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  • chetouae/open-research-data-monitor-back
  • mlarrieu/open-research-data-monitor-back
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.gitlab-ci.yml
View file @ 2bd53d86
......@@ -3,29 +3,57 @@ image: python:3-alpine
actualisation_dois:
only:
# restreindre au déclenchement automatique par schedule ou déclenchement manuel pour éviter de tourner en boucle (le commit produit par le pipeline qui déclenche le pipeline à nouveau)
- schedules
- web
before_script:
# installations permettant de faire tourner git
- apk update
- apk add git openssh
script:
# installation des bibliothèques python et exécution du script
- pip install pandas
- pip install requests
- pip install matplotlib
- python run-all-codes.py
after_script:
# commit des changements suite à l'exécution du script
- git config user.name "${GITLAB_USER_NAME}"
- git config user.email "${GITLAB_USER_EMAIL}"
- git remote set-url --push origin "https://PUSH_TOKEN:${ACCESS_TOKEN}@gricad-gitlab.univ-grenoble-alpes.fr/${CI_PROJECT_PATH}.git"
- git add -f dois-uga.csv 2-produce-graph/hist-evol-datasets-per-repo.png 2-produce-graph/hist-quantity-year-type.png 2-produce-graph/pie--datacite-client.png 2-produce-graph/pie--datacite-type.png
- git add -f dois-uga.csv dois-uga--last-500.csv 2-produce-graph/hist-evol-datasets-per-repo.png 2-produce-graph/hist-quantity-year-type.png 2-produce-graph/pie--datacite-client.png 2-produce-graph/pie--datacite-type.png 2-produce-graph/hist-last-datasets-by-client.png 1-enrich-with-datacite/all_datacite_clients_for_uga.csv 1-enrich-with-datacite/nb-dois.txt
- git commit -m "Execution du pipeline. Actualisation des dois et des graphes."
- git push origin HEAD:$CI_COMMIT_REF_NAME
- git push origin HEAD:${CI_COMMIT_REF_NAME}
# création d'un espace accueillant le clone du repo du site web, et tests au cas où l'espace existe déjà
- cd ..
- if ! [ -d "cloned_repo" ]; then mkdir cloned_repo; fi
- if [ -d "cloned_repo/${PATH_TO_PUSH}" ]; then cd cloned_repo/${PATH_TO_PUSH}; git pull; else cd cloned_repo; git clone ${LINK_TO_CLONE}; fi
- cd -
# copier le fichier "nb-dois.txt" pour commit dans le repo du site web
- cp open-research-data-monitor-back/1-enrich-with-datacite/nb-dois.txt cloned_repo/${PATH_TO_PUSH}
- cd cloned_repo/${PATH_TO_PUSH}
# commit du fichier "nb-dois.txt" vers le repo du site web
- git config user.name "${GITLAB_USER_NAME}"
- git config user.email "${GITLAB_USER_EMAIL}"
- git remote set-url --push origin "https://PUSH_TOKEN2:${ACCESS_TOKEN2}@gricad-gitlab.univ-grenoble-alpes.fr/${PROJECT_PATH2}.git"
- git add -f nb-dois.txt
- git commit -m "Execution du pipeline. Actualisation du nombre de dois."
- git push origin HEAD:main
artifacts:
# ajout des fichiers du dépôt qui ont été modifiés, au cas où un problème serait survenu dans "after_script"
paths:
- dois-uga.csv
- dois-uga--last-500.csv
- 2-produce-graph/hist-evol-datasets-per-repo.png
- 2-produce-graph/hist-quantity-year-type.png
- 2-produce-graph/pie--datacite-client.png
- 2-produce-graph/pie--datacite-type.png
- 2-produce-graph/hist-last-datasets-by-client.png
- 1-enrich-with-datacite/nb-dois.txt
- 1-enrich-with-datacite/all_datacite_clients_for_uga.csv
0-collect-data/datacite-dois.txt
View file @ 2bd53d86
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10.17178/emaa_para-h2(18o)_rotation_4ebe92f7
10.26302/sshade/experiment_dt_20180117_002
10.60527/bjb2-pj78
10.6084/m9.figshare.21285659
10.6084/m9.figshare.23822163
10.26302/sshade/experiment_ak_20141118_1
10.26302/sshade/bandlist_abs_s2o_ar-matrix
10.17178/gnss.products.europe
10.5281/zenodo.10276480
10.26302/sshade/experiment_ml_20171219_003
10.25384/sage.22573164
10.26302/sshade/experiment_bs_20200217_100
10.17178/emaa_hco-plus_rotation_a861ef7a
10.17178/zaa_soil_temp.gloria
10.26302/sshade/bandlist_raman_n2_alpha-n2
10.26302/sshade/experiment_pa_20180702_001
10.7280/d1wt11
10.26302/sshade/experiment_bs_20120923_001
10.26302/sshade/bandlist_abs_c4h2_am-c4h2
10.26302/sshade/experiment_soc_20181115_004
10.5061/dryad.k31d4
10.26302/sshade/experiment_ak_20141107_1
10.48380/dggv-jsn0-1k58
10.18709/perscido.2020.05.ds314
10.26302/sshade/experiment_vc_20050725_001
10.26302/sshade/experiment_sb_20210203_001
10.5061/dryad.wh70rxwnf
10.13140/rg.2.2.34930.04805
10.26302/sshade/experiment_cl_20181201_07
10.26302/sshade/experiment_sh_20050103_001
10.48380/dggv-p1k4-1p40
10.17178/emaa_(13c)(17o)_hyperfine_164ecb70
10.5061/dryad.4mj8gg0
10.26302/sshade/experiment_lb_20200914_001
10.17178/emaa_para-h2o_rotation_18d206fb
10.5061/dryad.g72v731
10.26302/sshade/experiment_cl_20181201_02
10.15778/resif.xw2019
10.6084/m9.figshare.12886126
10.6084/m9.figshare.22620043
10.3217/9rrz9-ywt68
10.17178/amma-catch.pa.schem_snfd
10.6084/m9.figshare.c.5129698
10.17178/ohmcv.dsd.vb3.12-16.1
10.26302/sshade/experiment_gl_20150103_1
10.48380/2gzr-9q70
10.5281/zenodo.10715585
10.6084/m9.figshare.22075664.v2
10.25364/19.2022.7.3
10.26302/sshade/experiment_rc_20191124_000
10.5061/dryad.t8534
10.6084/m9.figshare.16851093
10.15778/resif.xz2020
10.15778/resif.x12021
10.5281/zenodo.10069276
10.6084/m9.figshare.c.6712342
10.6084/m9.figshare.c.6627379
10.6084/m9.figshare.24202747
10.15778/resif.zh2003
10.6084/m9.figshare.c.6795368
10.26302/sshade/experiment_rc_20200506_000
10.15778/resif.cl
10.5061/dryad.brv15dvcj
10.5061/dryad.4f4qrfjjc
10.18709/perscido.2019.07.ds249
10.26302/sshade/bandlist_raman_otavite
10.17178/emaa_para-nhd2_hyperfine_3bc12b4c
10.26302/sshade/experiment_ak_20141113_1
10.17178/amma-catch.ae.h2oflux_ncw
10.15778/resif.zf2003
10.26302/sshade/experiment_hm_20171017_001
10.7280/d1mm37
10.26302/sshade/bandlist_raman_kutnohorite
10.26302/sshade/experiment_soc_20181115_003
10.6084/m9.figshare.22621187
10.17178/zaa_soil_temp.all
10.6084/m9.figshare.21432228
10.26302/sshade/experiment_nc_20181214_001
10.48380/6fh7-7m80
10.17178/amma-catch.ce.sap_odc
10.5061/dryad.vx0k6djp4
10.26302/sshade/experiment_cl_20180316_01
10.6084/m9.figshare.24202750
10.48390/0005-gz84
10.26302/sshade/experiment_ak_20141125_2
10.26302/sshade/experiment_gm_20221013_001
10.6084/m9.figshare.c.6424523
10.6084/m9.figshare.c.6604292.v1
10.6084/m9.figshare.23575360
10.5061/dryad.d2547d810
10.25384/sage.c.6377642
10.26302/sshade/experiment_zy_20180216_000
10.6084/m9.figshare.c.6910186
10.6084/m9.figshare.22735402
10.26302/sshade/bandlist_abs_n2_beta-n2
10.5281/zenodo.10551644
10.6084/m9.figshare.c.6592129
10.6084/m9.figshare.14870669.v1
10.17178/cryobsclim.clb.muzelle
10.6084/m9.figshare.16786741.v1
10.17178/cryobsclim.clb.dem
10.5061/dryad.78642
10.6084/m9.figshare.22609322
10.5061/dryad.dc25730
10.6084/m9.figshare.c.5105152
10.6084/m9.figshare.c.6575881.v1
10.17178/gnss.products.japan
10.26302/sshade/experiment_mb_20230522_0001
10.6084/m9.figshare.20235676.v1
10.6084/m9.figshare.16851117
10.25384/sage.23826552
10.17178/emaa_ortho-c3h2_rotation_caf26a12
10.6084/m9.figshare.16786765
10.26302/sshade/experiment_jg_20090226_003
10.6084/m9.figshare.23822160.v1
10.5061/dryad.6226d
10.26302/sshade/experiment_jf_20220531_001
10.6084/m9.figshare.23575390
10.5061/dryad.q573n5tm6
10.6084/m9.figshare.16851156
10.17178/amma-catch.cl.raind_gh
10.17178/emaa_para-(15n)h2d_rotation_e7374da5
10.17178/cryobsclim.cdp.2018.soil
10.26302/sshade/experiment_kd_20220525_002
10.26302/sshade/bandlist_raman_vaterite
10.5281/zenodo.10400476
10.5061/dryad.7wm37pvx5
10.17178/emaa_para-h2(18o)_rotation_4ebe92f7
10.5061/dryad.v41ns1rwx
10.17178/amma-catch.ce.run_odc
10.26302/sshade/experiment_op_20201203_001
10.15778/resif.6f2021
10.26302/sshade/experiment_bs_20130215_001
10.17178/cryobsclim.clb.dome
10.26302/sshade/experiment_cc_20180723_01
10.26302/sshade/bandlist_raman_n2_beta-n2
10.6084/m9.figshare.24940485
10.17178/amma-catch.ae.h2oflux_g
10.26302/sshade/experiment_yd_20160401_001
10.17178/cryobsclim.clb.bso
10.26302/sshade/experiment_bs_20200422_100
10.17178/emaa_hcn_rotation_a4d98833
10.6084/m9.figshare.23816832
10.26302/sshade/experiment_cc_20180724_01
10.26302/sshade/experiment_eb_20180618_001
10.26302/sshade/experiment_dl_20181107_002
10.18709/perscido.2022.09.ds375
10.25384/sage.c.6837354.v1
10.5061/dryad.c1jr3
10.26302/sshade/bandlist_abs_ch3oh_am-ch3oh
10.26302/sshade/experiment_zy_20180212_000
10.6084/m9.figshare.22613069
10.5061/dryad.pf420
10.17178/emaa_sh-plus_hyperfine_da4d4f7a
10.26302/sshade/experiment_op_20230220_001
10.5281/zenodo.10048443
10.6084/m9.figshare.22620031
10.17178/emaa_para-h2(13c)o_rotation_903f6446
10.6084/m9.figshare.c.5394711
10.26302/sshade/experiment_bs_20130125_001
10.26302/sshade/experiment_lm_20220304_mtkl
10.26302/sshade/experiment_lb_20191211_005
10.17178/emaa_para-h2c(18o)_rotation_8f130c2d
10.26302/sshade/experiment_sb_20200629_001
10.26302/sshade/bandlist_raman_siderite
10.57757/iugg23-4563
10.26302/sshade/experiment_yd_20220602_010
10.6084/m9.figshare.c.6272697.v1
10.15778/resif.yr2013
10.17178/ohmcv.dsd.ale.12-16.1
10.7914/wqvg-2272
10.6084/m9.figshare.c.6771537.v1
10.17178/amma-catch.pa.met_snnr
10.26302/sshade/experiment_sb_20210115_001
10.6084/m9.figshare.20080019
10.6084/m9.figshare.c.5487977
10.26302/sshade/experiment_soc_20201214_001
10.26302/sshade/experiment_sb_20200403_001
10.57757/iugg23-4534
10.6084/m9.figshare.22620043.v1
10.5061/dryad.xksn02vn4
10.7280/d1wt11
10.6084/m9.figshare.c.6604292
10.17178/emaa_n(15n)h-plus_hyperfine_4f2d77ce
10.26302/sshade/experiment_tg_20181312_001
10.17178/amma-catch.benin
10.26302/sshade/experiment_lb_20180718_001
10.26302/sshade/experiment_op_20200212_001
10.17178/cryobsclim.clb.col
10.25647/liepp.wp.36
10.17178/amma-catch.ce.sw_nc
10.26302/sshade/experiment_jg_20091028_001
10.26302/sshade/experiment_sb_20210324_001
10.6084/m9.figshare.23575384
10.17178/emaa_para-(13c)c2h2_rotation_62296b37
10.17178/amma-catch.ae.h2oflux_odc
10.17178/zaa_soil_temp.bioclim
10.25384/sage.23826552.v1
10.6084/m9.figshare.c.5394711.v1
10.6084/m9.figshare.23575372.v1
10.6084/m9.figshare.22625614.v1
10.26302/sshade/experiment_hm_20180417_001
10.26302/sshade/experiment_bms_20160101_001
10.17178/emaa_c2h_hyperfine_e0d01d58
10.6084/m9.figshare.24647123
10.6084/m9.figshare.22613066.v1
10.6084/m9.figshare.c.6650322.v1
10.26302/sshade/experiment_op_20180717_003
10.6084/m9.figshare.c.6592858.v1
10.5281/zenodo.10050502
10.7280/d1667w
10.26302/sshade/experiment_ml_20171220_002
10.26302/sshade/experiment_ml_20171219_004
10.6084/m9.figshare.22649276
10.26302/sshade/experiment_lb_20191220_001
10.6084/m9.figshare.c.6077576
10.6084/m9.figshare.12291707
10.18709/perscido.2023.06.ds392
10.5281/zenodo.10053093
10.6084/m9.figshare.16786774
10.15778/resif.yt2001
10.17178/emaa_para-nh2_hyperfine_0d542831
10.6084/m9.figshare.16851114
10.26302/sshade/experiment_bs_20120925_001
10.6084/m9.figshare.12886138.v1
10.17178/amma-catch.ce.veg_ncw
10.6084/m9.figshare.c.5279142
10.5281/zenodo.10654576
10.17178/emaa_a-ch3oh_rotation_aa19a665
10.6084/m9.figshare.24483508
10.6084/m9.figshare.c.6627480
10.26302/sshade/experiment_pc_20220301_001
10.26302/sshade/experiment_gm_20190903_001
10.5281/zenodo.10423906
10.5281/zenodo.10022527
10.5281/zenodo.8269855
10.6084/m9.figshare.c.7007130
10.26302/sshade/bandlist_raman_n2_alpha-n2
10.26302/sshade/experiment_ak_20141119_1
10.15778/resif.xf2018
10.18709/perscido.2017.06.ds80
10.17178/amma-catch.ae.shflux_odc
10.26302/sshade/experiment_bms_20150101_001
10.60527/kzta-f520
10.26302/sshade/experiment_ak_20141120_1
10.6084/m9.figshare.14870669
10.26302/sshade/bandlist_raman_smithsonite
10.26302/sshade/experiment_gl_20150105_1
10.26302/sshade/bandlist_raman_ankerite
0-collect-data/datacite.py
View file @ 2bd53d86
......@@ -12,8 +12,8 @@ https://support.datacite.org/docs/can-i-see-more-detailed-affiliation-informatio
* mémo : pour descendre au niveau des auteurs , le filtre `?person-id=orcid-nb`
* nb 2024-01-13
retrait de l'AAU (jbru.aau) car tout est du PD de congrès
* note 2023-02
retrait de l'AAU (jbru.aau) car tout est du PDF de congrès
ajout de client.uid:inist.sshade et client.uid:inist.resif
"""
......@@ -125,19 +125,6 @@ if main_no_dois :
print("datasets with an other identifier than DOI has been finded")
[print(f"\t\t{elem}") for elem in main_no_dois]
## __________n__________ remove DOIs who are newer version via Figshare repository
## example 10.6084/m9.figshare.23737431.v2
## peut être refait lors de la récupération en véridiant que relatedIdentifiers/relationType:"IsIdenticalTo" ne soit présent
doi_to_remove = []
for doi in main_dois :
if "figshare" in doi :
# synthaxe : remove the number of the version
doi_shorten = doi[: len(doi) - 1]
if doi_shorten.endswith(".v") :
doi_to_remove.append(doi)
[main_dois.remove(elem) for elem in doi_to_remove]
## __________n__________ remove duplicates
unique_dois = list(set(main_dois))
print(f"\tNb of unique DOI\t{len(unique_dois)}")
......
0-collect-data/nakala-uga-users.txt
View file @ 2bd53d86
......@@ -17,4 +17,18 @@ mbeligne
acarbonnelle
annegf
tleduc
abey
\ No newline at end of file
abey
mbarletta
lmaritaud
jbeaureder
kboczon
llacoste
fcorsi
ecarlier
lvanbogaert
nrousselot
jlevy1
mflecheux
pbai
ymonnier
slecuyerchardevel
\ No newline at end of file
0-collect-data/rdg.py
View file @ 2bd53d86
......@@ -21,16 +21,16 @@ urls = [
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=authorAffiliation%3AUGA',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=producerAffiliation%3AUGA',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=contributorAffiliation%3AUGA',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=datasetContactAffiliation%3AGrenoble',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=authorAffiliation%3AGrenoble',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=producerAffiliation%3AGrenoble',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=contributorAffiliation%3AGrenoble'
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=datasetContactAffiliation%3A(Grenoble AND Alpes)',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=authorAffiliation%3A(Grenoble AND Alpes)',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=producerAffiliation%3A(Grenoble AND Alpes)',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=contributorAffiliation%3A(Grenoble AND Alpes)'
# possiblilité d'ajouter d'autres requêtes
]
# on définit une fonction pour la lancer la requete avec chaque url pour les différentes affiliations
def get_results(url):
req = requests.get(url)
req = requests.get(url+"&type=dataset")
#print(req.url)
results = [req.json()]
......@@ -39,7 +39,7 @@ def get_results(url):
count = nb_res
page = 1
while(nb_res > 0):
newurl = url+"&start="+str(count)
newurl = url+"&type=dataset"+"&start="+str(count)
req = requests.get(newurl)
results.append(req.json())
nb_res = results[page]["data"]["count_in_response"]
......@@ -59,7 +59,7 @@ def get_dois(results):
nb_dois += len(num_dois)
for item in num_dois :
dois.append(item["global_id"])
dois.append(item.get("global_id"))
print("\tnb DOIs\t\t" + str(nb_dois))
return dois
......
0-collect-data/zenodo-dois.txt
View file @ 2bd53d86
10.5281/zenodo.4760491
10.5061/dryad.rxwdbrvbg
10.5281/zenodo.3899776
10.5281/zenodo.7418361
10.5281/zenodo.1189443
10.5281/zenodo.3384633
10.5281/zenodo.10058946
10.5281/zenodo.7495559
10.5281/zenodo.7275468
10.5281/zenodo.8319001
10.5281/zenodo.6793401
10.5061/dryad.v475g
10.5281/zenodo.5336853
10.5281/zenodo.5783436
10.5281/zenodo.5243362
10.5281/zenodo.5824568
10.5281/zenodo.5243268
10.5281/zenodo.5842110
10.5281/zenodo.45044
10.5281/zenodo.8046630
10.5281/zenodo.8388930
10.5281/zenodo.4761357
10.5281/zenodo.7085313
10.5281/zenodo.5508669
10.5281/zenodo.4022283
10.5281/zenodo.6941739
10.5281/zenodo.2577796
10.5281/zenodo.2248525
10.5281/zenodo.5765565
10.5281/zenodo.4964223
10.5281/zenodo.4761333
10.5281/zenodo.6956953
10.5281/zenodo.7561767
10.5281/zenodo.7688636
10.5281/zenodo.3628203
10.5281/zenodo.8408864
10.5281/zenodo.7982058
10.5281/zenodo.5654628
10.7280/D1GW91
10.5281/zenodo.4964227
10.5281/zenodo.3354711
10.5281/zenodo.5717483
10.5281/zenodo.7342481
10.5281/zenodo.8271248
10.5061/dryad.6t1g1jx0m
10.5281/zenodo.7689499
10.5281/zenodo.4760487
10.5281/zenodo.7472518
10.5281/zenodo.6078514
10.3897/phytokeys.95.22324.figure1
10.5281/zenodo.1185426
10.5281/zenodo.3817352
10.5281/zenodo.3817437
10.5281/zenodo.7007289
10.5061/dryad.pc866t1k5
10.5281/zenodo.8410346
10.3897/rio.7.e77750.figure8b
10.5281/zenodo.4632426
10.5061/dryad.mw6m905tc
10.5281/zenodo.8147580
10.5281/zenodo.7308352
10.5281/zenodo.8101891
10.5281/zenodo.5145755
10.5281/zenodo.4048589
10.5061/dryad.3bk3j9kph
10.5281/zenodo.6913393
10.5281/zenodo.6977303
10.5281/zenodo.4655840
10.5281/zenodo.3628203
10.3897/neobiota.62.52934.suppl3
10.5281/zenodo.3541149
10.5061/dryad.6226d
10.5281/zenodo.7143466
10.5281/zenodo.8247362
10.5281/zenodo.8392608
10.5281/zenodo.7307563
10.5281/zenodo.6401829
10.5281/zenodo.5237198
10.5281/zenodo.4759489
10.5281/zenodo.8390942
10.5061/dryad.5s77q
10.5281/zenodo.2641991
10.5281/zenodo.4761347
10.5281/zenodo.7558920
10.7280/D1667W
10.3897/rio.7.e77750.figure6
10.3897/rio.7.e77750.figure2
10.5281/zenodo.5744873
10.5281/zenodo.3585723
10.5281/zenodo.8094516
10.5281/zenodo.1035485
10.5281/zenodo.8342388
10.5281/zenodo.4964201
10.5281/zenodo.10419097
10.5281/zenodo.4925909
10.5281/zenodo.4538745
10.5061/dryad.bnzs7h4dx
10.5281/zenodo.7804590
10.5281/zenodo.4646678
10.5281/zenodo.4292168
10.5281/zenodo.4014726
10.5281/zenodo.8060225
10.5061/dryad.8gtht76tq
10.5061/dryad.490p9
10.5281/zenodo.20031
10.5281/zenodo.59146
10.5281/zenodo.37476
10.5281/zenodo.7982064
10.5281/zenodo.7627946
10.5281/zenodo.5727127
10.5281/zenodo.4029598
10.5281/zenodo.4760497
10.5281/zenodo.8155231
10.5281/zenodo.4288857
10.5281/zenodo.5763672
10.5281/zenodo.10165854
10.5281/zenodo.4745568
10.1002/esp.5650
10.5281/zenodo.6902628
10.3897/phytokeys.117.30672.figure2
10.5281/zenodo.7147022
10.5281/zenodo.4543403
10.5281/zenodo.6104368
10.5281/zenodo.4776419
10.5281/zenodo.3759385
10.5281/zenodo.5243189
10.5281/zenodo.4603782
10.5281/zenodo.10341149
10.5281/zenodo.7114168
10.5281/zenodo.4790667
10.5281/zenodo.5842110
10.5281/zenodo.6671769
10.5281/zenodo.2575055
10.5281/zenodo.6221153
10.5281/zenodo.7961207
10.5281/zenodo.6787572
10.5281/zenodo.6822165
10.5281/zenodo.4628245
10.5281/zenodo.59144
10.5281/zenodo.6074472
10.5281/zenodo.8104792
10.5061/dryad.bnzs7h4ds
10.5281/zenodo.6798922
10.5281/zenodo.5243209
10.5061/dryad.4n5929c
10.5281/zenodo.7307793
10.5061/dryad.rb0qk13
10.5281/zenodo.8388918
10.5281/zenodo.2641868
10.5281/zenodo.5835975
10.3897/BDJ.8.e48623.figure2a
10.5281/zenodo.1443459
10.5281/zenodo.8329497
10.5281/zenodo.7580589
10.5281/zenodo.7785223
10.5281/zenodo.5062314
10.5281/zenodo.10429521
10.5281/zenodo.3528068
10.5061/dryad.st350
10.5061/dryad.mw6m905tc
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10.5281/zenodo.6860527
10.5281/zenodo.5865001
10.5281/zenodo.8225005
10.5281/zenodo.4546112
10.5281/zenodo.5142591
10.5061/dryad.jm63xsj7b
10.5281/zenodo.5242859
10.5061/dryad.sq67g
10.5281/zenodo.6565895
10.5281/zenodo.7762439
10.5281/zenodo.8296848
10.5281/zenodo.8226430
10.5281/zenodo.3135479
10.5281/zenodo.8269855
10.3897/neobiota.70.65454.suppl2
10.5281/zenodo.4067946
10.5281/zenodo.6985564
10.5281/zenodo.10020919
10.5281/zenodo.6861341
10.5281/zenodo.10061547
10.3897/rio.7.e77750.figure8a
10.5281/zenodo.7840469
10.5281/zenodo.7590005
10.5281/zenodo.4308510
10.5281/zenodo.7982036
10.5281/zenodo.7928522
10.5281/zenodo.6319386
10.1016/j.mtla.2022.101480
10.5061/dryad.3216c
10.5281/zenodo.4745556
10.5281/zenodo.7308352
10.5281/zenodo.10200663
10.5281/zenodo.7462516
10.5281/zenodo.4964213
10.5281/zenodo.840877
10.5281/zenodo.10047691
10.5281/zenodo.4769825
10.5281/zenodo.10428215
10.5281/zenodo.7385445
10.5281/zenodo.7982040
10.5061/dryad.jwstqjqbr
10.5281/zenodo.7603489
10.5281/zenodo.7108355
10.5281/zenodo.3340631
10.5281/zenodo.4761339
10.5281/zenodo.4048589
10.5061/dryad.f4qrfj6wr
10.5281/zenodo.1489533
10.5281/zenodo.4753291
10.5281/zenodo.5062314
10.5281/zenodo.4059193
10.5061/dryad.3r2280ggb
10.3897/rio.7.e77750.figure4
10.5281/zenodo.10058946
10.5281/zenodo.999197
10.5281/zenodo.832421
10.5281/zenodo.8265979
10.5061/dryad.8cz8w9gqs
10.5281/zenodo.10419097
10.5061/dryad.d2547d810
10.5281/zenodo.7331741
10.5281/zenodo.10469400
10.5061/dryad.283pp
10.5281/zenodo.3759301
10.5281/zenodo.7855146
10.5281/zenodo.10133203
10.5281/zenodo.5788695
10.5281/zenodo.3613951
10.5281/zenodo.1112342
10.5281/zenodo.8404376
10.5281/zenodo.10062356
10.5281/zenodo.5905877
10.5281/zenodo.4761305
10.5281/zenodo.7982052
10.5281/zenodo.4761335
10.5281/zenodo.6786833
10.3897/alpento.2.23041.figure3
10.5281/zenodo.3550192
10.5061/dryad.9w0vt4bd1
10.5281/zenodo.2641214
10.5281/zenodo.4761355
10.5281/zenodo.1307653
10.5281/zenodo.8272736
10.5281/zenodo.7835266
10.5281/zenodo.7372398
10.5281/zenodo.6405782
10.5281/zenodo.4764317
10.5281/zenodo.4724109
10.5281/zenodo.8252180
10.5281/zenodo.6806404
10.5281/zenodo.6985564
10.5061/dryad.1qt12
10.5281/zenodo.10199624
10.5281/zenodo.7813697
10.5281/zenodo.8206446
10.5281/zenodo.4729758
10.3897/natureconservation.49.82931.suppl4
10.5281/zenodo.5237188
10.5281/zenodo.7119220
10.5281/zenodo.8408515
10.5281/zenodo.8014905
10.5281/zenodo.3345743
10.5281/zenodo.6526421
10.5281/zenodo.3981252
10.5281/zenodo.4745564
10.5281/zenodo.5243264
10.5281/zenodo.1256648
10.5281/zenodo.3817352
10.5281/zenodo.4574537
10.5281/zenodo.4607934
10.5281/zenodo.888977
10.5061/dryad.jwstqjqbr
10.5281/zenodo.1156633
10.5281/zenodo.10277799
10.5281/zenodo.8404376
10.5281/zenodo.8076436
10.5281/zenodo.8094516
10.5281/zenodo.5013300
10.5281/zenodo.7143466
10.5281/zenodo.8269855
10.1561/2200000089
10.5281/zenodo.4302473
10.5281/zenodo.1068339
10.5281/zenodo.10485581
10.5061/dryad.8290n
10.5281/zenodo.8421879
10.5281/zenodo.4680486
10.5281/zenodo.8189792
10.5281/zenodo.6498344
10.5061/dryad.ttdz08ktn
10.5281/zenodo.4715737
10.3897/BDJ.8.e48623.figure2a
10.3897/BDJ.8.e48623.figure2c
10.5281/zenodo.4640463
10.5281/zenodo.3384633
10.5281/zenodo.6860527
10.5281/zenodo.5554849
10.5281/zenodo.3817437
10.5281/zenodo.3817433
10.5281/zenodo.35230
10.5281/zenodo.6683741
10.5281/zenodo.7785223
10.5281/zenodo.7030107
10.5281/zenodo.7499383
10.5281/zenodo.7866962
10.5281/zenodo.5120376
10.5281/zenodo.7249512
10.5061/dryad.qv9s4mwf3
10.5281/zenodo.7395343
10.48550/arXiv.2310.14831
10.5281/zenodo.1289969
10.5281/zenodo.5243352
10.3897/phytokeys.95.22324.figure3
10.5061/dryad.cvdncjt2p
10.5281/zenodo.4139737
10.5061/dryad.hmgqnk9hh
10.5281/zenodo.1256648
10.5281/zenodo.5835983
10.5281/zenodo.3876188
10.5281/zenodo.8104455
10.5281/zenodo.5645545
10.5281/zenodo.5648316
10.5281/zenodo.4943234
10.5281/zenodo.7970338
10.5281/zenodo.3873216
10.7280/D1B114
10.5281/zenodo.8014905
10.5281/zenodo.8421859
10.5281/zenodo.4761299
10.3897/BDJ.8.e48623.figure1
10.5281/zenodo.5723996
10.5281/zenodo.4265431
10.1002/essoar.10508001.1
10.5061/dryad.k98sf7m98
10.5281/zenodo.8271121
10.5061/dryad.6q573n621
10.5281/zenodo.7189647
10.5281/zenodo.4760505
10.3897/phytokeys.95.22324.figure3
10.5281/zenodo.4761291
10.3897/phytokeys.117.30672.figure3
10.5281/zenodo.4581363
10.5281/zenodo.10133203
10.5281/zenodo.1199545
10.5281/zenodo.159474
10.5281/zenodo.3689678
10.7280/D1595V
10.5281/zenodo.1495070
10.5281/zenodo.8095047
10.5281/zenodo.8083133
10.5281/zenodo.6461645
10.5281/zenodo.6974319
10.5281/zenodo.10679304
10.5281/zenodo.8324811
10.5281/zenodo.4067946
10.5281/zenodo.4916120
10.5281/zenodo.3625778
10.5281/zenodo.8171326
10.5281/zenodo.5999123
10.5281/zenodo.3463483
10.5281/zenodo.8008238
10.5281/zenodo.1156633
10.5281/zenodo.8368904
10.3897/natureconservation.49.82931.suppl3
10.21203/rs.3.rs-1370209/v1
10.5281/zenodo.8010223
10.5281/zenodo.6380887
10.5281/zenodo.5500364
10.25914/604eb7628b4e7
10.3897/BDJ.8.e48623.suppl1
10.5281/zenodo.6390598
10.5281/zenodo.8181417
10.5281/zenodo.580042
10.5281/zenodo.8308027
10.5061/dryad.ttdz08m16
10.5281/zenodo.10568799
10.5061/dryad.2fqz612m4
10.5061/dryad.d51c5b019
10.5281/zenodo.6855728
10.5281/zenodo.10475278
10.5281/zenodo.1475906
10.5281/zenodo.8239429
10.5061/dryad.k31d4
10.5281/zenodo.7689499
10.5281/zenodo.5793694
10.5281/zenodo.4603535
10.5281/zenodo.8421879
10.7280/D1Z69G
10.5281/zenodo.5084367
10.5281/zenodo.4306051
10.5281/zenodo.6384945
10.5281/zenodo.3354711
10.5281/zenodo.6913393
10.5281/zenodo.8376503
10.5061/dryad.fbg79cnx2
10.5281/zenodo.10208773
10.5281/zenodo.3382123
10.5281/zenodo.7537055
10.5281/zenodo.4964225
10.5281/zenodo.4761331
10.3897/natureconservation.49.82931.suppl4
10.5281/zenodo.4761327
10.5281/zenodo.8303080
10.5281/zenodo.4486376
10.5061/dryad.3bk3j9kph
10.5281/zenodo.4761307
10.5281/zenodo.8341374
10.5281/zenodo.8353229
10.5281/zenodo.10067270
10.5281/zenodo.7385933
10.5281/zenodo.7796264
10.5061/dryad.q1d7f
10.5281/zenodo.2585685
10.5281/zenodo.4632200
10.5061/dryad.hmgqnk9hh
10.5061/dryad.2v1m1fj
10.5281/zenodo.6303309
10.5281/zenodo.6886498
10.5281/zenodo.3937672
10.5281/zenodo.3628018
10.5281/zenodo.7516852
10.5061/dryad.mgqnk98wm
10.5281/zenodo.5564457
10.5281/zenodo.6321323
10.5281/zenodo.4695961
10.5281/zenodo.2651652
10.5281/zenodo.10444954
10.5061/dryad.ttdz08m16
10.5281/zenodo.4761095
10.5281/zenodo.4310798
10.5281/zenodo.8085467
10.5281/zenodo.7310803
10.5281/zenodo.7092357
10.5281/zenodo.5243180
10.5281/zenodo.8144596
10.5281/zenodo.8147580
10.5281/zenodo.5724117
10.5281/zenodo.5189179
10.5281/zenodo.3832094
10.3897/rio.7.e77750.figure1
10.5281/zenodo.7855146
10.5281/zenodo.44754
10.5061/dryad.wh70rxwpz
10.5281/zenodo.4883250
10.5281/zenodo.10485583
10.3897/rio.7.e77750.figure5
10.5061/dryad.ksn02v75q
10.5061/dryad.wm37pvmkw
10.5281/zenodo.1476113
10.5281/zenodo.7025633
10.5281/zenodo.5535532
10.5281/zenodo.7798143
10.5281/zenodo.7122113
10.5281/zenodo.5237198
10.5281/zenodo.3813230
10.5281/zenodo.3707930
10.5281/zenodo.1199545
10.7280/D1Z69G
10.5281/zenodo.7763080
10.5281/zenodo.4421252
10.5281/zenodo.4591774
10.5281/zenodo.10392426
10.5281/zenodo.8362750
10.5281/zenodo.3872130
10.3897/subtbiol.25.23803.figure3
10.5281/zenodo.8086478
10.5281/zenodo.5729532
10.5281/zenodo.4964203
10.5281/zenodo.6315226
10.5281/zenodo.7057257
10.1021/acs.chemmater.2c00580
10.5281/zenodo.5682604
10.5281/zenodo.7269139
10.5281/zenodo.5645545
10.5281/zenodo.4573897
10.5281/zenodo.4760131
10.5281/zenodo.7547644
10.5281/zenodo.5373490
10.5281/zenodo.5158465
10.5281/zenodo.8421859
10.5281/zenodo.10394959
10.5281/zenodo.10440364
10.5061/dryad.cz8w9gj06
10.5281/zenodo.5148640
10.5281/zenodo.5723606
10.5281/zenodo.10020957
10.3897/BDJ.8.e48623.figure1
10.1088/1752-7163/aa9e71
10.5281/zenodo.7015277
10.5061/dryad.2z34tmpjg
10.5281/zenodo.7221105
10.5281/zenodo.7865424
10.5281/zenodo.4575211
10.5281/zenodo.4590885
10.5281/zenodo.5765565
10.5281/zenodo.6448390
10.5281/zenodo.8362722
10.5281/zenodo.3816663
10.5281/zenodo.2581296
10.5281/zenodo.7438422
10.3897/BDJ.8.e48623.suppl1
10.5281/zenodo.3819313
10.5281/zenodo.2540773
10.5281/zenodo.4761301
10.5061/dryad.wm37pvmkw
10.5281/zenodo.5527127
10.5281/zenodo.10222606
10.5281/zenodo.4603535
10.5281/zenodo.7762439
10.5281/zenodo.10551249
10.5281/zenodo.8363118
10.5281/zenodo.8283641
10.5281/zenodo.5526763
10.5281/zenodo.5243277
10.5281/zenodo.8247362
10.5061/dryad.c866t1g49
10.5281/zenodo.10204743
10.5281/zenodo.10142460
10.5281/zenodo.7327824
10.5061/dryad.4j0zpc8h2
10.5281/zenodo.10444954
10.5281/zenodo.6078514
10.5281/zenodo.7429639
10.5281/zenodo.3771710
10.5281/zenodo.3382123
10.5281/zenodo.7092357
10.5281/zenodo.7561767
10.5061/dryad.6c886
10.5281/zenodo.4079190
10.5061/dryad.c670tq2
10.5281/zenodo.5243362
10.5281/zenodo.5998113
10.5281/zenodo.7418361
10.5281/zenodo.5217997
10.5281/zenodo.5761723
10.5061/dryad.m1t32
10.5281/zenodo.4273949
10.5281/zenodo.8129629
10.5281/zenodo.10650737
10.5281/zenodo.4305970
10.5281/zenodo.10485579
10.5281/zenodo.1194786
10.5281/zenodo.10587576
10.5281/zenodo.8131976
10.5281/zenodo.7254133
10.5281/zenodo.4784408
10.5281/zenodo.6397629
10.5281/zenodo.3611936
10.5281/zenodo.7795898
\ No newline at end of file
0-collect-data/zenodo.py
View file @ 2bd53d86
......@@ -5,7 +5,6 @@
"""
## todo
- v2 : rechercher UGA comme financeur `grants.funder.doi`
- v2 : passer par les ORCID des `creator.orcid` et `contributors.orcid`
## Documentation
* Liste des métadonnées obligatoires lors du dépôts (upload_type, sub_type, publication_date, titre, creators, ) https://developers.zenodo.org/#representation
......@@ -13,12 +12,9 @@
* Doc dev API champs de requêtes classsiques https://developers.zenodo.org/#records
* doc champs poussées pour la recherche https://help.zenodo.org/guides/search/
* typologie des dépôts possiblent : publication: Publication, poster: Poster, presentation: Presentation, Dataset: Dataset, image: Image, video: Video/Audio, software: Software, lesson: Lesson, physicalobject: Physical object, other: Other
* descendre au niveau des ORCID des `creator.orcid` et `contributors.orcid`
### Identifier les dépôts :
- utilisation du champs `creators.affiliation` et contributor.affiliation (multi affiliation)
- utilisation de la forme "grenoble" uniquement, possibilité de bruit
## Notes sur la récupération
- exemple résultats de requete : https://zenodo.org/api/records?q=creators.affiliation%3A*grenoble*&type=dataset&page=6&size=100&sort=mostrecent&all_version=False
- deux DOI identiques sont présents : un à la racine `[hits][doi]` et un autre dans `[hits][metadata][doi]`
......@@ -29,13 +25,6 @@ import requests, json
print("\n\nRunning zenodo.py")
with open("personnal-keys.json") as f :
## load zenodo keys for requests the API
ACCESS_TOKEN = json.load(f)["ZENODO_KEY"]
def req_zenodo_with_page(uga_perimeter, record_type, page_nb) :
"""
retourne les jeux de données UGA depuis Zenodo
......@@ -51,7 +40,6 @@ def req_zenodo_with_page(uga_perimeter, record_type, page_nb) :
"size" : 100,
"sort" : "mostrecent",
"all_version" : False,
"access_tpoken" : ACCESS_TOKEN
}
)
# for debugging
......@@ -118,12 +106,20 @@ def req_zenodo(uga_perimeter, record_type) :
all_dois = set() # a set to gather all DOIs
uga_perimeter = "creators.affiliation:*grenoble* contributors.affiliation:*grenoble*"
uga_txt_query = "(\"grenoble alpes\" OR \"grenoble alps\" OR \"grenoble INP\" \
OR \"polytechnique de grenoble\" OR \"Grenoble Institute of Technology\" OR \"univeristé de grenoble\" )"
uga_query = f"creators.affiliation:{uga_txt_query} contributors.affiliation:{uga_txt_query}"
## memo 2024-02 two fields following by space will search in first field or in second field
## ne pas faire de recherche avec AND car ça recherche dans toutes les affiliations des auteurs
## SceincesPo Grenoble n'apporte pas de résultat https://zenodo.org/search?q=creators.affiliation%3A%28sciencespo%20AND%20Grenoble%29&q=type%3Adataset&l=list&p=1&s=10&sort=bestmatch
types_to_req = ["dataset", "image", "video", "software", "other"]
for record_type in types_to_req :
temp_dois = req_zenodo(uga_perimeter, record_type)
temp_dois = req_zenodo(uga_query, record_type)
## placer les DOI dans le buffer général qui ne peut contenir que des valeurs uniques
[all_dois.add(doi) for doi in temp_dois]
......
1-enrich-with-datacite/all_datacite_clients_for_uga.csv 0 → 100644
View file @ 2bd53d86
client,count,name,year,url
cern.zenodo,885,Zenodo,2013,https://zenodo.org/
inist.sshade,522,Solid Spectroscopy Hosting Architecture of Databases and Expertise,2019,https://www.sshade.eu/
figshare.ars,380,figshare Academic Research System,2016,http://figshare.com/
inist.osug,275,Observatoire des Sciences de l'Univers de Grenoble,2014,http://doi.osug.fr
dryad.dryad,168,DRYAD,2018,https://datadryad.org
inist.resif,99,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
rdg.prod,81,Recherche Data Gouv France,2022,https://recherche.data.gouv.fr/en
inist.humanum,75,NAKALA,2020,https://nakala.fr
inist.persyval,64,PERSYVAL-Lab : Pervasive Systems and Algorithms Lab,2016,
fmsh.prod,28,Fondation Maison des sciences de l'homme,2023,
inist.ccj,22,Centre Camille Jullian – UMR 7299,2020,
pangaea.repository,18,PANGAEA,2020,https://www.pangaea.de/
mcdy.dohrmi,14,dggv-e-publications,2020,https://www.dggv.de/publikationen/dggv-e-publikationen.html
inist.cirm,7,Centre International de Rencontres Mathématiques,2017,
figshare.sage,6,figshare SAGE Publications,2018,
iris.iris,5,NSF Seismological Facility for the Advancement of Geoscience (SAGE),2018,http://www.iris.edu/hq/
vqpf.dris,3,Direction des ressources et de l'information scientifique,2021,
tib.gfzbib,3,GFZpublic,2011,https://gfzpublic.gfz-potsdam.de
tib.repod,3,RepOD,2015,https://repod.icm.edu.pl/
cnic.sciencedb,3,ScienceDB,2022,https://www.scidb.cn/en
inist.eost,2,Ecole et Observatoire des Sciences de la Terre,2017,https://eost.unistra.fr/en/
tib.gfz,2,GFZ Data Services,2011,https://dataservices.gfz-potsdam.de/portal/
bl.mendeley,2,Mendeley Data,2015,https://data.mendeley.com/
bl.nerc,2,NERC Environmental Data Service,2011,https://eds.ukri.org
tug.openlib,2,TU Graz OPEN Library,2020,https://openlib.tugraz.at/
crui.ingv,2,Istituto Nazionale di Geofisica e Vulcanologia (INGV),2013,http://data.ingv.it/
ugraz.unipub,2,unipub,2019,http://unipub.uni-graz.at
ethz.sed,2,"Swiss Seismological Service, national earthquake monitoring and hazard center",2013,http://www.seismo.ethz.ch
inist.opgc,1,Observatoire de Physique du Globe de Clermont-Ferrand,2017,
ethz.da-rd,1,ETHZ Data Archive - Research Data,2013,http://data-archive.ethz.ch
ethz.zora,1,"Universität Zürich, ZORA",2013,https://www.zora.uzh.ch/
estdoi.ttu,1,TalTech,2019,https://digikogu.taltech.ee
repod.dbuw,1,University of Warsaw Research Data Repository,2023,https://danebadawcze.uw.edu.pl/
inist.ird,1,IRD,2016,
inist.omp,1,Observatoire Midi-Pyrénées,2011,
umass.uma,1,University of Massachusetts (UMass) Amherst,2018,https://scholarworks.umass.edu/
edi.edi,1,Environmental Data Initiative,2017,https://portal.edirepository.org/nis/home.jsp
bl.iita,1,International Institute of Tropical Agriculture datasets,2017,http://data.iita.org/
ardcx.nci,1,National Computational Infrastructure,2020,
ihumi.pub,1,IHU Méditerranée Infection,2020,
inist.inrap,1,Institut national de recherches archéologiques préventives,2019,
tib.mpdl,1,Max Planck Digital Library,2015,
tudublin.arrow,1,ARROW@TU Dublin,2020,https://arrow.dit.ie/
1-enrich-with-datacite/concatenate-enrich-dois.py
View file @ 2bd53d86
......@@ -20,7 +20,7 @@ print("\tDOIs to treat\t\t", len(dois))
## pour essayer avec un seul DOI
# temp_doi = dois[random.randint(0, len(dois))]
# #temp_doi = "10.57745/QYIAWX" - 10.25656/01:8509
# temp_doi = "10.57745/QYIAWX" - 10.25656/01:8509
# print(temp_doi)
# raw_metadatas = my_functions.get_md_from_datacite(temp_doi)
......@@ -28,6 +28,7 @@ doi_error = [] # retrieve doi error
temp_rows = [] # put data in dict before df
df_old = pd.read_csv("../dois-uga.csv")
print(f"\n\tnb of DOIs already treated\t{len(df_old)}")
# req dataCite and paste data following instructions
......@@ -52,10 +53,70 @@ for doi in dois : #[:300]
temp_rows.append(selected_md) ## ajouter ce dictionnaire à une liste
print(f"\tadded\t\t{doi}")
## if new datasets has been founded
if temp_rows :
df_fresh = pd.DataFrame(temp_rows)
df_out = pd.concat([df_old, df_fresh], ignore_index=True)
dois_added = list(df_old["doi"])
to_del = []
for i in range(0, len(df_fresh)):
result = my_functions.get_origin_version(df_fresh.loc[i, "doi"])
if result[0] not in dois_added:
dois_added.append(result[0])
df_fresh.loc[i, "doi"] = result[0]
if str(result[1]) != "[]": df_fresh.loc[i, "traveled_dois"] = str(result[1])
else: df_fresh.loc[i, "traveled_dois"] = ""
if str(result[2]) != "[]": df_fresh.loc[i, "all_relations"] = str(result[2])
else: df_fresh.loc[i, "all_relations"] = ""
else:
to_del.append(i)
df_fresh.drop(to_del, inplace=True)
print("Nombre de dois supprimés : " + str(len(to_del)))
print("Nb dois a garder : " + str(len(dois_added)))
df_concat = pd.concat([df_old, df_fresh], ignore_index=True)
## remove not wanted datacite type & clients
type_to_explude = ["Book", "ConferencePaper", "ConferenceProceeding", "JournalArticle", "BookChapter", "Service", "Preprint"]
clients_to_exclude = ["rg.rg", "inist.epure"]
df_out = df_concat[ ~df_concat["resourceTypeGeneral"].isin(type_to_explude) & ~df_concat["client"].isin(clients_to_exclude) ].copy()
## output main CSV
df_out.to_csv("../dois-uga.csv", index = False)
print(f"\n\nnb of doi exported \t{len(df_out)}")
# write the number of dois found in a file to display on the website
with open("nb-dois.txt", 'w') as outf :
outf.write(str(len(df_out)))
## output last 500 DOIs to make it easier to open in web tools
df_last_dois = df_out.sort_values(by = "created", ascending = False, inplace = False)[:500]
df_last_dois["created"] = df_last_dois["created"].str[:10]
df_last_dois[["doi", "client", "resourceTypeGeneral", "created", "publisher", "rights", "sizes"]].to_csv("../dois-uga--last-500.csv", index = False)
## for the website : output another csv with datacite client and number of datasets
df_client_raw = df_out["client"].value_counts().to_frame()
## get informations about each client
client_names = []
client_years = []
client_urls = []
for i in range(0, len(df_client_raw)):
client = df_client_raw.iloc[i].name
req = requests.get('https://api.datacite.org/clients?query=id:%22'+str(client)+'%22')
client_names.append(req.json()["data"][0]["attributes"]["name"])
client_years.append(req.json()["data"][0]["attributes"]["year"])
client_urls.append(req.json()["data"][0]["attributes"]["url"])
## add informations to the output csv
df_client_raw["name"] = client_names
df_client_raw["year"] = client_years
df_client_raw["url"] = client_urls
df_client_raw.to_csv("all_datacite_clients_for_uga.csv")
1-enrich-with-datacite/nb-dois.txt 0 → 100644
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2692
\ No newline at end of file
1-enrich-with-datacite/z_personal_functions.py
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import requests, json
# Fonction pour éviter la redondance des données associées à des DOIs différents mais pointant vers les mêmes fichiers :
# Dans Zenodo par exemple, il y a un DOI associé à chaque version d'un dépôt et il faut remonter au DOI "chapeau"
# Si le DOI "chapeau" obtenu ou un "is_identical_to" fait référence à un DOI déjà existant dans le csv, il doit être ignoré.
def get_origin_version(doi, history=[], first=True):
if first: history=[] # ligne ajoutée pour éviter certains soucis de cache où history n'est pas vide au premier appel de la fonction
req = requests.get( f"https://api.datacite.org/dois/{doi}" )
res = req.json()
final = []
result = (doi, history, final) # doi est le DOI qui sera ajouté au csv, history retrace les dois et les relations ayant permis les recherches et final enregistre les relations du doi final ajouté au csv
try:
related = res["data"]["attributes"]["relatedIdentifiers"] # test si des relations existent pour le doi courant
except:
pass # si pas de relation, on renvoie le doi courant
else:
ignore = False # ignore correspond à un doi ayant une version "chapeau" qui doit être trouvée. Le doi courant doit donc être ignoré
duplicate = False # duplicate correspond à un doi étant identique à un autre
for i in related:
final.append(i.get("relationType"))
if i.get("relationType") == "IsVersionOf" and i.get("relatedIdentifierType") == "DOI":
ignore = True
elem_to_save_i = i.get("relatedIdentifier")
history.append([i.get("relationType"), i.get("relatedIdentifier")])
if i.get("relationType") == "IsIdenticalTo" and i.get("relatedIdentifierType") == "DOI":
duplicate = True
elem_to_save_d = i.get("relatedIdentifier") # pas de symétrie pour les is_identical_to, donc il suffit de prendre l'autre (pas le doi courant) pour éviter les doublons
history.append([i.get("relationType"), i.get("relatedIdentifier")])
if duplicate and not(ignore):
result = (elem_to_save_d, history, final) # si identique mais pas de version chapeau on peut s'arrêter
if ignore: result = get_origin_version(elem_to_save_i, history, False) # si version chapeau, on avance sans regarder les identiques
return result
def get_md_from_datacite( doi ) :
"""
retrieve data research metadata from datacite
......@@ -179,5 +210,3 @@ def from_files_load_dois(files_name) :
return all_dois
2-produce-graph/all_datacite_clients.csv deleted 100644 → 0
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client,count
cern.zenodo,936
inist.sshade,468
inist.osug,238
figshare.ars,183
dryad.dryad,156
inist.resif,78
inist.persyval,55
rdg.prod,45
inist.humanum,28
figshare.sage,16
mcdy.dohrmi,12
rg.rg,4
vqpf.dris,3
tib.gfzbib,3
iris.iris,3
ugraz.unipub,2
inist.epure,2
bl.nerc,2
tib.repod,2
estdoi.ttu,1
inist.omp,1
tib.gfz,1
edi.edi,1
inist.opgc,1
bl.iita,1
ardcx.nci,1
umass.uma,1
crui.ingv,1
bl.mendeley,1
ethz.zora,1
inist.ird,1
inist.eost,1
ihumi.pub,1
tug.openlib,1
2-produce-graph/hist-evol-datasets-per-repo.png
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2-produce-graph/hist-evol-datasets-per-repo.py
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......@@ -37,7 +37,7 @@ def reduce_client(client) :
return "AAU"
if client == "rdg.prod" :
return "RDG"
return "Recherche Data Gouv"
if client == "inist.humanum" :
return "Nakala"
......@@ -94,8 +94,7 @@ for i, date in enumerate(df_evol.index) :
ax.set_xticks(x_idx_toshow)
ax.set_xticklabels(x_label_toshow, rotation=70, fontsize=10)
plt.title(f"Evolution of the quantity of UGA open datasets\n and distribution per repository", \
plt.title(f"Cumulative view of the quantity of UGA research data\n and distribution by repository", \
fontsize = 18, x = 0.5, y = 1.03, alpha = 0.8)
plt.suptitle(f"n = {len(df)}", fontsize = 12, x = 0.5, y = 0.87, alpha = 0.6)
......
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......@@ -53,7 +53,7 @@ ax.tick_params(axis='both', which='major', labelsize=8)
plt.legend(reverse = False)
plt.title(f"Datasets registered over the last 30 days\ndistributed by DataCite client",
plt.title(f"Research data registered over the last 30 days\ndistributed by DataCite client",
fontsize = 18, x = 0.5, y = 1.03, alpha = 0.8)
plt.suptitle(f"n = {len(df_last_weeks)}", fontsize = 12, x = 0.5, y = 0.89, alpha = 0.6)
......
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2-produce-graph/hist-quantity-year-type.py
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......@@ -17,7 +17,7 @@ df_year_type.index.rename("year", inplace = True)
## a set of color via plt
### see color palett https://matplotlib.org/stable/users/explain/colors/colormaps.html
colors = [plt.cm.tab20(i) for i in range(len(df_year_type.columns))]
colors = [plt.cm.Set3(i) for i in range(len(df_year_type.columns))]
ax = df_year_type.plot(
kind = "bar",
......@@ -39,7 +39,7 @@ plt.xlabel(None)
plt.legend(loc="center", reverse = True, bbox_to_anchor=(0.45, 0.65), fontsize = 10)
plt.title(f"Distribution of datasets by registration year\nand DataCite types",
plt.title(f"Distribution of research data by registration year\nand DataCite types",
fontsize = 18, x = 0.5, y = 1.03, alpha = 0.8)
plt.suptitle(f"n = {len(df)}", fontsize = 12, x = 0.5, y = 0.90, alpha = 0.6)
......
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