069 40000 40006 4079 6138 3166 30098 189 22201 33 1020204 2020204 30900 41001 21015 4229 5990) plus absent id 99999999 / retired-vs-listed checked against label.cgi on all 22 categories holding a retired field, by parsing the listing table (a field.cgi link scrape fails on categories 149 and 150) / Access Procedures v2.1 July 2022 PDF / fees and apply-for-access pages read 2026-08-18 in a browser, both Cloudflare-blocked to plain HTTP clients / Python 3.12.8, standard library only / curl 8.7.1 executed: 7 unverified: 0 --- # UK Biobank field catalogue UK Biobank followed ~500,000 UK adults recruited 2006–2010, and has kept adding to them: biochemistry, NMR metabolomics, Olink proteomics, exomes, genomes, imaging, accelerometry, and linked hospital, cancer and death records. **The whole data dictionary is an open download. No account, no application.** That answers the question researchers actually ask *before* applying — *is what I need even in there, and on how many people?* — in seconds rather than an afternoon of clicking a web interface. **This skill never touches participant data, and cannot get you any.** It reads the catalogue: field names, units, categories, participant counts, dates, and the structural metadata that says how a field is shaped. Individual-level values are controlled and require an approved application, a signed material transfer agreement, and a fee — see *Requesting access*, which describes that route and does not promise it. ## Read this before you plan a study Two facts settle most triage questions, and both cut the opposite way from the usual controlled-access resource: - **Consent is broad, not disease-specific.** The Access Procedures state the resource is available to all bona fide researchers "for all types of health-related research that is in the public interest", with no preferential or exclusive access. There is no per-study consent restriction to check the way there is on a disease-specific cohort. What binds instead is *health-related* and *public interest*. - **You do not need your own ethics approval for a data application.** UK Biobank is registered as a Research Tissue Bank and its clearance covers data and sample applications; the Access Procedures say separate REC or other ethical clearance "is not required nor needed". Re-contact applications are the exception and do need their own. So the binding constraint here is usually not consent. It is whether the measurement exists at a useful sample size — which is exactly what the catalogue answers for free. ## Downloading the catalogue One CGI endpoint serves every schema table as TSV. Schema 1 is the field catalogue. ```bash curl -sS "https://biobank.ndph.ox.ac.uk/showcase/scdown.cgi?fmt=txt&id=1" -o primary.tsv curl -sS "https://biobank.ctsu.ox.ac.uk/crystal/scdown.cgi?fmt=txt&id=1" -o mirror.tsv wc -c primary.tsv mirror.tsv shasum -a 256 primary.tsv mirror.tsv cut -f1,2,13,24 primary.tsv | head -3 ``` Printed 2026-08-18: ``` 4164390 primary.tsv 4164390 mirror.tsv 8328780 total 2d7b5f394457002a5a41e278074d64c3b56811f4c50fd916599145aa10210cc6 primary.tsv 2d7b5f394457002a5a41e278074d64c3b56811f4c50fd916599145aa10210cc6 mirror.tsv field_id title units num_participants 3 Verbal interview duration seconds 501109 4 Biometrics duration seconds 497769 ``` Same bytes, same digest, so the `crystal` mirror is a drop-in fallback when the primary is down. A third path, `biobank.ndph.ox.ac.uk/ukb/scdown.cgi`, served the same file. **`scdown.cgi` answers HTTP 200 with an HTML error page** when it does not recognise the table id — `Content-Type: text/html`, body "Sorry, internal error prevents download of schema". Fed to a TSV parser that trusts the status code, it yields ~40 rows whose sole column is named ``, with no exception raised. `codown.cgi` behaves the same way. Every loader below checks the first bytes rather than the status. The tables worth knowing, all at `scdown.cgi?fmt=txt&id=`: | id | table | rows on 2026-08-18 | why you want it | |---|---|---|---| | 1 | data field properties | 11,821 | the catalogue itself | | 3 | categories | 410 | `main_category` is an integer — this names it | | 9 | instancing dictionaries | 12 | what an instance index *means* for a field | | 2 | encoding dictionaries | 858 | which coding a categorical field uses | | 13 | category browse tree | 362 | parent/child, for walking a category subtree | Individual codings come from a different script, `codown.cgi?id=`, and the full index of schema tables is listed at `showcase/schema.cgi`. ## The 29 columns, and the code tables Schema 1's header is: ``` field_id title availability stability private value_type base_type item_type strata instanced arrayed sexed units main_category encoding_id instance_id instance_min instance_max array_min array_max notes debut version num_participants item_count showcase_order cost_do cost_on cost_sc ``` Almost everything interesting is an integer code. The mappings **are** published, but only as prose on `https://biobank.ndph.ox.ac.uk/showcase/schema.cgi?id=1` — there is no machine-readable download of them, so hardcode the table and re-read that page when a code you have never seen appears: | column | codes | |---|---| | `availability` | 0 full · 1 view-only · 4 retired | | `stability` | 0 complete · 1 updateable · 2 accruing · 3 variable | | `private` | 0 public · 1 restricted | | `value_type` | 11 integer · 21 categorical single · 22 categorical multiple · 31 continuous · 41 text · 51 date · 61 time · 101 compound · 201 blob | | `base_type` | 0 see value type · 11 integer · 31 real · 41 string · 51 date | | `item_type` | 0 data · 10 sample · 20 bulk · 30 record | | `strata` | 0 primary · 2 auxiliary · 3 derived | | `instanced` | 0 no · 1 defined · 2 variable | | `arrayed` | 0 no · 1 yes | | `sexed` | 0 unisex · 1 male · 2 female | Three of those need more than a lookup: **`value_type` is the shape of one value.** On the 2026-08-18 snapshot: 4,951 continuous, 3,698 categorical-single, 1,345 date, 1,008 integer, 533 text, 171 categorical-multiple, 85 time, 30 compound. No `201 blob` field existed, though the code is documented. **`base_type` is only meaningful for categorical fields**, which the data proves rather than the documentation. Cross-tabulating the two columns, `base_type` is `0` for every one of the 7,952 non-categorical fields, and non-zero for exactly the 3,869 categorical ones — 3,818 coded as integers and 51 as strings (single choice splits 3,664 integer / 34 string, multiple choice 154 / 17). So for a categorical field, `base_type` tells you whether the codes behind the labels are integers or strings, which is what you need to join against the coding in schema 2. For anything else it carries no information. **`item_type` tells you whether the field is even a column in your extract**, and this is the one that quietly breaks analysis plans. `0 data` (11,271 fields) lands in the tabular dataset. `20 bulk` (424) is a separate per-participant file download — DICOMs, VCFs, accelerometer records. `30 record` (112) lives in a Data Portal record table, not in the tabular extract at all. `10 sample` (14) refers to physical material. A plan that assumes every field is a column will mis-size 550 of them. The last three columns are fee tiers, and only one of them is populated. `cost_do` is `0` for all 11,821 fields. `cost_on` — the tier for analysis on the Research Analysis Platform — is `1` for 11,266 fields, `2` for 413, `3` for 141, and `0` for exactly one, field `33` Date of birth. The showcase page renders those as `o1`/`o2`/`o3` and the lone zero as `oX`, so treat `0` as *no tier stated*, not as tier zero — mapping it to an integer tier is how a one-field hole becomes a wrong fee. The rest tracks the published three-tier fee structure: the 141 tier-3 fields are exome and whole-genome sequencing and their QC metrics, and tier 2 is dominated by imaging-derived measures, polygenic risk scores and cardiac monitoring. So the open catalogue tells you which fee tier your shortlist sits in before you ask. `cost_sc` renders as `s` (and as `sX` when it is `0`), and **its definition is not published in the schema documentation** — that page still documents a single `tier` column where the file now has three. Do not infer a meaning for `cost_sc`; report the raw value. ## Searching and grouping — the triage layer Match on title *and* category name, group by category, lead with the participant count, and **carry `availability`**. Grouping matters because a title search alone scatters related fields, and the category is often where the useful name lives. Carrying availability matters for the reason in the next section: the download contains 172 retired fields that the Showcase website does not show you. ```python import csv, io, re, urllib.request from collections import defaultdict SHOWCASE = "https://biobank.ndph.ox.ac.uk/showcase" def load(sid, encoding): raw = urllib.request.urlopen(f"{SHOWCASE}/scdown.cgi?fmt=txt&id={sid}", timeout=300).read() # scdown.cgi answers HTTP 200 with an HTML error page for an unknown table id. if raw.lstrip()[:9].lower().startswith(b" title cat_name = {c["category_id"]: c["title"] for c in cats} MARK = {"4": " RETIRED", "1": " view-only"} # availability; "0" is full def triage(query, min_n=0): """Fields whose title or category matches `query`, grouped by category, reporting the participant count that decides whether the study is powered — and whether the field is still live. Retired fields stay in the download but vanish from the Showcase website.""" pat = re.compile(query, re.I) groups = defaultdict(list) for f in fields: cname = cat_name.get(f["main_category"], f"[{f['main_category']} unknown]") if pat.search(f["title"]) or pat.search(cname): n = int(f["num_participants"]) if n >= min_n: # -1 and 0 fall out at min_n=1 groups[(f["main_category"], cname)].append( (f["field_id"], f["title"], n, f["availability"])) print(f"query {query!r} min_n={min_n}") for (cid, cname), hits in sorted(groups.items(), key=lambda kv: -max(h[2] for h in kv[1])): ns = [h[2] for h in hits] dead = sum(1 for h in hits if h[3] == "4") print(f" cat {cid:>6} {cname[:38]:38} {len(hits):>4} fields " f"n {min(ns):,}-{max(ns):,}" + (f" [{dead} retired]" if dead else "")) for fid, title, n, avail in sorted(hits, key=lambda h: -h[2])[:3]: print(f" {fid:>8} {title[:44]:44} n={n:,}{MARK.get(avail, '')}") triage(r"\bliver\b", min_n=10000) print() triage(r"liver iron|townsend") ``` Printed 2026-08-18: ``` query '\\bliver\\b' min_n=10000 cat 1039 Food (and other) preferences 1 fields n 181,941-181,941 20686 Liking for liver n=181,941 cat 126 Liver MRI 7 fields n 10,094-94,790 20204 Liver Imaging - T1 ShMoLLI - DICOM n=94,790 20254 Liver imaging - IDEAL protocol - DICOM n=86,699 40063 Acquisition protocol n=41,641 cat 149 Abdominal composition 1 fields n 47,036-47,036 24352 FR liver PDFF mean n=47,036 cat 158 Abdominal organ composition 5 fields n 10,069-38,967 21080 Liver volume n=38,967 21088 Liver PDFF (fat fraction) n=30,736 21089 Liver iron n=30,736 cat 2411 Digestive system disorders 2 fields n 17,707-17,707 131670 Date K76 first reported (other diseases of l n=17,707 131671 Source of report of K76 (other diseases of l n=17,707 cat 1000 [1000 unknown] 2 fields n 15,112-15,116 1020254 EMBARGOED UNLINKED : Liver imaging - IDEAL p n=15,116 1020204 EMBARGOED UNLINKED : Liver Imaging - T1 ShMo n=15,112 query 'liver iron|townsend' min_n=0 cat 100094 Baseline characteristics 2 fields n 501,315-501,315 [1 retired] 189 Townsend deprivation index at recruitment n=501,315 RETIRED 22189 Townsend deprivation index at recruitment n=501,315 cat 126 Liver MRI 4 fields n 1,110-40,354 [2 retired] 40060 Liver iron (Fe) n=40,354 40062 Liver iron corrected T1 (ct1) n=34,291 22417 Liver iron corrected T1 (ct1) n=2,812 RETIRED cat 158 Abdominal organ composition 2 fields n 10,069-30,736 21089 Liver iron n=30,736 21093 Liver iron (Fe) - gradient echo n=10,069 ``` The first output is the whole point of the skill, and it also shows why you cannot rank by `num_participants` alone: the largest hit is a food-preference questionnaire item. The real answer — quantitative liver fat on ~30,700 people, liver volume on ~39,000, raw imaging on ~95,000 — is three lines down. Read the titles. The `[1000 unknown]` group is the orphan-category trap arriving unannounced: a `main_category` that schema 3 does not define, so an inner join would have deleted those two rows silently. All 126 fields in it are bulk items marked `private = 1`, so their presence in a hit list is not the same as their being available. They split exactly in half — **63 titled `EMBARGOED UNLINKED :` and 63 `EMBARGOED LINKED :`** — and that word is the whole question for an imaging file, because it says whether the file can be joined to a participant at all. Do not read the prefix off one of them and generalise. ## Retired fields are in the download and not on the website This is the trap that survives a careless search, because nothing in the field's own numbers looks wrong. `availability = 4` marks 172 fields as **retired**. They are still in schema 1, with full titles, full participant counts and a live `field.cgi` page that says "Field is currently retired" — but **the Showcase's category pages do not list them at all**. So a shortlist built from the download does not match the website, and the difference is silent. The second `triage` output above is the counter-example. `189` and `22189` are both titled `Townsend deprivation index at recruitment`, both report n=501,315, and `189` is retired. Nothing but `availability` separates them. Eleven retired fields carry the exact title of a live replacement, and three of those differ by an order of magnitude in n: | retired | n | current | n | title | |---|---|---|---|---| | `22400` | 1,110 | `40060` | 40,354 | Liver iron (Fe) | | `22402` | 4,609 | `40061` | 40,746 | Proton density fat fraction (PDFF) | | `22417` | 2,812 | `40062` | 34,291 | Liver iron corrected T1 (ct1) | | `189` | 501,315 | `22189` | 501,315 | Townsend deprivation index at recruitment | Whole categories can be dead. Category `2000` "Hospital inpatient" holds 36 fields in schema 1, with `num_participants` up to 413,163 — and **all 36 are retired, so its Showcase page lists none of them**. A query on `hospital inpatient|episodes containing` returns 86 fields of which 86 are retired. Categories `2002`–`2005` are largely the same story: the summary-diagnosis and summary-operation fields were superseded by the first-occurrence and record-level tables. The ground truth is one HTTP request away, and it is worth making once for any category you plan to build on: `label.cgi?id=` lists the live fields, so `fields in schema 1 − retired == fields the page lists`. It holds on all 22 categories that contain a retired field — **but only if you parse the listing table** (``). Scraping every `field.cgi?id=` on the page picks up cross-references out of the category's own prose and fails on two of the 22: category `149` reports 29 against a true 28, and `150` reports 7 against 5, because its description names two retired fields in a sentence about them being retired. Category `126` Liver MRI is the compact case — 11 fields in the download, 7 on the page, difference exactly the 4 retired ones. Note how easily this hides. The `\bliver\b` search above uses `min_n=10000`, and all four retired Liver MRI fields have n below 10,000, so they never appear. A threshold chosen for a good reason silently suppressed the evidence. Two more columns are easy to skip for the same kind of reason. `private = 1` (319 fields, including `33` Date of birth and parents' months of birth) marks fields carrying disclosure risk, not fields you can simply request. `availability = 1` (54 fields, including per-chromosome genotype probabilities) is *view-only* — documented on the showcase, not handed out. Filter on all three before you build a shortlist. Two habits that make this reliable. Search the **category name** as well as the title, because UK Biobank names the assay at the category level and the analyte at the field level. And set `min_n` deliberately: the imaging sub-cohort is tens of thousands, not half a million, so a `min_n` tuned to the full cohort silently hides every imaging field. ## `instanced` and `arrayed` — the part that trips people These two columns are why UK Biobank fields are hard to reason about, and they are independent of each other. **`instanced`** means the field was captured more than once per participant, indexed by an *instance*. The index does **not** universally mean "visit": - `instanced = 0` (4,605 fields) — captured once, almost always at instance `0`. Two exceptions carry `instance_min = instance_max = 3`: `41000` and `41001`, the COVID-19 re-imaging fields. Read the index off `instance_min`, not off the `instanced` flag — `field.cgi` calls both of them "Singular" and does not print an index at all. - `instanced = 1` (7,198 fields) — a *defined* instancing scheme. `instance_id` points into schema 9, which says what the instances are. `instance_id = 2` is the common one: initial assessment centre plus later repeat visits. - `instanced = 2` (18 fields) — *variable*. The index has no fixed meaning across participants. Field `40000` (Date of death) uses `instance_id 9000001`, "Death registry reports": instance 0 and 1 are two registry reports, not two visits. Treating a variable instance as a timepoint is a real analysis error, and it is only 18 fields, so it is easy to miss. **`arrayed`** means one participant can have many values *at the same instance* — a list, not a repeat measure. `arrayed = 1` on 550 fields. Total columns a field becomes in a tabular extract is the product, named `-.`: ```python import csv, io, urllib.request SHOWCASE = "https://biobank.ndph.ox.ac.uk/showcase" def load(sid, encoding): raw = urllib.request.urlopen(f"{SHOWCASE}/scdown.cgi?fmt=txt&id={sid}", timeout=300).read() if raw.lstrip()[:9].lower().startswith(b" {n_inst} instance(s) x {n_arr} array slot(s) = {n_inst * n_arr} columns") print(f" column names: {field_id}-{lo}.{alo} ... {field_id}-{hi}.{ahi}") if f["instanced"] != "0": print(f" instance {f['instance_id']} means: {inst.get(f['instance_id'], '?')[:96]}") if n > 0: print(f" participants {n:,} item_count {items:,}" f" -> {items / n:.2f} values per participant with data") else: print(f" participants {n} (sentinel — not published) item_count {items:,}") for fid in ["23400", "41270", "40000", "30069"]: shape(fid); print() ``` Printed 2026-08-18: ``` 23400 Total Cholesterol instanced=1 arrayed=0 -> 2 instance(s) x 1 array slot(s) = 2 columns column names: 23400-0.0 ... 23400-1.0 instance 2 means: All participants attended an initial assessment centre. A proportion were invited several years participants 488,513 item_count 507,961 -> 1.04 values per participant with data 41270 Diagnoses - ICD10 instanced=0 arrayed=1 -> 1 instance(s) x 259 array slot(s) = 259 columns column names: 41270-0.0 ... 41270-0.258 participants 448,651 item_count 7,276,575 -> 16.22 values per participant with data 40000 Date of death instanced=2 arrayed=0 -> 2 instance(s) x 1 array slot(s) = 2 columns column names: 40000-0.0 ... 40000-1.0 instance 9000001 means: Death registry reports participants 56,961 item_count 57,021 -> 1.00 values per participant with data 30069 Cell images instanced=2 arrayed=1 -> 16 instance(s) x 104 array slot(s) = 1664 columns column names: 30069-0.0 ... 30069-15.103 instance 693 means: Used to group together items related to the same case report. participants 1,124 item_count 19,074 -> 16.97 values per participant with data ``` Read those four together and the whole model falls out. `23400` is one measurement repeated at a second visit for a minority — 488,513 people, 507,961 values, so about 19,400 repeats. `41270` is not visit-indexed at all: a single instance slot holding up to 259 ICD-10 codes per person, 16 on average. `40000` is a *variable* instance, where the index is a registry report. `30069` combines instancing and arraying and becomes 1,664 columns for 1,124 participants — which is what a wide extract of an arrayed, instanced field looks like, and why you compute the column budget before requesting one. `item_count / num_participants` is the cheapest sanity check available: near 1.0 means one value per person, well above 1 means the field is genuinely multi-valued. Both columns carry `-1` on the same 270 fields and `0` on the same 12, so the ratio never silently divides by a fake count — but neither column can be summed. The independent check on all of this is the field's own showcase page, which declares the shape in its header table: `Instances` reads `Singular`, `Defined (N)` or `Variable (N)` and `Array` reads `No` or `Yes (N)`, alongside `Participants` and `Item count`. Those four were compared against this arithmetic on nineteen fields spanning every `value_type`, both `item_type 20` bulk and `item_type 30` record, `array_min = 1`, the registry-indexed instancing schemes and the retired and embargoed ends of the catalogue, and they agreed every time. One nuance the arithmetic cannot see: declared slots are not populated slots. `30900` declares `Defined (4)`, and its page then breaks out **3** instances, because nobody was measured at instance 1. Four is still the right column budget; three is the right number of timepoints. ## Resolving a field to its showcase page `field.cgi?id=` is the human-readable page. **It answers HTTP 200 for field ids that do not exist**, so status code is not a validity test — validate against the catalogue you already downloaded. ```python import csv, io, urllib.request SHOWCASE = "https://biobank.ndph.ox.ac.uk/showcase" raw = urllib.request.urlopen(f"{SHOWCASE}/scdown.cgi?fmt=txt&id=1", timeout=300).read() known = {f["field_id"]: f["title"] for f in csv.DictReader(io.StringIO(raw.decode("utf-8")), delimiter="\t", quoting=csv.QUOTE_NONE)} def resolve(field_id): """field.cgi answers HTTP 200 for ids that do not exist, so validate locally.""" field_id = str(field_id) if field_id not in known: return None, f"field {field_id} is not in the catalogue" return f"{SHOWCASE}/field.cgi?id={field_id}", known[field_id] for fid in ["23400", "30900", "41270", "99999999"]: url, note = resolve(fid) print(f"{fid:>9} {note[:44]:44} {url or ''}") for fid in ["23400", "99999999"]: body = urllib.request.urlopen(f"{SHOWCASE}/field.cgi?id={fid}", timeout=60).read().decode( "utf-8", "replace") print(f" field.cgi?id={fid:<9} HTTP 200 " f"'Field is not in database' present: {'Field is not in database' in body}") ``` Printed 2026-08-18: ``` 23400 Total Cholesterol https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=23400 30900 Number of proteins measured https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=30900 41270 Diagnoses - ICD10 https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=41270 99999999 field 99999999 is not in the catalogue field.cgi?id=23400 HTTP 200 'Field is not in database' present: False field.cgi?id=99999999 HTTP 200 'Field is not in database' present: True ``` Nor is the body string a test. `field.cgi?id=99999999` does carry "Field is not in database", but `field.cgi?id=0` and `field.cgi?id=abc` answer 200 *without* it. The local membership check is the only reliable one, which is why it comes first. Categories resolve the same way at `label.cgi?id=` — and that page is also the ground truth for which fields in a category are still live, since it omits retired ones. Field and category notes contain the showcase's own cross-reference markup: `~F30900~` a field, `~C143~` a **coding** (not a category), `~L100116~` a label, `~R4654~` a resource, `~P1874~` a publication, `~Ehttps://…~` an external link. `C` meaning coding rather than category is a genuine ambiguity — coding 143 is the Olink assay list, while *category* 143 is "Cannabis use". ## Traps confirmed on the live file - **`num_participants` is a sentinel column, not a count.** 270 fields carry `-1` and 12 carry `0` on the 2026-08-18 snapshot. `-1` means the count is not published — the showcase page renders those fields' participant count as the word `pending`, so it is not "nobody". `item_count` carries `-1` on exactly the same 270 rows. Summing or averaging either raw column silently subtracts. - **172 fields are retired and the website does not show them.** `availability = 4`. They stay in the download with full counts; `label.cgi` omits them. Eleven of them carry the exact title of the live field that replaced them. See *Retired fields are in the download and not on the website* above — this is the single easiest way to size a study on a dead field id. - **The download endpoints answer HTTP 200 with an HTML error page.** Both `scdown.cgi?id=` and `codown.cgi?id=` return status 200 and `Content-Type: text/html`. A TSV parser accepts the HTML without raising and hands back rows whose first column is named ``. Check the leading bytes, not the status. - **The two files disagree on text encoding.** Schema 1 is valid UTF-8. Schema 3 (categories) is **cp1252** — decoding it as UTF-8 raises `UnicodeDecodeError` on byte `0x97` at offset 152,914, an em dash inside a category description. Decode each table with the encoding that table actually uses. - **126 fields reference a category that does not exist.** Every field's `main_category` resolves against schema 3 except `1000`, which is absent from the category table entirely. All 126 are `item_type 20` bulk items with `private = 1`, and they split 63 `EMBARGOED UNLINKED : …` / 63 `EMBARGOED LINKED : …` — the two prefixes are field-id twins (`1020204` / `2020204`), so reading one and generalising gets the linkage backwards for half of them. An inner join on category drops all 126 without a word; use an outer join and label the orphan. - **`availability = 7` is undocumented.** Three fields carry it (`12652`, `12663`, `12704`, all brain-MRI "reason not performed" fields) and the schema page documents only 0, 1 and 4. Pass unknown codes through as raw values rather than mapping them to a guess. - **`codown.cgi` output has no trailing newline**, while `scdown.cgi` output does. So `wc -l` undercounts a coding file by one row. Use `grep -c ''`, or parse it. - **Titles and notes contain literal double-quote characters** — 8,756 across schema 1, 200 of them in titles, spread over 4,083 rows. On this snapshot they are all mid-field, so a default CSV parse and `quoting=QUOTE_NONE` give byte-identical results (checked with both `csv` and pandas 2.3.2). Pass `QUOTE_NONE` anyway; it costs nothing and removes the failure mode entirely. - **The documented column list is stale.** `schema.cgi?id=1` ends its list with `showcase_order | tier`; the file ends with `showcase_order cost_do cost_on cost_sc`. Read the header row, never the documentation, to order columns. - **What the catalogue does not contain**: any participant value, any per-field missingness pattern, which participants have data, or the distribution of a variable. `num_participants` is a headcount of people with at least one non-missing value across all instances — not the analysable n for a specific visit, and not a complete-case count for a model with covariates. Treat it as an upper bound. ## Two worked answers Both of these are aging-project shaped questions, and the catalogue answers them in opposite ways. **NMR metabolomics — three categories, 512 fields, ~488,500 people.** | category | title | fields | max `num_participants` | |---|---|---|---| | 220 | NMR metabolomics | 251 | 488,514 | | 221 | NMR metabolomics QC indicators | 249 | 412,098 | | 222 | NMR metabolomics processing | 12 | 488,521 | Category 220 is the biomarker panel itself — field ids 20280–20281 and 23400–23648, all `value_type 31` continuous, `instanced=1` with instances 0–1 (baseline and repeat), and units 163 `mmol/l`, 77 `percent`, 4 `ratio`, 3 `nm`, 3 `g/l`, 1 `degree`. Representative ids: `23400` Total Cholesterol (n=488,513), `23407` Total Triglycerides (488,513), `23444` Omega-3 Fatty Acids (487,913), `23470` Glucose (487,597), `23478` Creatinine (479,247), `23480` Glycoprotein Acetyls (488,514). Note the number. The published NMR biomarker atlas is titled for 118,461 individuals; the catalogue on 2026-08-18 reports ~488,500 for those same fields, roughly four times as many. **The catalogue is the current answer and a paper is a historical one** — which is exactly why you read `num_participants` rather than inheriting an n from the literature. Category 221 is the paired QC layer — all 249 titles end `, QC Flag`, all are `value_type 21` categorical, and 76 of them carry `num_participants = -1`. Budget for them: a per-biomarker QC flag is how you exclude a bad spectrum, and it is a second column per analyte. **Olink proteomics — five fields, and not one protein among them.** Category 1838 "Proteomics" has a single child, category 1839 "Protein biomarkers", containing exactly five fields: `30900` Number of proteins measured (n=53,039), `30901` Plate used for sample run (53,039), `30902` Well used for sample run (53,039), `30903` UKB-PPP Consortium selected participant (6,230), `30904` Number of blind-spike duplicates (1,318). Searching the catalogue for a protein name finds nothing, and that is not a search failure. `30900` has `item_type = 30` — *record* — meaning the normalised protein expression values live in a Data Portal record table (`olink_data`), one row per participant-instance-protein, **not** as thousands of tabular fields. Selecting `30900` is what grants access to that table. Any plan that budgets "one column per protein" is built on a shape that does not exist. The assay list is open, though, which answers "is my protein on the panel?" without an application. It is coding 143: ```bash curl -sS "https://biobank.ndph.ox.ac.uk/showcase/codown.cgi?id=143" -o olink_assays.tsv grep -c '' olink_assays.tsv head -3 olink_assays.tsv grep -E "^[0-9]+\s(GDF15|LEP|IL6|APOE);" olink_assays.tsv ``` Printed 2026-08-18 — 2,924 lines, so 2,923 assays: ``` 2924 coding meaning 1 A1BG;Alpha-1B-glycoprotein 2 AAMDC;Mth938 domain-containing protein 154 APOE;Apolipoprotein E 1137 GDF15;Growth/differentiation factor 15 1418 IL6;Interleukin-6 1572 LEP;Leptin ``` `meaning` is `GENE;description`, and the integer `coding` is the protein id used inside the `olink_data` table. Split on `;` to get a gene-symbol lookup. For an organ-specific proteomic clock this file plus `30900`'s n=53,039 is the complete feasibility answer: which of your marker set is measurable, on how many people. ## Get the files This writes **catalogue metadata** to disk as CSV — field descriptions, counts and shapes. It does not download and cannot download participant data. Set `QUERY` to a case-insensitive regular expression matched against field titles and category names. Every code column is decoded to a label, `columns_in_extract` is precomputed, the `-1` sentinel is preserved as a flag rather than coerced to a number, and a manifest records the SHA-256 of each source file so a later rebuild is comparable. ```python import csv, hashlib, io, json, os, re, urllib.request SHOWCASE = "https://biobank.ndph.ox.ac.uk/showcase" OUT = "Data/ukb_catalogue" QUERY = r"nmr|olink|protein biomarker" # case-insensitive, matched on title + category VALUE_TYPE = {"11": "integer", "21": "categorical single", "22": "categorical multiple", "31": "continuous", "41": "text", "51": "date", "61": "time", "101": "compound", "201": "blob"} ITEM_TYPE = {"0": "data (tabular)", "10": "sample", "20": "bulk (files)", "30": "record (portal table)"} STABILITY = {"0": "complete", "1": "updateable", "2": "accruing", "3": "variable"} INSTANCED = {"0": "no", "1": "defined", "2": "variable"} AVAILABLE = {"0": "full", "1": "view-only", "4": "retired"} os.makedirs(OUT, exist_ok=True) manifest = {"source": SHOWCASE, "query": QUERY, "files": [], "downloads": []} def fetch(url, dest, encoding): raw = urllib.request.urlopen(url, timeout=300).read() if raw.lstrip()[:9].lower().startswith(b"10,} bytes {dest}") return list(csv.DictReader(io.StringIO(raw.decode(encoding)), delimiter="\t", quoting=csv.QUOTE_NONE)) print("downloading:") fields = fetch(f"{SHOWCASE}/scdown.cgi?fmt=txt&id=1", f"{OUT}/schema_1_fields.tsv", "utf-8") cats = fetch(f"{SHOWCASE}/scdown.cgi?fmt=txt&id=3", f"{OUT}/schema_3_categories.tsv", "cp1252") insts = fetch(f"{SHOWCASE}/scdown.cgi?fmt=txt&id=9", f"{OUT}/schema_9_instancing.tsv", "utf-8") cat_name = {c["category_id"]: c["title"] for c in cats} inst_txt = {i["instance_id"]: " ".join(i["descript"].split())[:120] for i in insts} pat = re.compile(QUERY, re.I) rows = [] for f in fields: cname = cat_name.get(f["main_category"], f"[category {f['main_category']} not in schema 3]") if not (pat.search(f["title"]) or pat.search(cname)): continue lo, hi = int(f["instance_min"]), int(f["instance_max"]) n_inst = (hi - lo + 1) if f["instanced"] != "0" else 1 alo, ahi = int(f["array_min"]), int(f["array_max"]) n_arr = (ahi - alo + 1) if f["arrayed"] == "1" else 1 n = int(f["num_participants"]) rows.append({ "field_id": f["field_id"], "title": f["title"], "category_id": f["main_category"], "category": cname, "num_participants": "" if n < 0 else n, # -1 means "not published", not zero "n_is_sentinel": n < 0, "item_count": f["item_count"], "units": f["units"], "value_type": VALUE_TYPE.get(f["value_type"], f["value_type"]), "item_type": ITEM_TYPE.get(f["item_type"], f["item_type"]), "availability": AVAILABLE.get(f["availability"], f"code {f['availability']}"), "stability": STABILITY.get(f["stability"], f["stability"]), "instanced": INSTANCED.get(f["instanced"], f["instanced"]), "instances": n_inst, "instance_meaning": inst_txt.get(f["instance_id"], "") if f["instanced"] != "0" else "", "array_slots": n_arr, "columns_in_extract": n_inst * n_arr, "encoding_id": f["encoding_id"], "rap_cost_tier": f["cost_on"], "debut": f["debut"][:10], "version": f["version"][:10], "showcase_url": f"{SHOWCASE}/field.cgi?id={f['field_id']}", }) if not rows: raise SystemExit(f"no field matched /{QUERY}/ — widen it. Titles name measurements, " "not constructs, so a derived quantity has no field to match") rows.sort(key=lambda r: (r["category"], int(r["field_id"]))) csv_path = f"{OUT}/ukb_fields_filtered.csv" with open(csv_path, "w", newline="", encoding="utf-8") as fh: w = csv.DictWriter(fh, fieldnames=list(rows[0].keys())) w.writeheader() w.writerows(rows) manifest["files"].append({"path": csv_path, "rows": len(rows), "bytes": os.path.getsize(csv_path)}) manifest["catalogue_version"] = max(f["version"] for f in fields)[:10] manifest["fields_in_catalogue"] = len(fields) manifest["contains"] = ("field-level metadata only — no participant data, " "no individual-level values") with open(f"{OUT}/manifest.json", "w") as fh: json.dump(manifest, fh, indent=2) print(f"\n{len(rows)} fields matched /{QUERY}/ -> {csv_path}") print("catalogue version:", manifest["catalogue_version"]) for cid, cname in sorted({(r['category_id'], r['category']) for r in rows}, key=lambda t: int(t[0])): sub = [r for r in rows if r["category_id"] == cid] ns = [r["num_participants"] for r in sub if r["num_participants"] != ""] cols = sum(r["columns_in_extract"] for r in sub) dead = sum(r["availability"] == "retired" for r in sub) print(f" cat {cid:>5} {cname[:34]:34} {len(sub):>4} fields " f"max n {max(ns) if ns else 0:>7,} {cols:>5} extract columns" + (f" {dead} RETIRED" if dead else "")) ``` Printed 2026-08-18: ``` downloading: 4,164,390 bytes Data/ukb_catalogue/schema_1_fields.tsv 176,100 bytes Data/ukb_catalogue/schema_3_categories.tsv 1,530 bytes Data/ukb_catalogue/schema_9_instancing.tsv 517 fields matched /nmr|olink|protein biomarker/ -> Data/ukb_catalogue/ukb_fields_filtered.csv catalogue version: 2025-09-20 cat 220 NMR metabolomics 251 fields max n 488,514 502 extract columns cat 221 NMR metabolomics QC indicators 249 fields max n 412,098 345 extract columns cat 222 NMR metabolomics processing 12 fields max n 488,521 21 extract columns cat 1839 Protein biomarkers 5 fields max n 53,039 20 extract columns ``` Five files on disk: the three raw schema tables, the filtered CSV (517 rows, 187,261 bytes, 76 of them flagged `n_is_sentinel`), and `manifest.json`. Keep the raw TSVs — the catalogue is rebuilt continuously and the `version` column moves per field, so a checksummed copy is the only way to tell a real change from a mis-parse later. The error path is worth exercising too. `QUERY = r"metabolomic aging clock"` exits 1 with `no field matched …`, because titles name **measurements, not constructs** — a clock, a risk score you intend to derive, or a phenotype definition has no field to match. Search for the inputs instead. Conversely `QUERY = r"c-reactive protein"` returns 7 fields across Blood biochemistry (n up to 469,326) and its processing category, so plenty of names you would assume are absent are simply spelled UK Biobank's way. The query above is a flattering one: all 517 of its fields are `availability = full`, so it never exercises the retired path. Run it once on something that does, and read the summary rather than the CSV: ``` 86 fields matched /hospital inpatient|episodes containing/ cat 2000 Hospital inpatient 36 fields max n 413,163 36 extract columns 36 RETIRED cat 2002 Summary Diagnoses 10 fields max n 409,809 10 extract columns 10 RETIRED cat 2003 Summary Maternity 23 fields max n 195,756 23 extract columns 23 RETIRED cat 2004 Summary Psychiatric 6 fields max n 365,945 6 extract columns 6 RETIRED cat 2005 Summary Operations 11 fields max n 409,747 11 extract columns 11 RETIRED ``` Eighty-six for eighty-six, at cohort-scale participant counts, and every one of those categories shows an empty field list on the Showcase website. Without the `RETIRED` count this reads as the richest result in the skill. ## Requesting access **This skill cannot obtain access to UK Biobank data, and nothing in it should be read as a promise of access.** What follows describes the published route so you can decide whether to start it. The authoritative documents are the Access Procedures PDF and the UK Biobank website, linked under *Sources*; where this section and those disagree, they are right and this is stale. **Applications are closed. Do not start this process expecting it to complete.** Two UK Biobank pages said so on 2026-08-18, and they are the two a reader would land on: - Access Management System login page: "We are currently not accepting new applications to UK Biobank, as the UKB-RAP remains closed. We will share an update via the Researcher Community as soon as new applications reopen." - *Apply for access* (page's own stamp 1 July 2026): "Applications are currently paused whilst necessary changes are made to the UK Biobank Research Analysis Platform, and our priority is restoring access to compliant researchers on ongoing projects." followed by "**We intend to accept new applications in late 2026.**" So there is a published intention but no open door and no committed date. Everything below describes the route as published, for deciding whether to queue for it — re-read both pages before planning a timeline. This is a dated observation of a state expected to change, and the catalogue work above is the part you can do today regardless. **Who may apply.** Any bona fide researcher, for health-related research in the public interest, from academia, charity, government or commercial industry, in any country, all under the same process and criteria. The legal counterparty is the **Applicant Institution**, not the individual; the Applicant PI is named but carries no direct contractual responsibility. Collaborating institutions can be added at any time, each registering its researchers, executing the agreement, and paying an additional institution fee. **The published eight steps.** Register on AMS → registration reviewed (the Access Team aims for 5 days) → complete the application form and select the data → application reviewed by the Access Team and the Scientific Team → approved → fees and material transfer agreement issued → payment and signatures returned → data released. Approval is valid for **90 days**, within which the fee must be paid and the agreement signed. **What the forms ask for.** Registration: name, address, email, a CV, a list of peer-reviewed publications with links where possible, and the research department and institutional website. The application: a short lay summary; every researcher and institution involved; a summary scientific rationale — research question and aims, background and any pilot data, a brief overview of planned methodology, and the expected value and public health impact; contact details for a signatory authorised to sign the agreement for each institution; and the selection of the data itself, which is where the catalogue work above pays off. Two things this list does *not* include, and it is worth knowing early: your own ethics approval, and a hypothesis review. UK Biobank's Research Tissue Bank registration covers data applications, and the Access Procedures state UK Biobank does not consider its remit to be second-guessing the science "except in situations where the application is potentially untenable, absurd or unethical". **Duration and ongoing obligations.** Projects are granted a minimum three years, extendable in one-year increments during the final year and cumulatively beyond that, subject to fees and compliance. Annual report forms are required. Findings must be published in a journal or an open-access site — the Access Procedures ask for reasonable endeavours to do so within 6 months of the project completion date, and a commensurate level of publication within the first three years — and results must be returned to UK Biobank so other researchers can use them. Researchers agree not to attempt to identify participants. UK Biobank does not approve publications, and makes no claim over inventions developed using the resource. **Fees.** Cost-recovery only; UK Biobank states it recovers the incremental cost of servicing an application, not the cost of building the resource. The structure is **three tiers** by dataset size, charged for an initial 3-year period and renewable, exclusive of VAT, plus a per-additional-institution fee. Sample and re-contact applications are priced case by case. **The tier amounts are on the fees page, not in the Access Procedures PDF.** As that page carried them on 2026-08-18 (its own stamp: 14 April 2026): | | first 3 years | per year extension | covers | |---|---|---|---| | Tier 1 | £3,000 | £1,000 | questionnaires, physical measures, health outcomes, linked health data | | Tier 2 | £6,000 | £2,000 | plus assays, proteomics, measured and imputed genotypes | | Tier 3 | £9,000 | £3,000 | plus imaging, large-scale assays, whole genome and exome sequence | | additional institution | £1,000 | £500 | each institution added to an application | | student / lower-income country | £500 | £175 | reduced access fee | Two things that page says which are easy to get wrong from an older reading. **All tiers now include access via the UKB-RAP only** — that is not a restriction peculiar to the reduced-fee route; downloading fields at all is an exceptional-circumstances request charged at the Tier 3 fee. And the tier boundaries are currently discounted: proteomics sits under Tier 1 and imaging under Tier 2 "for a limited period", so a field's `cost_on` value and the tier you are billed may not be the same thing this year. Read the fees page rather than trusting any figure quoted anywhere else, this table included; the `cost_on` column tells you which tier each field is *classified* in. **What this skill will and will not do for you.** It will assemble your field shortlist with participant counts and fee tiers, tell you which fields are tabular versus bulk versus portal-table, draft a research question and methodology summary for you to edit, and checklist what each form asks for. It will **not** fill in or draft any attestation — data security arrangements, institutional or ethics status, non-re-identification undertakings, or the signature of an authorised signatory. Those are legal claims made by a named person on behalf of an institution, and they have to be written by that person. ## Try it A self-contained check that the catalogue is reachable and still has the shape this skill describes. Open data, no account, no key. **Data** — UK Biobank Showcase schema 1, the data-field catalogue: https://biobank.ndph.ox.ac.uk/showcase/scdown.cgi?fmt=txt&id=1 Openly published field-level metadata for the whole resource — no participant data, no account, no application. The Showcase publishes `wget` commands for these schema files on its own schema pages, so retrieving them is what the endpoint is for. **They do not carry an open-data licence**: the Showcase footer reserves reuse without a written licence from UK Biobank, so treat the downloaded catalogue as reference material for your own planning and do not republish or redistribute it. Last confirmed reachable 2026-08-18, together with schema 3 (categories) from the same host. ```python import csv, io, re, urllib.request from collections import Counter SHOWCASE = "https://biobank.ndph.ox.ac.uk/showcase" def schema(sid, encoding): """Showcase schema tables are TSV. Encoding differs per table — see below.""" raw = urllib.request.urlopen(f"{SHOWCASE}/scdown.cgi?fmt=txt&id={sid}", timeout=180).read() # scdown.cgi answers HTTP 200 with an HTML error page for a table id it does not know. assert not raw.lstrip()[:9].lower().startswith(b"]*id="f(\d+)"', page)) in_cat = [f for f in fields if f["main_category"] == "126"] dead = [f for f in in_cat if f["availability"] == "4"] print(f"category 126 Liver MRI : schema 1 has {len(in_cat)}, its Showcase page lists " f"{len(listed)}, difference {len(dead)} retired") assert len(in_cat) - len(dead) == len(listed), (len(in_cat), len(dead), len(listed)) # 126 passes with either method, so it cannot detect the method rotting. 149 and 150 can: # a `field.cgi?id=` scrape returns 29 and 7 there against a true 28 and 5. for cat in ("149", "150"): p = urllib.request.urlopen(f"{SHOWCASE}/label.cgi?id={cat}", timeout=180).read().decode( "utf-8", "replace") tbl = set(re.findall(r']*id="f(\d+)"', p)) live = [f for f in fields if f["main_category"] == cat and f["availability"] != "4"] scraped = set(re.findall(r"field\.cgi\?id=(\d+)", p)) print(f"category {cat:<3} : table {len(tbl)} == live {len(live)}, " f"prose scrape would say {len(scraped)}") assert len(tbl) == len(live), (cat, len(tbl), len(live)) assert len(scraped) > len(live), f"cat {cat} no longer distinguishes the two methods" # scdown.cgi is an HTTP-200-with-an-error-body endpoint. The status code proves nothing. bad = urllib.request.urlopen(f"{SHOWCASE}/scdown.cgi?fmt=txt&id=9999", timeout=60) body = bad.read() header = csv.DictReader(io.StringIO(body.decode()), delimiter="\t").fieldnames print(f"scdown.cgi?id=9999 : HTTP {bad.status}, {bad.headers.get('Content-Type')}, " f"parses as {len(header)} column named {header[0]!r}") assert bad.status == 200 and body.lstrip()[:9].lower().startswith(b"` without raising. Status codes are not a validity test on this host, for the schema download any more than for `field.cgi`. - NMR metabolomics and Protein biomarkers each resolve to exactly one category id, and Protein biomarkers holds fewer than 10 fields — the Olink values are a portal record table, not per-protein fields. Observed 2026-08-18 against catalogue version **2025-09-20** — these move whenever UK Biobank rebuilds, so a mismatch is drift to investigate, not a failure: ``` bytes (field catalogue) : 4164390 fields : 11821 categories : 410 catalogue version (max) : 2025-09-20 num_participants == -1 : 270 (sentinel, not a count) num_participants == 0 : 12 largest num_participants: 501938 main_category ids used but absent from schema 3: ['1000'] instanced: {'0': 4605, '1': 7198, '2': 18} arrayed: {'0': 11271, '1': 550} category 1000 titles : {'EMBARGOED UNLINKED': 63, 'EMBARGOED LINKED': 63} availability : {'0': 11592, '1': 54, '4': 172, '7': 3} retired fields : 172 retired under a live field's exact title: 11 ['189', '20033', '20034', '20074'] ... category 126 Liver MRI : schema 1 has 11, its Showcase page lists 7, difference 4 retired category 149 : table 28 == live 28, prose scrape would say 29 category 150 : table 5 == live 5, prose scrape would say 7 scdown.cgi?id=9999 : HTTP 200, text/html, parses as 1 column named '' NMR metabolomics category ['220'] | 251 fields | max n = 488,514 (field 23480) Protein biomarkers category ['1839'] | 5 fields | max n = 53,039 (field 30900) ``` ## Sources - Showcase — https://biobank.ndph.ox.ac.uk/showcase/ - Schema index (every downloadable table and its column definitions) — https://biobank.ndph.ox.ac.uk/showcase/schema.cgi - Schema 1 column definitions and code meanings — https://biobank.ndph.ox.ac.uk/showcase/schema.cgi?id=1 - Mirror — https://biobank.ctsu.ox.ac.uk/crystal/ - Access Procedures v2.1 (July 2022) — https://www.ukbiobank.ac.uk/wp-content/uploads/2026/05/Access-procedures.pdf - Apply for access — https://www.ukbiobank.ac.uk/use-our-data/apply-for-access/ - Fees — https://www.ukbiobank.ac.uk/use-our-data/fees/ - Financial support — https://www.ukbiobank.ac.uk/use-our-data/fees/financial-support/ - Access Management System — https://ams.ukbiobank.ac.uk/ams/ - Researcher community — https://community.ukbiobank.ac.uk/hc/en-gb - Sudlow et al. (2015) *PLoS Medicine* 12, e1001779 — https://doi.org/10.1371/journal.pmed.1001779 - Bycroft et al. (2018) *Nature* 562, 203-209 — https://doi.org/10.1038/s41586-018-0579-z - Littlejohns et al. (2020) *Nature Communications* 11, 2624 — https://doi.org/10.1038/s41467-020-15948-9 - Backman et al. (2021) *Nature* 599, 628-634 — https://doi.org/10.1038/s41586-021-04103-z - Julkunen et al. (2023) *Nature Communications* 14, 604 — https://doi.org/10.1038/s41467-023-36231-7 - Sun et al. (2023) *Nature* 622, 329-338 — https://doi.org/10.1038/s41586-023-06592-6 - Oh et al. (2023) *Nature* 624, 164-172 — https://doi.org/10.1038/s41586-023-06802-1 The field catalogue is published openly by UK Biobank and needs no account, but it is not released under an open-data licence — the Showcase carries a legal notice reserving reuse without a written licence. Download it, plan with it, and do not redistribute it. Individual-level participant data is separate, controlled, and governed by the material transfer agreement issued with an approved application. Cite the resource papers above and your application reference in published work.