An official datacite export is available at https://datafiles.datacite.org/datafiles. A recent version of datacite metadata generated with dcdump can be found here: https://archive.org/details/datacite-2024-07-31.
As of Fall 2019 the datacite API is a bit flaky: #237, #851, #188, #709 #897, #898, #1805.
This tool tries to get a data dump from the API, until a full dump might be available.
This data has been ingested into fatcat, via fatcat_import.py in 01/2020.
Built at the Internet Archive.
You'll need the go tool installed (i.e. installed go).
$ git clone https://git.archive.org/webgroup/dcdump.git
$ cd dcdump
$ make
Or install with the Go tool:
$ go install github.com/miku/dcdump/cmd/dcdump@latest
The basic idea is to request small enough chunks (intervals) from the API to eventuall capture most records. Some hand-holding may be required; e.g. request most data via "daily" or "hourly" slices and if gaps remain (e.g. because the number of updates in a given time slice exceeds the maximum number of records sent by the api), use "every minute" slices for the rest.
$ dcdump -h
Usage of dcdump:
-A do not include affiliation information
-d string
directory, where to put harvested files (default ".")
-debug
only print intervals then exit
-e value
end date for harvest (default 2022-07-04)
-i string
[w]eekly, [d]daily, [h]ourly, [e]very minute (default "d")
-l int
upper limit for number of requests (default 16777216)
-p string
file prefix for harvested files (default "dcdump-")
-s value
start date for harvest (default 2018-01-01)
-sleep duration
backoff after HTTP error (default 5m0s)
-version
show version
-w int
parallel workers (approximate) (default 4)
Affiliations are requested by default (turn if off with -A).
Example:
{
"data": [
{
"id": "10.3886/e100985v1",
"type": "dois",
"attributes": {
"doi": "10.3886/e100985v1",
"identifiers": [
{
"identifier": "https://doi.org/10.3886/e100985v1",
"identifierType": "DOI"
}
],
"creators": [
{
"name": "Porter, Joshua J.",
"nameType": "Personal",
"givenName": "Joshua J.",
"familyName": "Porter",
"affiliation": [
{
"name": "George Washington University"
}
],
"nameIdentifiers": []
}
],
...The dcdump tool uses datacite API version 2. We query for intervals and via cursor to circumvent the Index Deep Paging Problem (limit as of 12/2019 is 10000 records for a query, 400 pages x 25 records per page).
To just list the intervals (depending on the -i flag), use the -debug flag:
$ dcdump -i h -s 2019-10-01 -e 2019-10-02 -debug
2019-10-01 00:00:00 +0000 UTC -- 2019-10-01 00:59:59.999999999 +0000 UTC
2019-10-01 01:00:00 +0000 UTC -- 2019-10-01 01:59:59.999999999 +0000 UTC
2019-10-01 02:00:00 +0000 UTC -- 2019-10-01 02:59:59.999999999 +0000 UTC
2019-10-01 03:00:00 +0000 UTC -- 2019-10-01 03:59:59.999999999 +0000 UTC
2019-10-01 04:00:00 +0000 UTC -- 2019-10-01 04:59:59.999999999 +0000 UTC
2019-10-01 05:00:00 +0000 UTC -- 2019-10-01 05:59:59.999999999 +0000 UTC
2019-10-01 06:00:00 +0000 UTC -- 2019-10-01 06:59:59.999999999 +0000 UTC
2019-10-01 07:00:00 +0000 UTC -- 2019-10-01 07:59:59.999999999 +0000 UTC
2019-10-01 08:00:00 +0000 UTC -- 2019-10-01 08:59:59.999999999 +0000 UTC
2019-10-01 09:00:00 +0000 UTC -- 2019-10-01 09:59:59.999999999 +0000 UTC
2019-10-01 10:00:00 +0000 UTC -- 2019-10-01 10:59:59.999999999 +0000 UTC
2019-10-01 11:00:00 +0000 UTC -- 2019-10-01 11:59:59.999999999 +0000 UTC
2019-10-01 12:00:00 +0000 UTC -- 2019-10-01 12:59:59.999999999 +0000 UTC
2019-10-01 13:00:00 +0000 UTC -- 2019-10-01 13:59:59.999999999 +0000 UTC
2019-10-01 14:00:00 +0000 UTC -- 2019-10-01 14:59:59.999999999 +0000 UTC
2019-10-01 15:00:00 +0000 UTC -- 2019-10-01 15:59:59.999999999 +0000 UTC
2019-10-01 16:00:00 +0000 UTC -- 2019-10-01 16:59:59.999999999 +0000 UTC
2019-10-01 17:00:00 +0000 UTC -- 2019-10-01 17:59:59.999999999 +0000 UTC
2019-10-01 18:00:00 +0000 UTC -- 2019-10-01 18:59:59.999999999 +0000 UTC
2019-10-01 19:00:00 +0000 UTC -- 2019-10-01 19:59:59.999999999 +0000 UTC
2019-10-01 20:00:00 +0000 UTC -- 2019-10-01 20:59:59.999999999 +0000 UTC
2019-10-01 21:00:00 +0000 UTC -- 2019-10-01 21:59:59.999999999 +0000 UTC
2019-10-01 22:00:00 +0000 UTC -- 2019-10-01 22:59:59.999999999 +0000 UTC
2019-10-01 23:00:00 +0000 UTC -- 2019-10-01 23:59:59.999999999 +0000 UTC
2019-10-02 00:00:00 +0000 UTC -- 2019-10-02 00:59:59.999999999 +0000 UTC
INFO[0000] 25 intervals
Start and end date are relatively flexible, for example (minute slices for a single day):
$ dcdump -s 2019-05-01 -e '2019-05-01 23:59:59' -i e -debug
2019-05-01 00:00:00 +0000 UTC -- 2019-05-01 00:00:59.999999999 +0000 UTC
...
2019-05-01 23:59:00 +0000 UTC -- 2019-05-01 23:59:59.999999999 +0000 UTC
INFO[0000] 1440 intervals
...
So create some temporary dir (to not pollute the current directory with the harvested files).
$ mkdir tmp
Start harvesting (minute intervals, into tmp, with 2 workers).
$ dcdump -i e -d tmp -w 2
The time windows are not adjusted dynamically. Worse, it seems that even with a low profile harvest (two workers, backoffs, retries) and minute intervals, the harvest still can stall (maybe with a 403 or 500).
If a specific time window fails repeatedly, you can manually touch the file, e.g.
$ touch tmp/dcdump-20190801114700-20190801114759.ndjson
The dcdump tool checks for the existence of the file, before harvesting; this way it's possible to skip unfetchable slices.
After successful runs, concatenate the data to get a newline delimited single file dump of datacite.
$ cat tmp/*ndjson | sort -u > datacite.ndjson
Or, more modern:
$ fd 'dcdump-.*ndjson' -x cat | jq -rc '.data[]' > datacite.ndjson # may contain dupsAgain, this is ugly, but should all be obsolete as soon as a public data dump is available.
Another way:
$ fd | \
parallel --block 20M -j 32 cat | \
parallel --block 30M --pipe 'jq -rc .data[]' | \
pv -l | \
zstd -c -T0 > /tmp/dcdump-2024-04-17.json.zstA duration data point, about 80h.
$ dcdump -version
dcdump 5ae0556 2020-01-21T16:25:10Z
$ dcdump -i e
...
INFO[294683] 1075178 date slices succeeded
real 4911m23.343s
user 930m54.034s
sys 173m7.383s
After 80h, the total size amounts to about 78G.
- https://archive.org/details/datacite-2024-04-17
- https://archive.org/details/datacite-2024-01-26
- https://archive.org/details/datacite_dump_20230713
- https://archive.org/details/datacite_dump_20221118
- https://archive.org/details/datacite_dump_20211022
- https://archive.org/details/datacite_dump_20200824
- https://archive.org/details/datacite_dump_20191122
A datacite snapshot from 11/2019 is available as part of the Bulk Bibliographic Metadata collection at Datacite Dump 20191122.
18210075 items, 72GB uncompressed.
See: #1805, #709 and ia_biblio_metadata for updates.
- https://archive.org/details/datacite_dump_20211022; 25859678 unique (lowercased) DOI
$ curl -sL https://archive.org/download/datacite_dump_20211022/datacite_dump_20211022.json.zst | \
zstdcat -c -T0 | jq -rc '.id'
10.1001/jama.289.8.989
10.1001/jama.293.14.1723-a
10.1001/jamainternmed.2013.9245
10.1001/jamaneurol.2015.4885
10.1002/2014gb004975
10.1002/2014gl061020
10.1002/2014jc009965
10.1002/2014jd022411
10.1002/2015gb005314
10.1002/2015gl065259
...
$ xz -T0 -cd datacite.ndjson.xz | wc
18210075 2562859030 72664858976
$ xz -T0 -cd datacite.ndjson.xz | sha1sum
6fa3bbb1fe07b42e021be32126617b7924f119fb -
JI:KNIEKQ2QKJFEGVCTFUZDIMZQBI