PyScopus: Quick Start
PyScopus is a Python wrapper of Elsevier Scopus API. More details of this Python package can be found here.
Import Scopus class and initialize with your own API Key
In [4]:
import pyscopus
pyscopus.__version__
Out[4]:
'1.0.3a1'
In [32]:
from pyscopus import Scopus
In [33]:
key = 'YOUR_OWN_API'
In [34]:
scopus = Scopus(key)
In [8]:
search_df = scopus.search("KEY(topic modeling)", count=30)
In [9]:
print(search_df.head(10))
scopus_id title \
0 85059802501 A hybrid IT framework for identifying high-qua...
1 85059563587 Knowledge empowered prominent aspect extractio...
2 85056216742 Subevents detection through topic modeling in ...
3 85059799573 An overview of statistical methods for handlin...
4 85056478111 Content features of tweets for effective commu...
5 85059561429 Variational-based latent generalized Dirichlet...
6 85055869272 Integrating Topic, Sentiment, and Syntax for M...
7 85058006073 Identifying spatial interaction patterns of ve...
8 85057796067 Topical Co-Attention Networks for hashtag reco...
9 85055701263 High-level event identification in social media
publication_name issn isbn \
0 International Journal of Information Management 02684012 None
1 Information Processing and Management 03064573 None
2 Future Generation Computer Systems 0167739X None
3 Journal of Clinical Epidemiology 08954356 None
4 International Journal of Information Management 02684012 None
5 Neurocomputing 09252312 None
6 Journal of Computing and Information Science i... 15309827 None
7 Computers, Environment and Urban Systems 01989715 None
8 Neurocomputing 09252312 None
9 Concurrency Computation 15320626 None
eissn volume page_range cover_date \
0 None 47 65-75 2019-08-01
1 None 56 408-423 2019-05-01
2 None 93 290-303 2019-04-01
3 18785921 108 121-131 2019-04-01
4 None 45 56-68 2019-04-01
5 18728286 332 372-395 2019-03-07
6 None 19 None 2019-03-01
7 None 74 50-61 2019-03-01
8 18728286 331 356-365 2019-02-28
9 15320634 31 None 2019-02-10
doi citation_count \
0 10.1016/j.ijinfomgt.2019.01.005 0
1 10.1016/j.ipm.2018.11.006 0
2 10.1016/j.future.2018.09.008 0
3 10.1016/j.jclinepi.2018.12.002 0
4 10.1016/j.ijinfomgt.2018.10.012 0
5 10.1016/j.neucom.2018.12.046 0
6 10.1115/1.4041475 0
7 10.1016/j.compenvurbsys.2018.12.001 0
8 10.1016/j.neucom.2018.11.057 0
9 10.1002/cpe.4668 1
affiliation aggregation_type \
0 [{'name': 'Wuhan University', 'city': 'Wuhan',... Journal
1 [{'name': 'Shanghai Jiao Tong University', 'ci... Journal
2 [{'name': 'Universidade Federal do Rio de Jane... Journal
3 [{'name': 'University of Exeter', 'city': 'Exe... Journal
4 [{'name': 'California State University Chico',... Journal
5 [{'name': 'Universite Concordia', 'city': 'Mon... Journal
6 [{'name': 'Chongqing Technology and Business U... Journal
7 [{'name': 'Shenzhen Institute of Advanced Tech... Journal
8 [{'name': 'Northeast Forestry University', 'ci... Journal
9 [{'name': 'Mongolian University of Science and... Journal
subtype_description authors \
0 Article [57205380806, 57205380806, 56192958200, 570355...
1 Article [57193132691, 57198740159, 8857364700]
2 Article [56319064100, 7202452493]
3 Review [55173782900, 57205245086, 8860721500, 5720537...
4 Article [57202449048, 39861640600, 57202457040, 572031...
5 Article [56462945200, 6603545988]
6 Article [57203987692, 36767821000, 35206193200, 572012...
7 Article [55729555700, 55729555700, 55876526300, 560479...
8 Article [56520462300, 35080762900, 57204907011, 561960...
9 Conference Paper [56023841200, 37123435200]
full_text
0 https://api.elsevier.com/content/article/eid/1...
1 https://api.elsevier.com/content/article/eid/1...
2 https://api.elsevier.com/content/article/eid/1...
3 https://api.elsevier.com/content/article/eid/1...
4 https://api.elsevier.com/content/article/eid/1...
5 https://api.elsevier.com/content/article/eid/1...
6 None
7 https://api.elsevier.com/content/article/eid/1...
8 https://api.elsevier.com/content/article/eid/1...
9 None
In [10]:
full_text_link_arr = search_df.full_text.values
full_text_link_arr
Out[10]:
array(['https://api.elsevier.com/content/article/eid/1-s2.0-S026840121830834X',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0306457318305193',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0167739X18307611',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0895435618304943',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0268401217308265',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0925231218315030',
None,
'https://api.elsevier.com/content/article/eid/1-s2.0-S0198971518302898',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0925231218314012',
None, None,
'https://api.elsevier.com/content/article/eid/1-s2.0-S1047320319300057',
'https://api.elsevier.com/content/article/eid/1-s2.0-S1047320319300094',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0020025518307667',
None,
'https://api.elsevier.com/content/article/eid/1-s2.0-S0957417418306146',
None,
'https://api.elsevier.com/content/article/eid/1-s2.0-S0925231217315242',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0925231218312128',
'https://api.elsevier.com/content/article/eid/1-s2.0-S0950705118305586',
None, None, None, None, None, None,
'https://api.elsevier.com/content/article/eid/1-s2.0-S138589471831564X',
None, None, None], dtype=object)
For those with full text links, you are able to get all the text by calling scopus.retrieve_full_text()
In [11]:
full_text = scopus.retrieve_full_text(full_text_link_arr[2])
In [12]:
start = 39500
full_text[start:start+10000]
Out[12]:
'any times they are implicitly described in the text. Our goal is to address the subevent detection in an unsupervised way so it can find unknown subevents while also producing explanatory labels so a user could identify the main aspects of the subevent without having to know beforehand their details. The main challenge in subevents detection is the similarity of vocabulary since every subevent shares some of the key terms present in the main event. They are also usually in a larger quantity and occurs in small timeframes when comparing to main events. We are not resorting to classification and supervised methods since social networks events are unpredictable. Also, we are concerned about giving an understanding of the event to the user without requiring a field specialist via phrases that can capture the thematic of the social discussions. This way, many events occurring could be deeply investigated at the level of subevents and be used to help in a variety of situations such as emergencies, epidemics, political and cultural movements. This work focuses on the detection of these components that are present in the reports and conversations but are not detectable without human analysis. We propose a method capable of identifying subevents from main events and subsequently creating labels to represent them, thus facilitating its comprehension by a specialist for example (e.g., an emergency specialist in the tsunami scenario). Textual information from Twitter users are used as input in the experiments because of its data volume and popularity, then we use a topic modeling based algorithm for detection mixed with a topic labeling algorithm for representation of the subevents as concise labels. Two experiments were made in the political and public health scenarios. The first one was made with a series of subevents from political protests that occurred in Brazil in 2013. The second one related to the Zika virus epidemic that occurred between 2015 and 2016. The main contributions of the method proposed include its viability when used with short and informal texts (A Tweeter post, for example, can have up to 140 characters and usually includes abbreviations, local terms, and specific words). Scalability, because the algorithms can be used with any amount of data with minimal additional processing. Language Independence, as the topic modeling and labeling can be used with any language and do not involve specific language feature processing. These characteristics can be useful for detection of subevents of different sizes with different amount of available data and for detection of global or international subevents that usually includes reports in many languages. It also differentiates itself from the related works by adding the novel labeling methods to display the subevents as entities that could be understood without analyzing the documents or the collection. This paper is organized as follows: In the next section, we explain and compare related works to give an overview of the main approaches discussed in the literature. In Section 3 we give a brief explanation of the event definitions used and the human as a sensor approach along with a topic modeling background, followed by the proposal in Section 4. In Section 5, we describe the experiments and evaluation performed along with the corresponding discussion. Finally, in Section 6, we conclude with the final considerations. 2 Related work Many works in the event detection area have been published in the last years, the majority of them focus on main events, how to differentiate an event and noise/false events and news. The research of [1], for example, proposes a news processing system based on Twitter. They make a classifier to separate news from irrelevant information and cluster them, although not making labels for the news. A similar work is presented by [5], where the authors track the breaking news through “#breakingnews” hashtags, indexing them to monitor their evolution and group them based on Named Entity Recognition. This method needs more processing and usefulness in large databases in debatable. The distinction between events and common topics or subjects is addressed by [6], where they use a grouping and classifier technique to distinguish real-world events from topics reported that have no relation to any real-world occurrence. Other works focus on the detection and monitoring through time as [3] that proposes the construction of wavelet signals based on individual words which could identify the beginning, end, and duration of an event. The same method is used in [7], but with the use of hashtags instead of individual words. For subevents, these methods have an obstacle for application due to the subevents being associated with the main event and thus sharing tags and terms. The specified event detection has works as [4] that focus on detecting events that generate public discussion in social networks. The discovery is entity based, given a controversial figure (such as a president), their goal is to detect events that generate public discussions about this figure. Musical concerts event detection is proposed by [8], using a graph model, artists information and event location to detect messages related to the specific event. Local Festivals detection is made by [9] via geotags, which are geographic information included in posts and that can be used to detect agglomeration and movement patterns. Finally, query expansion is used by [10] to assess the usage of terms over time. In each interval, the usage of terms is computed and compared to the history database to know which terms are at a peak and so more suitable to be related to an event. 3 Definitions and background Since our main objective is the subevent detection using microblogging data (specifically Twitter posts), we will define each post as a document. A collection of posts can be defined as C = {d1, d2, d3 …} where d1 is document number one, d2 the number two, and so on. The order of the documents can be retrieved anytime based on timestamp or document indexes and thus, there is no need for the collection to be ordered previously. In our method and experiments, we will use Twitter posts, specifically, the text contained in the posts. Each text consists of a maximum of 140 characters, which presents a challenge for topic and subject extraction due to the data restriction. Links and tags are considered as “text” and are considered during the processing. 3.1 Event detection Foremost, we will define an event as: Definition 1 An event is a significant occurrence limited by time and with an associated location [11]. A subevent can be defined, in the same way, as an event associated with and dependent on a “main event”. A subevent is part of an event. An event includes two or more subevents. Thus an event can be imagined as a chain of connected subevents. Then, a subevent is defined as: Definition 2 A subevent is an event associated with another event by a composition association. Event detection can be classified into two types: Specified event detection and unspecified event detection. Specified event detection is the identification of events which are previously known as planned events, such as a soccer game or a music concert, which have assigned dates and locations. Unspecified event detection, on the other hand, is the identification of unknown events, which occur quickly and unexpectedly, such as the murder of a political figure or the beginning of an earthquake. Subevents are often rarely anticipated so the majority of them would be unspecified. This characteristic makes it hard to predict the outcome with traditional trained algorithms or classifiers. Even specified events have unspecified subevents that occur as the main event develops. This work goal concerning event detection is to detect and differentiate events coming from the same main event. The methods applied can be used in both specified and unspecified subevents, although by nature, the majority of subevents occur without a previous knowledge. 3.2 Humans as sensors Sensor Networks typically consist of a set of sensors that sends data about environment parameters. The increasing growth in social network data suggests that the largest “sensor network” yet might be human. The use of social data and humans as sensor networks is called the “Human as a Sensor” (HaaS) paradigm [12]. In this approach, individuals are represented by sensors (data sources) who occasionally make observations about the physical world [13]. These observations may be true or false and hence are viewed as binary claims. The reliability of the system is based on the amount or “correct” observations and it is usually assumed that with millions of users reporting, the amount of “correct” observations surpass by far the “incorrect” ones. However, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Humans may report false propaganda based on bias rather than their own experience. Individuals may report observations made by others as their own. Mechanisms like sharing and linking could induce this behavior and the advent of fake news and its reflections on events worldwide has been a subject of debate concerning social media reliability [12]. Despite these concerns, many studies have used social networks as sensor networks with success in tasks like reporting emergency scenarios and disaster recovery [14,15]. Other works displayed good results when analyzing the reliability of microblogging posts on disaster and sports scenarios [2,12]. The focus of this work is the external environment surrounding the human sensor. Social networks can carry some subjective information as posts about the user’s feelings or isolated personal opinions. As such we want to detect only discussions and opinions regarding the subevent topic like the sentiment regarding a specific protest or an opinio'
In [13]:
author_result_df = scopus.search_author("AUTHLASTNAME(Zuo) and AUTHFIRST(Zhiya) and AFFIL(Iowa)")
In [14]:
print(author_result_df)
author_id name document_count affiliation affiliation_id
0 57189222659 Zhiya Zuo 9 University of Iowa 60024324
Then we can retrieve more detailed info about the author we are looking for using his/her author_id:
In [15]:
zuo_info_dict = scopus.retrieve_author('57189222659')
In [16]:
zuo_info_dict.keys()
Out[16]:
dict_keys(['author-id', 'eid', 'document-count', 'cited-by-count', 'citation-count', 'name', 'last', 'first', 'indexed-name', 'publication-range', 'affiliation-current', 'journal-history', 'affiliation-history'])
In [17]:
print('\n'.join(zuo_info_dict['affiliation-history'].name.values))
University of Iowa
University of Iowa, Interdisciplinary Graduate Program in Informatics
China Academy of Chinese Medical Sciences, Data Center of TCM
Tongji University, Department of Control Science and Engineering
In [18]:
zuo_pub_df = scopus.search_author_publication('57189222659')
In [19]:
zuo_pub_df[['title', 'cover_date', 'publication_name', 'scopus_id']].sort_values('cover_date').reset_index(drop=True)
Out[19]:
title
cover_date
publication_name
scopus_id
0
Subhealth state classification with AdaBoost l...
2013-01-01
International Journal of Functional Informatic...
84892146718
1
The evolution of user roles in online health c...
2015-01-01
Pacific Asia Conference on Information Systems...
85011032472
2
The evolution and diffusion of user roles in o...
2015-12-08
Proceedings - 2015 IEEE International Conferen...
84966453564
3
Investigating regional prejudice in China thro...
2016-01-01
Lecture Notes in Computer Science (including s...
84995379893
4
Systematic investigation of sex disparity in m...
2017-01-01
Proceedings of the Association for Information...
85040762441
5
Comparing of feature selection and classificat...
2017-01-17
Proceedings - 2016 IEEE International Conferen...
85013231650
6
The state and evolution of U.S. iSchools: From...
2017-05-01
Journal of the Association for Information Sci...
85004154180
7
A Graphical Model for Topical Impact over Time
2018-05-23
Proceedings of the ACM/IEEE Joint Conference o...
85048881340
8
The more multidisciplinary the better? - The p...
2018-08-01
Journal of Informetrics
85049552190
If the 2nd argument download_path is not given, the JSON response would not be saved
In [20]:
pub_info = scopus.retrieve_abstract('85049552190', './')
In [21]:
pub_info
Out[21]:
{'srctype': 'j',
'eid': '2-s2.0-85049552190',
'prism:coverDate': '2018-08-01',
'prism:aggregationType': 'Journal',
'prism:url': 'https://api.elsevier.com/content/abstract/scopus_id/85049552190',
'source-id': '5100155103',
'pii': 'S1751157718300452',
'citedby-count': '1',
'prism:volume': '12',
'subtype': 'ar',
'openaccess': '0',
'prism:issn': '18755879 17511577',
'prism:issueIdentifier': '3',
'subtypeDescription': 'Article',
'prism:publicationName': 'Journal of Informetrics',
'prism:pageRange': '736-756',
'prism:endingPage': '756',
'openaccessFlag': 'false',
'prism:doi': '10.1016/j.joi.2018.06.006',
'prism:startingPage': '736',
'dc:publisher': 'Elsevier Ltd',
'scopus-id': '85049552190',
'abstract': '© 2018 Elsevier Ltd. All rights reserved. Scientific research is increasingly relying on collaborations to address complex real-world problems. Many researchers, policymakers, and administrators consider a multidisciplinary environment an important factor for fostering research collaborations, especially interdisciplinary ones that involve researchers from different disciplines. However, it remains unknown whether a higher level of multidisciplinarity within an academic institution is associated with internal collaborations that are more prevalent and more interdisciplinary. Analyzing 90,000 publications by 2500 faculty members in over 100 academic institutions from three multidisciplinary areas, information, public policy, and neuroscience, we investigated the connection between multidisciplinarity and research collaborations. Based on social network analysis and text mining, our analysis suggests that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary. Our findings provide implications for academic administrators and policymakers to promote research collaborations and interdisciplinarity in academic institutions.',
'title': 'The more multidisciplinary the better? - The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions'}
In [22]:
cat 85049552190.json
{"abstracts-retrieval-response": {"item": {"ait:process-info": {"ait:status": {"@state": "update", "@type": "core", "@stage": "S300"}, "ait:date-delivered": {"@day": "29", "@timestamp": "2018-09-29T21:25:59.000059-04:00", "@year": "2018", "@month": "09"}, "ait:date-sort": {"@day": "01", "@year": "2018", "@month": "08"}}, "xocs:meta": {"xocs:funding-list": {"@pui-match": "primary", "@has-funding-info": "1", "xocs:funding-addon-generated-timestamp": "2018-07-12T21:30:32.702Z"}}, "bibrecord": {"head": {"author-group": {"affiliation": {"country": "United States", "@afid": "60024324", "@country": "usa", "organization": {"$": "University of Iowa"}, "affiliation-id": {"@afid": "60024324"}, "@affiliation-instance-id": "S1751157718300452-767136c5842968c97d8ce9c2ef3b8b2b"}, "author": [{"ce:given-name": "Zhiya", "preferred-name": {"ce:given-name": "Zhiya", "ce:initials": "Z.", "ce:surname": "Zuo", "ce:indexed-name": "Zuo Z."}, "@author-instance-id": "S1751157718300452-d600411a3d86bb2e0b1f62d25e5fdbfe", "@seq": "1", "ce:initials": "Z.", "@_fa": "true", "@type": "auth", "ce:surname": "Zuo", "@auid": "57189222659", "ce:indexed-name": "Zuo Z."}, {"ce:given-name": "Kang", "preferred-name": {"ce:given-name": "Kang", "ce:initials": "K.", "ce:surname": "Zhao", "ce:indexed-name": "Zhao K."}, "@author-instance-id": "S1751157718300452-e75be0984065383d100461426df26ae6", "@seq": "2", "ce:initials": "K.", "@_fa": "true", "@type": "auth", "ce:surname": "Zhao", "@auid": "36635367700", "ce:indexed-name": "Zhao K."}]}, "citation-title": "The more multidisciplinary the better? - The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions", "abstracts": "\u00a9 2018 Elsevier Ltd. All rights reserved. Scientific research is increasingly relying on collaborations to address complex real-world problems. Many researchers, policymakers, and administrators consider a multidisciplinary environment an important factor for fostering research collaborations, especially interdisciplinary ones that involve researchers from different disciplines. However, it remains unknown whether a higher level of multidisciplinarity within an academic institution is associated with internal collaborations that are more prevalent and more interdisciplinary. Analyzing 90,000 publications by 2500 faculty members in over 100 academic institutions from three multidisciplinary areas, information, public policy, and neuroscience, we investigated the connection between multidisciplinarity and research collaborations. Based on social network analysis and text mining, our analysis suggests that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary. Our findings provide implications for academic administrators and policymakers to promote research collaborations and interdisciplinarity in academic institutions.", "correspondence": {"affiliation": {"country": "United States", "@country": "usa", "organization": {"$": "University of Iowa"}}, "person": {"ce:given-name": "Kang", "ce:initials": "K.", "ce:surname": "Zhao", "ce:indexed-name": "Zhao K."}}, "citation-info": {"author-keywords": {"author-keyword": [{"$": "Collaboration", "@xml:lang": "eng"}, {"$": "Interdisciplinarity", "@xml:lang": "eng"}, {"$": "Multidisciplinarity", "@xml:lang": "eng"}, {"$": "Network analysis", "@xml:lang": "eng"}, {"$": "Text mining", "@xml:lang": "eng"}]}, "citation-type": {"@code": "ar"}, "citation-language": {"@language": "English", "@xml:lang": "eng"}, "abstract-language": {"@language": "English", "@xml:lang": "eng"}}, "source": {"sourcetitle-abbrev": "J. Inf.", "website": {"ce:e-address": {"$": "http://www.journals.elsevier.com/journal-of-informetrics/", "@type": "email"}}, "@country": "gbr", "translated-sourcetitle": {"$": "Journal of Informetrics", "@xml:lang": "eng"}, "issn": [{"$": "18755879", "@type": "electronic"}, {"$": "17511577", "@type": "print"}], "volisspag": {"voliss": {"@volume": "12", "@issue": "3"}, "pagerange": {"@first": "736", "@last": "756"}}, "@type": "j", "publicationyear": {"@first": "2018"}, "publisher": {"publishername": "Elsevier Ltd"}, "sourcetitle": "Journal of Informetrics", "@srcid": "5100155103", "publicationdate": {"month": "08", "year": "2018", "date-text": "August 2018", "day": "01"}}, "enhancement": {"classificationgroup": {"classifications": [{"@type": "CPXCLASS", "classification": [{"classification-code": "703.1.1", "classification-description": "Electric Network Analysis"}, {"classification-code": "723.2", "classification-description": "Data Processing"}, {"classification-code": "723.5", "classification-description": "Computer Applications"}]}, {"@type": "FLXCLASS", "classification": {"classification-code": "902", "classification-description": "FLUIDEX; Related Topics"}}, {"@type": "ASJC", "classification": [{"$": "1706"}, {"$": "3309"}]}, {"@type": "SUBJABBR", "classification": [{"$": "COMP"}, {"$": "SOCI"}]}]}}}, "item-info": {"copyright": {"$": "Copyright 2018 Elsevier B.V., All rights reserved.", "@type": "Elsevier"}, "dbcollection": [{"$": "CPX"}, {"$": "REAXYSCAR"}, {"$": "SCOPUS"}, {"$": "Scopusbase"}], "history": {"date-created": {"@day": "12", "@timestamp": "BST 06:19:48", "@year": "2018", "@month": "07"}}, "itemidlist": {"itemid": [{"$": "622895862", "@idtype": "PUI"}, {"$": "911534263", "@idtype": "CAR-ID"}, {"$": "20182805532386", "@idtype": "CPX"}, {"$": "20181206345", "@idtype": "REAXYSCAR"}, {"$": "20182073350", "@idtype": "SCOPUS"}, {"$": "85049552190", "@idtype": "SCP"}, {"$": "85049552190", "@idtype": "SGR"}], "ce:pii": "S1751157718300452", "ce:doi": "10.1016/j.joi.2018.06.006"}}, "tail": {"bibliography": {"@refcount": "74", "reference": [{"ref-fulltext": "J. 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Science and Public Policy 42 2015 567 582 10.1093/scipol/scu074", "@id": "66", "ref-info": {"ref-publicationyear": {"@first": "2015"}, "ref-title": {"ref-titletext": "Research collaboration in the social sciences: What factors are associated with disciplinary and interdisciplinary collaboration?"}, "refd-itemidlist": {"itemid": [{"$": "10.1093/scipol/scu074", "@idtype": "DOI"}, {"$": "84939516508", "@idtype": "SGR"}]}, "ref-volisspag": {"voliss": {"@volume": "42"}, "pagerange": {"@first": "567", "@last": "582"}}, "ref-authors": {"author": [{"@seq": "1", "ce:initials": "R.", "@_fa": "true", "ce:surname": "Woolley", "ce:indexed-name": "Woolley R."}, {"@seq": "2", "ce:initials": "M.", "@_fa": "true", "ce:surname": "S\u00e1nchez-Barrioluengo", "ce:indexed-name": "Sanchez-Barrioluengo M."}, {"@seq": "3", "ce:initials": "T.", "@_fa": "true", "ce:surname": "Turpin", "ce:indexed-name": "Turpin T."}, {"@seq": "4", "ce:initials": "J.", "@_fa": "true", "ce:surname": "Marceau", "ce:indexed-name": "Marceau J."}]}, "ref-sourcetitle": "Science and Public Policy"}}, {"ref-fulltext": "S. 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Zhao, B. Huo, W. Selen, and J.H.Y. Yeung The impact of internal integration and relationship commitment on external integration Journal of Operations Management 29 2011 17 32 10.1016/j.jom.2010.04.004", "@id": "72", "ref-info": {"ref-publicationyear": {"@first": "2011"}, "ref-title": {"ref-titletext": "The impact of internal integration and relationship commitment on external integration"}, "refd-itemidlist": {"itemid": [{"$": "10.1016/j.jom.2010.04.004", "@idtype": "DOI"}, {"$": "79951575629", "@idtype": "SGR"}]}, "ref-volisspag": {"voliss": {"@volume": "29"}, "pagerange": {"@first": "17", "@last": "32"}}, "ref-authors": {"author": [{"@seq": "1", "ce:initials": "X.", "@_fa": "true", "ce:surname": "Zhao", "ce:indexed-name": "Zhao X."}, {"@seq": "2", "ce:initials": "B.", "@_fa": "true", "ce:surname": "Huo", "ce:indexed-name": "Huo B."}, {"@seq": "3", "ce:initials": "W.", "@_fa": "true", "ce:surname": "Selen", "ce:indexed-name": "Selen W."}, {"@seq": "4", "ce:initials": "J.H.Y.", "@_fa": "true", "ce:surname": "Yeung", "ce:indexed-name": "Yeung J.H.Y."}]}, "ref-sourcetitle": "Journal of Operations Management"}}, {"ref-fulltext": "Y. Zhu, E. Yan, and M. Song Understanding the evolving academic landscape of library and information science through faculty hiring data Scientometrics 108 2016 1461 1478 10.1007/s11192-016-2033-z", "@id": "73", "ref-info": {"ref-publicationyear": {"@first": "2016"}, "ref-title": {"ref-titletext": "Understanding the evolving academic landscape of library and information science through faculty hiring data"}, "refd-itemidlist": {"itemid": [{"$": "10.1007/s11192-016-2033-z", "@idtype": "DOI"}, {"$": "84976320561", "@idtype": "SGR"}]}, "ref-volisspag": {"voliss": {"@volume": "108"}, "pagerange": {"@first": "1461", "@last": "1478"}}, "ref-authors": {"author": [{"@seq": "1", "ce:initials": "Y.", "@_fa": "true", "ce:surname": "Zhu", "ce:indexed-name": "Zhu Y."}, {"@seq": "2", "ce:initials": "E.", "@_fa": "true", "ce:surname": "Yan", "ce:indexed-name": "Yan E."}, {"@seq": "3", "ce:initials": "M.", "@_fa": "true", "ce:surname": "Song", "ce:indexed-name": "Song M."}]}, "ref-sourcetitle": "Scientometrics"}}, {"ref-fulltext": "Z. Zuo, K. Zhao, and D. Eichmann The state and evolution of U.S. iSchools: From talent acquisitions to research outcome Journal of the Association for Information Science and Technology 68 2017 1266 1277 10.1002/asi.23751", "@id": "74", "ref-info": {"ref-publicationyear": {"@first": "2017"}, "ref-title": {"ref-titletext": "The state and evolution of U.S. ISchools: From talent acquisitions to research outcome"}, "refd-itemidlist": {"itemid": [{"$": "10.1002/asi.23751", "@idtype": "DOI"}, {"$": "85004154180", "@idtype": "SGR"}]}, "ref-volisspag": {"voliss": {"@volume": "68"}, "pagerange": {"@first": "1266", "@last": "1277"}}, "ref-authors": {"author": [{"@seq": "1", "ce:initials": "Z.", "@_fa": "true", "ce:surname": "Zuo", "ce:indexed-name": "Zuo Z."}, {"@seq": "2", "ce:initials": "K.", "@_fa": "true", "ce:surname": "Zhao", "ce:indexed-name": "Zhao K."}, {"@seq": "3", "ce:initials": "D.", "@_fa": "true", "ce:surname": "Eichmann", "ce:indexed-name": "Eichmann D."}]}, "ref-sourcetitle": "Journal of the Association for Information Science and Technology"}}]}}}}, "affiliation": {"affiliation-city": "Iowa City", "@id": "60024324", "affilname": "University of Iowa", "@href": "https://api.elsevier.com/content/affiliation/affiliation_id/60024324", "affiliation-country": "United States"}, "coredata": {"srctype": "j", "eid": "2-s2.0-85049552190", "dc:description": "\u00a9 2018 Elsevier Ltd. All rights reserved. Scientific research is increasingly relying on collaborations to address complex real-world problems. Many researchers, policymakers, and administrators consider a multidisciplinary environment an important factor for fostering research collaborations, especially interdisciplinary ones that involve researchers from different disciplines. However, it remains unknown whether a higher level of multidisciplinarity within an academic institution is associated with internal collaborations that are more prevalent and more interdisciplinary. Analyzing 90,000 publications by 2500 faculty members in over 100 academic institutions from three multidisciplinary areas, information, public policy, and neuroscience, we investigated the connection between multidisciplinarity and research collaborations. Based on social network analysis and text mining, our analysis suggests that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary. Our findings provide implications for academic administrators and policymakers to promote research collaborations and interdisciplinarity in academic institutions.", "prism:coverDate": "2018-08-01", "prism:aggregationType": "Journal", "prism:url": "https://api.elsevier.com/content/abstract/scopus_id/85049552190", "dc:creator": {"author": [{"ce:given-name": "Zhiya", "preferred-name": {"ce:given-name": "Zhiya", "ce:initials": "Z.", "ce:surname": "Zuo", "ce:indexed-name": "Zuo Z."}, "@seq": "1", "ce:initials": "Z.", "@_fa": "true", "affiliation": {"@id": "60024324", "@href": "https://api.elsevier.com/content/affiliation/affiliation_id/60024324"}, "ce:surname": "Zuo", "@auid": "57189222659", "author-url": "https://api.elsevier.com/content/author/author_id/57189222659", "ce:indexed-name": "Zuo Z."}]}, "link": [{"@_fa": "true", "@rel": "self", "@href": "https://api.elsevier.com/content/abstract/scopus_id/85049552190"}, {"@_fa": "true", "@rel": "scopus", "@href": "https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049552190&origin=inward"}, {"@_fa": "true", "@rel": "scopus-citedby", "@href": "https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85049552190&origin=inward"}], "source-id": "5100155103", "pii": "S1751157718300452", "citedby-count": "1", "prism:volume": "12", "subtype": "ar", "dc:title": "The more multidisciplinary the better? - The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions", "openaccess": "0", "prism:issn": "18755879 17511577", "prism:issueIdentifier": "3", "subtypeDescription": "Article", "prism:publicationName": "Journal of Informetrics", "prism:pageRange": "736-756", "prism:endingPage": "756", "openaccessFlag": "false", "prism:doi": "10.1016/j.joi.2018.06.006", "prism:startingPage": "736", "dc:identifier": "SCOPUS_ID:85049552190", "dc:publisher": "Elsevier Ltd"}, "idxterms": {"mainterm": [{"$": "Academic administrators", "@weight": "b", "@candidate": "n"}, {"$": "Academic institutions", "@weight": "b", "@candidate": "n"}, {"$": "Collaboration", "@weight": "b", "@candidate": "n"}, {"$": "Interdisciplinarity", "@weight": "b", "@candidate": "n"}, {"$": "Multidisciplinarity", "@weight": "b", "@candidate": "n"}, {"$": "Research collaborations", "@weight": "b", "@candidate": "n"}, {"$": "Scientific researches", "@weight": "b", "@candidate": "n"}, {"$": "Text mining", "@weight": "b", "@candidate": "n"}]}, "language": {"@xml:lang": "eng"}, "authkeywords": {"author-keyword": [{"@_fa": "true", "$": "Collaboration"}, {"@_fa": "true", "$": "Interdisciplinarity"}, {"@_fa": "true", "$": "Multidisciplinarity"}, {"@_fa": "true", "$": "Network analysis"}, {"@_fa": "true", "$": "Text mining"}]}, "subject-areas": {"subject-area": [{"@_fa": "true", "$": "Computer Science Applications", "@code": "1706", "@abbrev": "COMP"}, {"@_fa": "true", "$": "Library and Information Sciences", "@code": "3309", "@abbrev": "SOCI"}]}, "authors": {"author": [{"ce:given-name": "Zhiya", "preferred-name": {"ce:given-name": "Zhiya", "ce:initials": "Z.", "ce:surname": "Zuo", "ce:indexed-name": "Zuo Z."}, "@seq": "1", "ce:initials": "Z.", "@_fa": "true", "affiliation": {"@id": "60024324", "@href": "https://api.elsevier.com/content/affiliation/affiliation_id/60024324"}, "ce:surname": "Zuo", "@auid": "57189222659", "author-url": "https://api.elsevier.com/content/author/author_id/57189222659", "ce:indexed-name": "Zuo Z."}, {"ce:given-name": "Kang", "preferred-name": {"ce:given-name": "Kang", "ce:initials": "K.", "ce:surname": "Zhao", "ce:indexed-name": "Zhao K."}, "@seq": "2", "ce:initials": "K.", "@_fa": "true", "affiliation": {"@id": "60024324", "@href": "https://api.elsevier.com/content/affiliation/affiliation_id/60024324"}, "ce:surname": "Zhao", "@auid": "36635367700", "author-url": "https://api.elsevier.com/content/author/author_id/36635367700", "ce:indexed-name": "Zhao K."}]}}}
Note that Searching for articles in specific journals (venues) is not supported anymore since this can be easily done by general search.
Note that the use of citation overview API needs to be approved by Elsevier.
In [23]:
pub_citations_df = scopus.retrieve_citation(scopus_id_array=['85049552190', '85004154180'],
year_range=[2016, 2018])
In [24]:
print(pub_citations_df)
scopus_id previous_citation 2016 2017 2018 later_citation total_citation
0 85004154180 0 0 1 2 0 3
1 85049552190 0 0 0 1 0 1
If interested in meta information and metrics at publication venue level (e.g., journal/conference), we can now use search_serial or retrieve_serial
In [25]:
meta_df, citescore_df, sj_rank_df = scopus.search_serial('informetrics')
meta_df
Out[25]:
dc:publisher
dc:title
oaAllowsAuthorPaid
openArchiveArticle
openaccess
openaccessArticle
openaccessStartDate
openaccessType
prism:aggregationType
prism:issn
source-id
subject-area
0
Elsevier BV
Journal of Informetrics
True
False
0
False
None
None
journal
1751-1577
5100155103
[1706, 3309]
See more about CiteScore
In [26]:
citescore_df
Out[26]:
citationCount
citeScore
docType
percentCited
scholarlyOutput
status
year
source-id
prism:issn
0
1010
3.44
all
77
294
In-Progress
2018
5100155103
1751-1577
1
985
3.52
all
80
280
Complete
2017
5100155103
1751-1577
2
835
2.99
all
73
279
Complete
2016
5100155103
1751-1577
3
706
2.60
all
74
272
Complete
2015
5100155103
1751-1577
4
713
2.89
all
82
247
Complete
2014
5100155103
1751-1577
5
938
4.38
all
87
214
Complete
2013
5100155103
1751-1577
6
782
4.55
all
90
172
Complete
2012
5100155103
1751-1577
7
550
3.96
all
79
139
Complete
2011
5100155103
1751-1577
The last dataframe below lists the rank/percentile of this serial in each subject area it is assigned to across years
In [27]:
sj_rank_df.head(2)
Out[27]:
percentile
rank
subjectCode
year
source-id
prism:issn
0
82
102
1706
2018
5100155103
1751-1577
1
92
16
3309
2018
5100155103
1751-1577
Given a ISSN, we can use retrieve_serial:
In [28]:
meta_df, citescore_df, sj_rank_df = scopus.retrieve_serial('2330-1643')
meta_df
Out[28]:
dc:publisher
dc:title
oaAllowsAuthorPaid
openArchiveArticle
openaccess
openaccessArticle
openaccessStartDate
openaccessType
prism:aggregationType
prism:eIssn
prism:issn
source-id
subject-area
0
John Wiley and Sons Ltd
Journal of the Association for Information Sci...
None
None
None
None
None
None
journal
2330-1643
2330-1635
21100307484
[1710, 1705, 1802, 3309]
In [29]:
citescore_df
Out[29]:
citationCount
citeScore
docType
percentCited
scholarlyOutput
status
year
source-id
prism:issn
0
2225
3.48
all
76
640
In-Progress
2018
21100307484
2330-1635
1
2088
3.36
all
75
621
Complete
2017
21100307484
2330-1635
2
1657
2.74
all
71
605
Complete
2016
21100307484
2330-1635
3
364
2.25
all
73
162
Complete
2015
21100307484
2330-1635
In [30]:
sj_rank_df.head(2)
Out[30]:
percentile
rank
subjectCode
year
source-id
prism:issn
0
82
48
1710
2018
21100307484
2330-1635
1
83
47
1705
2018
21100307484
2330-1635
In [31]:
uiowa = scopus.retrieve_affiliation('60024324')
uiowa
Out[31]:
{'eid': '10-s2.0-60024324',
'affiliation-name': 'University of Iowa',
'address': None,
'city': 'Iowa City',
'country': 'United States',
'org-type': 'univ',
'org-domain': 'uiowa.edu',
'org-URL': 'http://www.uiowa.edu/',
'date-created': '02/02/2008',
'aff_id': '60024324'}
Content source: zhiyzuo/pyscopus
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