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  • chetouae/open-research-data-monitor-back
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.gitlab-ci.yml
View file @ 35ba6558
...@@ -3,29 +3,57 @@ image: python:3-alpine ...@@ -3,29 +3,57 @@ image: python:3-alpine
actualisation_dois: actualisation_dois:
only: 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 - schedules
- web - web
before_script: before_script:
# installations permettant de faire tourner git
- apk update - apk update
- apk add git openssh - apk add git openssh
script: script:
# installation des bibliothèques python et exécution du script
- pip install pandas - pip install pandas
- pip install requests - pip install requests
- pip install matplotlib - pip install matplotlib
- python run-all-codes.py - python run-all-codes.py
after_script: after_script:
# commit des changements suite à l'exécution du script
- git config user.name "${GITLAB_USER_NAME}" - git config user.name "${GITLAB_USER_NAME}"
- git config user.email "${GITLAB_USER_EMAIL}" - git config user.email "${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 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 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: artifacts:
# ajout des fichiers du dépôt qui ont été modifiés, au cas où un problème serait survenu dans "after_script"
paths: paths:
- dois-uga.csv - dois-uga.csv
- dois-uga--last-500.csv
- 2-produce-graph/hist-evol-datasets-per-repo.png - 2-produce-graph/hist-evol-datasets-per-repo.png
- 2-produce-graph/hist-quantity-year-type.png - 2-produce-graph/hist-quantity-year-type.png
- 2-produce-graph/pie--datacite-client.png - 2-produce-graph/pie--datacite-client.png
- 2-produce-graph/pie--datacite-type.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 @ 35ba6558
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10.26302/sshade/experiment_gl_20150102_1 10.26302/sshade/bandlist_abs_h2o_h2o-ih
10.17178/emaa_para-nh2_hyperfine_0d542831 10.6084/m9.figshare.24091564
10.6084/m9.figshare.21368764 10.26302/sshade/experiment_bms_20120101_001
10.26302/sshade/experiment_op_20171130_001 10.26302/sshade/bandlist_raman_spherocobaltite
10.6084/m9.figshare.21351214 10.26302/sshade/experiment_cl_20181201_01
10.7275/bp3n-mw53 10.6084/m9.figshare.24165071
10.17178/emaa_ch2nh_rotation_6a3d5ab2
10.26302/sshade/experiment_dt_20180117_007
10.17178/amma-catch.ce.run_odc
10.17178/amma-catch.cl.gwatwell_n
10.26302/sshade/experiment_kd_20170711
10.26302/sshade/experiment_dt_20171221_001
10.5281/zenodo.10587575
10.15778/resif.ya2009
10.18709/perscido.2022.06.ds370
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 10.5281/zenodo.10276480
10.26302/sshade/experiment_ml_20171219_003 10.6084/m9.figshare.12886126
10.25384/sage.22573164 10.6084/m9.figshare.22620043
10.26302/sshade/experiment_bs_20200217_100 10.3217/9rrz9-ywt68
10.17178/emaa_hco-plus_rotation_a861ef7a 10.17178/amma-catch.pa.schem_snfd
10.17178/zaa_soil_temp.gloria 10.6084/m9.figshare.c.5129698
10.26302/sshade/bandlist_raman_n2_alpha-n2 10.17178/ohmcv.dsd.vb3.12-16.1
10.26302/sshade/experiment_pa_20180702_001 10.26302/sshade/experiment_gl_20150103_1
10.7280/d1wt11 10.48380/2gzr-9q70
10.26302/sshade/experiment_bs_20120923_001 10.5281/zenodo.10715585
10.26302/sshade/bandlist_abs_c4h2_am-c4h2 10.6084/m9.figshare.22075664.v2
10.26302/sshade/experiment_soc_20181115_004 10.25364/19.2022.7.3
10.5061/dryad.k31d4 10.26302/sshade/experiment_rc_20191124_000
10.26302/sshade/experiment_ak_20141107_1 10.5061/dryad.t8534
10.48380/dggv-jsn0-1k58 10.6084/m9.figshare.16851093
10.18709/perscido.2020.05.ds314 10.15778/resif.xz2020
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
10.15778/resif.x12021 10.15778/resif.x12021
10.5281/zenodo.10069276 10.26302/sshade/experiment_cl_20180316_01
10.6084/m9.figshare.c.6712342 10.6084/m9.figshare.24202750
10.6084/m9.figshare.c.6627379 10.48390/0005-gz84
10.6084/m9.figshare.24202747 10.26302/sshade/experiment_ak_20141125_2
10.15778/resif.zh2003 10.26302/sshade/experiment_gm_20221013_001
10.6084/m9.figshare.c.6795368 10.6084/m9.figshare.c.6424523
10.26302/sshade/experiment_rc_20200506_000 10.6084/m9.figshare.c.6604292.v1
10.15778/resif.cl 10.6084/m9.figshare.23575360
10.5061/dryad.brv15dvcj 10.5061/dryad.d2547d810
10.5061/dryad.4f4qrfjjc 10.25384/sage.c.6377642
10.18709/perscido.2019.07.ds249 10.26302/sshade/experiment_zy_20180216_000
10.26302/sshade/bandlist_raman_otavite 10.6084/m9.figshare.c.6910186
10.17178/emaa_para-nhd2_hyperfine_3bc12b4c 10.6084/m9.figshare.22735402
10.26302/sshade/experiment_ak_20141113_1 10.26302/sshade/bandlist_abs_n2_beta-n2
10.17178/amma-catch.ae.h2oflux_ncw 10.5281/zenodo.10551644
10.15778/resif.zf2003 10.6084/m9.figshare.c.6592129
10.26302/sshade/experiment_hm_20171017_001 10.6084/m9.figshare.14870669.v1
10.7280/d1mm37 10.17178/cryobsclim.clb.muzelle
10.26302/sshade/bandlist_raman_kutnohorite 10.6084/m9.figshare.16786741.v1
10.26302/sshade/experiment_soc_20181115_003 10.17178/cryobsclim.clb.dem
10.6084/m9.figshare.22621187 10.5061/dryad.78642
10.17178/zaa_soil_temp.all 10.6084/m9.figshare.22609322
10.6084/m9.figshare.21432228 10.5061/dryad.dc25730
10.26302/sshade/experiment_nc_20181214_001 10.6084/m9.figshare.c.5105152
10.48380/6fh7-7m80 10.6084/m9.figshare.c.6575881.v1
10.17178/amma-catch.ce.sap_odc 10.17178/gnss.products.japan
10.5061/dryad.vx0k6djp4 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/amma-catch.cl.raind_gh
10.17178/emaa_para-(15n)h2d_rotation_e7374da5 10.6084/m9.figshare.c.5394711
10.17178/cryobsclim.cdp.2018.soil 10.26302/sshade/experiment_bs_20130125_001
10.26302/sshade/experiment_kd_20220525_002 10.26302/sshade/experiment_lm_20220304_mtkl
10.26302/sshade/bandlist_raman_vaterite 10.26302/sshade/experiment_lb_20191211_005
10.5281/zenodo.10400476 10.17178/emaa_para-h2c(18o)_rotation_8f130c2d
10.5061/dryad.7wm37pvx5 10.26302/sshade/experiment_sb_20200629_001
10.17178/emaa_para-h2(18o)_rotation_4ebe92f7 10.26302/sshade/bandlist_raman_siderite
10.5061/dryad.v41ns1rwx 10.57757/iugg23-4563
10.17178/amma-catch.ce.run_odc 10.26302/sshade/experiment_yd_20220602_010
10.26302/sshade/experiment_op_20201203_001 10.6084/m9.figshare.c.6272697.v1
10.15778/resif.6f2021 10.15778/resif.yr2013
10.26302/sshade/experiment_bs_20130215_001 10.17178/ohmcv.dsd.ale.12-16.1
10.17178/cryobsclim.clb.dome 10.7914/wqvg-2272
10.26302/sshade/experiment_cc_20180723_01 10.6084/m9.figshare.c.6771537.v1
10.26302/sshade/bandlist_raman_n2_beta-n2 10.17178/amma-catch.pa.met_snnr
10.6084/m9.figshare.24940485 10.26302/sshade/experiment_sb_20210115_001
10.17178/amma-catch.ae.h2oflux_g 10.6084/m9.figshare.20080019
10.26302/sshade/experiment_yd_20160401_001 10.6084/m9.figshare.c.5487977
10.17178/cryobsclim.clb.bso 10.26302/sshade/experiment_soc_20201214_001
10.26302/sshade/experiment_bs_20200422_100 10.26302/sshade/experiment_sb_20200403_001
10.17178/emaa_hcn_rotation_a4d98833 10.57757/iugg23-4534
10.6084/m9.figshare.23816832 10.6084/m9.figshare.22620043.v1
10.26302/sshade/experiment_cc_20180724_01 10.5061/dryad.xksn02vn4
10.26302/sshade/experiment_eb_20180618_001 10.7280/d1wt11
10.26302/sshade/experiment_dl_20181107_002 10.6084/m9.figshare.c.6604292
10.18709/perscido.2022.09.ds375 10.17178/emaa_n(15n)h-plus_hyperfine_4f2d77ce
10.25384/sage.c.6837354.v1 10.26302/sshade/experiment_tg_20181312_001
10.5061/dryad.c1jr3 10.17178/amma-catch.benin
10.26302/sshade/bandlist_abs_ch3oh_am-ch3oh 10.26302/sshade/experiment_lb_20180718_001
10.26302/sshade/experiment_zy_20180212_000 10.26302/sshade/experiment_op_20200212_001
10.6084/m9.figshare.22613069 10.17178/cryobsclim.clb.col
10.5061/dryad.pf420 10.25647/liepp.wp.36
10.17178/emaa_sh-plus_hyperfine_da4d4f7a 10.17178/amma-catch.ce.sw_nc
10.26302/sshade/experiment_op_20230220_001 10.26302/sshade/experiment_jg_20091028_001
10.5281/zenodo.10048443 10.26302/sshade/experiment_sb_20210324_001
10.6084/m9.figshare.22620031 10.6084/m9.figshare.23575384
10.17178/emaa_para-h2(13c)o_rotation_903f6446 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 10.5281/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 @ 35ba6558
...@@ -12,8 +12,8 @@ https://support.datacite.org/docs/can-i-see-more-detailed-affiliation-informatio ...@@ -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` * mémo : pour descendre au niveau des auteurs , le filtre `?person-id=orcid-nb`
* nb 2024-01-13 * note 2023-02
retrait de l'AAU (jbru.aau) car tout est du PD de congrès retrait de l'AAU (jbru.aau) car tout est du PDF de congrès
ajout de client.uid:inist.sshade et client.uid:inist.resif ajout de client.uid:inist.sshade et client.uid:inist.resif
""" """
...@@ -125,19 +125,6 @@ if main_no_dois : ...@@ -125,19 +125,6 @@ if main_no_dois :
print("datasets with an other identifier than DOI has been finded") print("datasets with an other identifier than DOI has been finded")
[print(f"\t\t{elem}") for elem in main_no_dois] [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 ## __________n__________ remove duplicates
unique_dois = list(set(main_dois)) unique_dois = list(set(main_dois))
print(f"\tNb of unique DOI\t{len(unique_dois)}") print(f"\tNb of unique DOI\t{len(unique_dois)}")
......
0-collect-data/nakala-uga-users.txt
View file @ 35ba6558
...@@ -17,4 +17,18 @@ mbeligne ...@@ -17,4 +17,18 @@ mbeligne
acarbonnelle acarbonnelle
annegf annegf
tleduc tleduc
abey abey
\ No newline at end of file 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 @ 35ba6558
...@@ -21,16 +21,16 @@ urls = [ ...@@ -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=authorAffiliation%3AUGA',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=producerAffiliation%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=contributorAffiliation%3AUGA',
'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=datasetContactAffiliation%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%3AGrenoble', '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%3AGrenoble', '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%3AGrenoble' 'https://entrepot.recherche.data.gouv.fr/api/search?q=*&fq=contributorAffiliation%3A(Grenoble AND Alpes)'
# possiblilité d'ajouter d'autres requêtes # possiblilité d'ajouter d'autres requêtes
] ]
# on définit une fonction pour la lancer la requete avec chaque url pour les différentes affiliations # on définit une fonction pour la lancer la requete avec chaque url pour les différentes affiliations
def get_results(url): def get_results(url):
req = requests.get(url) req = requests.get(url+"&type=dataset")
#print(req.url) #print(req.url)
results = [req.json()] results = [req.json()]
...@@ -39,7 +39,7 @@ def get_results(url): ...@@ -39,7 +39,7 @@ def get_results(url):
count = nb_res count = nb_res
page = 1 page = 1
while(nb_res > 0): while(nb_res > 0):
newurl = url+"&start="+str(count) newurl = url+"&type=dataset"+"&start="+str(count)
req = requests.get(newurl) req = requests.get(newurl)
results.append(req.json()) results.append(req.json())
nb_res = results[page]["data"]["count_in_response"] nb_res = results[page]["data"]["count_in_response"]
...@@ -59,7 +59,7 @@ def get_dois(results): ...@@ -59,7 +59,7 @@ def get_dois(results):
nb_dois += len(num_dois) nb_dois += len(num_dois)
for item in 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)) print("\tnb DOIs\t\t" + str(nb_dois))
return dois return dois
......
0-collect-data/zenodo-dois.txt
View file @ 35ba6558
10.5281/zenodo.4760491 10.5281/zenodo.7275468
10.5061/dryad.rxwdbrvbg 10.5281/zenodo.8319001
10.5281/zenodo.3899776 10.5281/zenodo.6793401
10.5281/zenodo.7418361
10.5281/zenodo.1189443
10.5281/zenodo.3384633
10.5281/zenodo.10058946
10.5281/zenodo.7495559
10.5061/dryad.v475g 10.5061/dryad.v475g
10.5281/zenodo.5336853 10.5281/zenodo.3354711
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.5717483 10.5281/zenodo.5717483
10.5281/zenodo.7342481 10.5061/dryad.pc866t1k5
10.5281/zenodo.8271248 10.5281/zenodo.8410346
10.5061/dryad.6t1g1jx0m 10.3897/rio.7.e77750.figure8b
10.5281/zenodo.7689499 10.5281/zenodo.4632426
10.5281/zenodo.4760487 10.5061/dryad.mw6m905tc
10.5281/zenodo.7472518 10.5281/zenodo.8147580
10.5281/zenodo.6078514 10.5281/zenodo.7308352
10.3897/phytokeys.95.22324.figure1 10.5281/zenodo.8101891
10.5281/zenodo.1185426 10.5281/zenodo.5145755
10.5281/zenodo.3817352 10.5281/zenodo.4048589
10.5281/zenodo.3817437 10.5061/dryad.3bk3j9kph
10.5281/zenodo.7007289 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 10.5061/dryad.6226d
10.5281/zenodo.7143466 10.5281/zenodo.6104368
10.5281/zenodo.8247362 10.5281/zenodo.4776419
10.5281/zenodo.8392608 10.5281/zenodo.3759385
10.5281/zenodo.7307563 10.5281/zenodo.5243189
10.5281/zenodo.6401829 10.5281/zenodo.4603782
10.5281/zenodo.5237198 10.5281/zenodo.10341149
10.5281/zenodo.4759489 10.5281/zenodo.7114168
10.5281/zenodo.8390942 10.5281/zenodo.4790667
10.5061/dryad.5s77q 10.5281/zenodo.5842110
10.5281/zenodo.2641991 10.5281/zenodo.6671769
10.5281/zenodo.4761347 10.5281/zenodo.2575055
10.5281/zenodo.7558920 10.5281/zenodo.6221153
10.7280/D1667W 10.5281/zenodo.7961207
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10.5281/zenodo.5024655
10.5281/zenodo.61089 10.5281/zenodo.61089
10.5281/zenodo.2677907
10.5281/zenodo.7590005
10.5281/zenodo.4745568
10.5061/dryad.fbg79cnx2
10.5281/zenodo.5243356
10.5281/zenodo.5024655
10.5281/zenodo.3813574
10.5281/zenodo.8316094
10.5281/zenodo.7180985
10.5061/dryad.8gtht76tq
10.5281/zenodo.5999123
10.5281/zenodo.8057992
10.5281/zenodo.5243218
10.5281/zenodo.4603774
10.5281/zenodo.4442416
10.5281/zenodo.10211920
10.5281/zenodo.5744873
10.5281/zenodo.10005463
10.5061/dryad.4n5929c
10.5281/zenodo.10213989
10.5281/zenodo.3862151
10.1002/essoar.10508001.1
10.5281/zenodo.7372398
10.5281/zenodo.7627946
10.5281/zenodo.4629216
10.5061/dryad.5b58400
10.5281/zenodo.10037956
10.5281/zenodo.6506336
10.5281/zenodo.7135091
10.5281/zenodo.3257654
10.5281/zenodo.4010153
10.5061/dryad.612jm643q
10.5061/dryad.b8gtht78h 10.5061/dryad.b8gtht78h
10.5281/zenodo.4759505 10.5281/zenodo.6939154
10.5281/zenodo.5060328 10.5281/zenodo.4763381
10.5281/zenodo.7986836 10.5281/zenodo.3635402
10.5061/dryad.4f4qrfjjc 10.5281/zenodo.6324930
10.5281/zenodo.10117556 10.5281/zenodo.8139775
10.5281/zenodo.7993122 10.5281/zenodo.4776977
10.5281/zenodo.5647786
10.3897/natureconservation.49.82931.suppl1 10.3897/natureconservation.49.82931.suppl1
10.5281/zenodo.6860527 10.5281/zenodo.5062314
10.5281/zenodo.5865001 10.5281/zenodo.4059193
10.5281/zenodo.8225005 10.5061/dryad.3r2280ggb
10.5281/zenodo.4546112 10.3897/rio.7.e77750.figure4
10.5281/zenodo.5142591 10.5281/zenodo.10058946
10.5061/dryad.jm63xsj7b 10.5281/zenodo.999197
10.5281/zenodo.5242859 10.5281/zenodo.832421
10.5061/dryad.sq67g 10.5281/zenodo.8265979
10.5281/zenodo.6565895 10.5061/dryad.8cz8w9gqs
10.5281/zenodo.7762439 10.5281/zenodo.10419097
10.5281/zenodo.8296848 10.5061/dryad.d2547d810
10.5281/zenodo.8226430 10.5281/zenodo.7331741
10.5281/zenodo.3135479 10.5281/zenodo.10469400
10.5281/zenodo.8269855 10.5061/dryad.283pp
10.3897/neobiota.70.65454.suppl2 10.5281/zenodo.3759301
10.5281/zenodo.4067946 10.5281/zenodo.7855146
10.5281/zenodo.6985564 10.5281/zenodo.10133203
10.5281/zenodo.10020919 10.5281/zenodo.5788695
10.5281/zenodo.6861341 10.5281/zenodo.3613951
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.1112342 10.5281/zenodo.1112342
10.5281/zenodo.8404376 10.5281/zenodo.8252180
10.5281/zenodo.10062356 10.5281/zenodo.6806404
10.5281/zenodo.5905877 10.5281/zenodo.6985564
10.5281/zenodo.4761305 10.5061/dryad.1qt12
10.5281/zenodo.7982052 10.5281/zenodo.10199624
10.5281/zenodo.4761335 10.5281/zenodo.7813697
10.5281/zenodo.6786833 10.5281/zenodo.8206446
10.3897/alpento.2.23041.figure3 10.5281/zenodo.4729758
10.5281/zenodo.3550192 10.3897/natureconservation.49.82931.suppl4
10.5061/dryad.9w0vt4bd1 10.5281/zenodo.5237188
10.5281/zenodo.2641214 10.5281/zenodo.7119220
10.5281/zenodo.4761355 10.5281/zenodo.8408515
10.5281/zenodo.1307653 10.5281/zenodo.8014905
10.5281/zenodo.8272736 10.5281/zenodo.3345743
10.5281/zenodo.7835266 10.5281/zenodo.6526421
10.5281/zenodo.7372398 10.5281/zenodo.3981252
10.5281/zenodo.6405782
10.5281/zenodo.4764317
10.5281/zenodo.4724109
10.5281/zenodo.4745564 10.5281/zenodo.4745564
10.5281/zenodo.5243264 10.5281/zenodo.3817352
10.5281/zenodo.1256648 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 10.5281/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 10.7280/D1B114
10.5281/zenodo.8014905 10.5281/zenodo.8095047
10.5281/zenodo.8421859 10.5281/zenodo.8083133
10.5281/zenodo.4761299 10.5281/zenodo.6461645
10.3897/BDJ.8.e48623.figure1 10.5281/zenodo.6974319
10.5281/zenodo.5723996 10.5281/zenodo.10679304
10.5281/zenodo.4265431 10.5281/zenodo.8324811
10.1002/essoar.10508001.1 10.5281/zenodo.4067946
10.5061/dryad.k98sf7m98 10.5281/zenodo.4916120
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.3625778 10.5281/zenodo.3625778
10.5281/zenodo.8171326 10.5281/zenodo.8368904
10.5281/zenodo.5999123 10.3897/natureconservation.49.82931.suppl3
10.5281/zenodo.3463483 10.21203/rs.3.rs-1370209/v1
10.5281/zenodo.8008238 10.5281/zenodo.8010223
10.5281/zenodo.1156633 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.5793694
10.5281/zenodo.4603535 10.5281/zenodo.4632200
10.5281/zenodo.8421879 10.5061/dryad.hmgqnk9hh
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
10.5061/dryad.2v1m1fj 10.5061/dryad.2v1m1fj
10.5281/zenodo.6303309 10.5061/dryad.wh70rxwpz
10.5281/zenodo.6886498 10.5281/zenodo.4883250
10.5281/zenodo.3937672 10.5281/zenodo.10485583
10.5281/zenodo.3628018 10.3897/rio.7.e77750.figure5
10.5281/zenodo.7516852 10.5061/dryad.ksn02v75q
10.5061/dryad.mgqnk98wm 10.5061/dryad.wm37pvmkw
10.5281/zenodo.5564457 10.5281/zenodo.1476113
10.5281/zenodo.6321323 10.5281/zenodo.7025633
10.5281/zenodo.4695961 10.5281/zenodo.5535532
10.5281/zenodo.2651652 10.5281/zenodo.7798143
10.5281/zenodo.10444954 10.5281/zenodo.7122113
10.5061/dryad.ttdz08m16 10.5281/zenodo.5237198
10.5281/zenodo.4761095 10.5281/zenodo.3813230
10.5281/zenodo.4310798 10.5281/zenodo.3707930
10.5281/zenodo.8085467 10.5281/zenodo.1199545
10.5281/zenodo.7310803 10.7280/D1Z69G
10.5281/zenodo.7092357 10.5281/zenodo.7763080
10.5281/zenodo.5243180 10.5281/zenodo.4421252
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
10.5281/zenodo.4591774 10.5281/zenodo.4591774
10.5281/zenodo.10392426 10.5281/zenodo.8421859
10.5281/zenodo.8362750 10.5281/zenodo.10394959
10.5281/zenodo.3872130 10.5281/zenodo.10440364
10.3897/subtbiol.25.23803.figure3 10.5061/dryad.cz8w9gj06
10.5281/zenodo.8086478 10.5281/zenodo.5148640
10.5281/zenodo.5729532 10.5281/zenodo.5723606
10.5281/zenodo.4964203 10.5281/zenodo.10020957
10.5281/zenodo.6315226 10.3897/BDJ.8.e48623.figure1
10.5281/zenodo.7057257 10.1088/1752-7163/aa9e71
10.1021/acs.chemmater.2c00580 10.5281/zenodo.7015277
10.5281/zenodo.5682604 10.5061/dryad.2z34tmpjg
10.5281/zenodo.7269139 10.5281/zenodo.7221105
10.5281/zenodo.5645545 10.5281/zenodo.7865424
10.5281/zenodo.4573897 10.5281/zenodo.4575211
10.5281/zenodo.4760131 10.5281/zenodo.4590885
10.5281/zenodo.7547644 10.5281/zenodo.5765565
10.5281/zenodo.5373490 10.5281/zenodo.6448390
10.5281/zenodo.5158465
10.5281/zenodo.8362722 10.5281/zenodo.8362722
10.5281/zenodo.3816663 10.5281/zenodo.5527127
10.5281/zenodo.2581296 10.5281/zenodo.10222606
10.5281/zenodo.7438422 10.5281/zenodo.4603535
10.3897/BDJ.8.e48623.suppl1 10.5281/zenodo.7762439
10.5281/zenodo.3819313 10.5281/zenodo.10551249
10.5281/zenodo.2540773 10.5281/zenodo.8363118
10.5281/zenodo.4761301 10.5281/zenodo.8283641
10.5061/dryad.wm37pvmkw 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 @ 35ba6558
...@@ -5,7 +5,6 @@ ...@@ -5,7 +5,6 @@
""" """
## todo ## todo
- v2 : rechercher UGA comme financeur `grants.funder.doi` - v2 : rechercher UGA comme financeur `grants.funder.doi`
- v2 : passer par les ORCID des `creator.orcid` et `contributors.orcid`
## Documentation ## Documentation
* Liste des métadonnées obligatoires lors du dépôts (upload_type, sub_type, publication_date, titre, creators, ) https://developers.zenodo.org/#representation * Liste des métadonnées obligatoires lors du dépôts (upload_type, sub_type, publication_date, titre, creators, ) https://developers.zenodo.org/#representation
...@@ -13,12 +12,9 @@ ...@@ -13,12 +12,9 @@
* Doc dev API champs de requêtes classsiques https://developers.zenodo.org/#records * Doc dev API champs de requêtes classsiques https://developers.zenodo.org/#records
* doc champs poussées pour la recherche https://help.zenodo.org/guides/search/ * 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 * 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 ## 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 - 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]` - 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 ...@@ -29,13 +25,6 @@ import requests, json
print("\n\nRunning zenodo.py") 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) : def req_zenodo_with_page(uga_perimeter, record_type, page_nb) :
""" """
retourne les jeux de données UGA depuis Zenodo retourne les jeux de données UGA depuis Zenodo
...@@ -51,7 +40,6 @@ def req_zenodo_with_page(uga_perimeter, record_type, page_nb) : ...@@ -51,7 +40,6 @@ def req_zenodo_with_page(uga_perimeter, record_type, page_nb) :
"size" : 100, "size" : 100,
"sort" : "mostrecent", "sort" : "mostrecent",
"all_version" : False, "all_version" : False,
"access_tpoken" : ACCESS_TOKEN
} }
) )
# for debugging # for debugging
...@@ -118,12 +106,20 @@ def req_zenodo(uga_perimeter, record_type) : ...@@ -118,12 +106,20 @@ def req_zenodo(uga_perimeter, record_type) :
all_dois = set() # a set to gather all DOIs 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"] types_to_req = ["dataset", "image", "video", "software", "other"]
for record_type in types_to_req : 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 ## placer les DOI dans le buffer général qui ne peut contenir que des valeurs uniques
[all_dois.add(doi) for doi in temp_dois] [all_dois.add(doi) for doi in temp_dois]
......
1-enrich-with-datacite/all_datacite_clients_for_uga.csv 0 → 100644
View file @ 35ba6558
client,count,name,year,url
cern.zenodo,894,Zenodo,2013,https://zenodo.org/
inist.sshade,527,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,101,Réseau sismologique et géodésique français,2014,https://www.resif.fr/
rdg.prod,84,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/
tib.repod,4,RepOD,2015,https://repod.icm.edu.pl/
vqpf.dris,3,Direction des ressources et de l'information scientifique,2021,
tib.gfzbib,3,GFZpublic,2011,https://gfzpublic.gfz-potsdam.de
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 @ 35ba6558
...@@ -20,7 +20,7 @@ print("\tDOIs to treat\t\t", len(dois)) ...@@ -20,7 +20,7 @@ print("\tDOIs to treat\t\t", len(dois))
## pour essayer avec un seul DOI ## pour essayer avec un seul DOI
# temp_doi = dois[random.randint(0, len(dois))] # 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) # print(temp_doi)
# raw_metadatas = my_functions.get_md_from_datacite(temp_doi) # raw_metadatas = my_functions.get_md_from_datacite(temp_doi)
...@@ -28,6 +28,7 @@ doi_error = [] # retrieve doi error ...@@ -28,6 +28,7 @@ doi_error = [] # retrieve doi error
temp_rows = [] # put data in dict before df temp_rows = [] # put data in dict before df
df_old = pd.read_csv("../dois-uga.csv") df_old = pd.read_csv("../dois-uga.csv")
print(f"\n\tnb of DOIs already treated\t{len(df_old)}") print(f"\n\tnb of DOIs already treated\t{len(df_old)}")
# req dataCite and paste data following instructions # req dataCite and paste data following instructions
...@@ -52,10 +53,70 @@ for doi in dois : #[:300] ...@@ -52,10 +53,70 @@ for doi in dois : #[:300]
temp_rows.append(selected_md) ## ajouter ce dictionnaire à une liste temp_rows.append(selected_md) ## ajouter ce dictionnaire à une liste
print(f"\tadded\t\t{doi}") print(f"\tadded\t\t{doi}")
## if new datasets has been founded
if temp_rows : if temp_rows :
df_fresh = pd.DataFrame(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) df_out.to_csv("../dois-uga.csv", index = False)
print(f"\n\nnb of doi exported \t{len(df_out)}") 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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2712
\ No newline at end of file
1-enrich-with-datacite/z_personal_functions.py
View file @ 35ba6558
import requests, json 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 ) : def get_md_from_datacite( doi ) :
""" """
retrieve data research metadata from datacite retrieve data research metadata from datacite
...@@ -179,5 +210,3 @@ def from_files_load_dois(files_name) : ...@@ -179,5 +210,3 @@ def from_files_load_dois(files_name) :
return all_dois return all_dois
2-produce-graph/all_datacite_clients.csv deleted 100644 → 0
View file @ 28ac4b6d
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
View file @ 35ba6558
...@@ -37,7 +37,7 @@ def reduce_client(client) : ...@@ -37,7 +37,7 @@ def reduce_client(client) :
return "AAU" return "AAU"
if client == "rdg.prod" : if client == "rdg.prod" :
return "RDG" return "Recherche Data Gouv"
if client == "inist.humanum" : if client == "inist.humanum" :
return "Nakala" return "Nakala"
...@@ -94,8 +94,7 @@ for i, date in enumerate(df_evol.index) : ...@@ -94,8 +94,7 @@ for i, date in enumerate(df_evol.index) :
ax.set_xticks(x_idx_toshow) ax.set_xticks(x_idx_toshow)
ax.set_xticklabels(x_label_toshow, rotation=70, fontsize=10) ax.set_xticklabels(x_label_toshow, rotation=70, fontsize=10)
plt.title(f"Cumulative view of the quantity of UGA research data\n and distribution by repository", \
plt.title(f"Evolution of the quantity of UGA open datasets\n and distribution per repository", \
fontsize = 18, x = 0.5, y = 1.03, alpha = 0.8) 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) plt.suptitle(f"n = {len(df)}", fontsize = 12, x = 0.5, y = 0.87, alpha = 0.6)
......
2-produce-graph/hist-last-datasets-by-client.png
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2-produce-graph/hist-last-datasets-by-client.py
View file @ 35ba6558
...@@ -53,7 +53,7 @@ ax.tick_params(axis='both', which='major', labelsize=8) ...@@ -53,7 +53,7 @@ ax.tick_params(axis='both', which='major', labelsize=8)
plt.legend(reverse = False) 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) 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) plt.suptitle(f"n = {len(df_last_weeks)}", fontsize = 12, x = 0.5, y = 0.89, alpha = 0.6)
......
2-produce-graph/hist-quantity-year-type.png
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2-produce-graph/hist-quantity-year-type.py
View file @ 35ba6558
...@@ -17,7 +17,7 @@ df_year_type.index.rename("year", inplace = True) ...@@ -17,7 +17,7 @@ df_year_type.index.rename("year", inplace = True)
## a set of color via plt ## a set of color via plt
### see color palett https://matplotlib.org/stable/users/explain/colors/colormaps.html ### 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( ax = df_year_type.plot(
kind = "bar", kind = "bar",
...@@ -39,7 +39,7 @@ plt.xlabel(None) ...@@ -39,7 +39,7 @@ plt.xlabel(None)
plt.legend(loc="center", reverse = True, bbox_to_anchor=(0.45, 0.65), fontsize = 10) 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) 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) plt.suptitle(f"n = {len(df)}", fontsize = 12, x = 0.5, y = 0.90, alpha = 0.6)
......
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