This HTML5 document contains 98 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
wdthttp://www.wikidata.org/prop/direct/
wdtnhttp://www.wikidata.org/prop/direct-normalized/
n10http://dx.doi.org/10.1016/
schemahttp://schema.org/
rdfshttp://www.w3.org/2000/01/rdf-schema#
skoshttp://www.w3.org/2004/02/skos/core#
n4https://mbexc.uni-goettingen.de/literature/publications/
wikibasehttp://wikiba.se/ontology#
phttp://www.wikidata.org/prop/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
xsdhhttp://www.w3.org/2001/XMLSchema#
wdshttp://www.wikidata.org/entity/statement/
wdhttp://www.wikidata.org/entity/

Statements

Subject Item
wd:Q113319081
rdf:type
wikibase:Item
schema:description
articol științific مقالة بحثية نشرت في يوليو 2022 teaduslik artikkel 2022年學術文章 2022년 논문 научна статия 2022年学术文章 artigo científico wissenschaftlicher Artikel 2022年の論文 scientific article published in July 2022 article scientifique naučni članak artykuł naukowy научни чланак სამეცნიერო სტატია artikull shkencor επιστημονικό άρθρο tieteellinen artikkeli գիտական հոդված հրատարակված 2022 թվականի հուլիսի 1-ին article scientific scienca artikolo articolo scientifico artículo científico publicado en 2022 bài báo khoa học 2022年學術文章 artigo científico scientific article published on 01 July 2022 bilimsel makale 2022年學術文章 2022年学术文章 artigo científico vitskapeleg artikkel мақолаи илмӣ 2022年學術文章 научни чланак vetenskaplig artikel מאמר מדעי article científic artikel ilmiah scientific article published in July 2022 наукова стаття, опублікована в липні 2022 videnskabelig artikel udgivet juli 2022 2022 nî lūn-bûn wetenschappelijk artikel 2022年學術文章 научная статья 2022年学术文章 2022年学术文章 artículu científicu vitenskapelig artikkel 2022年学术文章 vědecký článek vedecký článok জুলাই ২০২২-এ প্রকাশিত বৈজ্ঞানিক নিবন্ধ 2022年学术文章 tudományos cikk artikulong pang-agham บทความทางวิทยาศาสตร์
p:P577
wds:Q113319081-9A965550-7B43-4FEB-92B0-701B47A913A2
wdt:P577
2022-07-01T00:00:00Z
p:P2860
wds:Q113319081-BFB85B88-8DF2-4333-A2F6-BC43657E745C
wdt:P2860
wd:Q111151652
p:P2093
wds:Q113319081-F9429A20-7BF4-4420-A49C-21FC33815B56 wds:Q113319081-A6211821-D35D-4774-B1B7-545B69F2E0F2 wds:Q113319081-BE267E96-BB40-47DA-9B26-14A71E3B7224 wds:Q113319081-7E11728E-BDD6-47C7-8858-690FB64748B4 wds:Q113319081-95A516D7-1D4E-489F-9235-9CD031659D45 wds:Q113319081-3651984D-E862-48EA-BA02-BD7E73F6532B wds:Q113319081-3169D0D6-C5C3-4B41-B700-7A996A9E0C45
wdt:P2093
Wojciech Wietrzynski Ricardo D. Righetto Antonio Martinez-Sanchez Tingying Peng Matthias Pöge Benjamin D. Engel Lorenz Lamm
rdfs:label
MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms
skos:prefLabel
MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms
schema:name
MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms
p:P1476
wds:Q113319081-182E5E2E-ED26-49CC-B370-79C13AA6AC34
wdt:P1476
MemBrain: A deep learning-aided pipeline for detection of membrane proteins in Cryo-electron tomograms
p:P304
wds:Q113319081-0F423223-1822-49D3-BE10-78F7C68A564A
wdt:P304
106990
p:P31
wds:Q113319081-33B2F64B-4A6D-4463-B7E8-35A05F45C862
wdt:P31
wd:Q13442814
p:P1433
wds:Q113319081-D431EE7D-18CA-45B7-92E1-95DF1B4C1E94
wdt:P1433
wd:Q15751136
p:P478
wds:Q113319081-9ACD43AE-214D-436D-AB78-2BF7D6C91FFE
wdt:P478
224
p:P356
wds:Q113319081-7865269E-B57B-4CA5-81E1-68ABF8F8C77A
wdtn:P356
n10:J.CMPB.2022.106990
wdt:P356
10.1016/J.CMPB.2022.106990
p:P856
wds:Q113319081-7083560b-4ded-5f96-348f-7dc6d71fabab
p:P859
wds:Q113319081-2020b52a-48fa-11b4-7f90-8fe2b41308f0
wdt:P856
n4:511
wdt:P859
wd:Q96756740