Scimeetr

Install
Introduction
From data to reading list
In depth description of each steps

Install

scimeetr can be installed directly from the R console using the following lines :

if (!require("devtools")) install.packages("devtools")

devtools::install_github("MaximeRivest/scimeetr")

Introduction

Scimeetr helps explore the scholarly literature. It contains a suit of function that let someone:

load bibliometric data into R
make a map of peer reviewed papers by creating various networks
find research community
characterise the research communities
generate reading list

This tutorial is composed of two self-contained section. The first section show case the whole process with all the default parameters. The second section describes each function in more detail by presenting the rational for the function, the algorithms used and the options.

From data to reading list

You can automatically generate a reading list of seminal papers in a research litterature by using only those three functions: ìmport_wos_files, scimap, and scilist. This first section describes this process in more details.

loading and exploring bibliometric data

The first step in exploring the literature is to retrieve bibliometric data from the Web of Science or Scopus. In this first tutorial I use a dataset from the Web of Science about ecological networks.

library(scimeetr)
scimeetr_list <- import_wos_files("path/to/folder/")

Then,summary can be used to get a quick characterisation of the data.

summary(scimeetr_list)

## 
##  # Summary of Scimeetr #
## -----------------------
##     Number of papers:  742
##     Number of different reference:  28526
## 
##     Average number of reference per paper:  51
## 
##     Quantiles of total citation per paper: 
## 
##      0%     25%     50%     75%    100% 
##    0.00    2.00    7.00   19.75 1333.00 
## 
##     Mean number of citation per paper:  19.81536
## 
##     Average number of citation per paper per year:  1.2
## 
## 
##   Table of the 10 most mentionned keywords 
## 
##                       Keyword    Frequency
## 1                BIODIVERSITY           57
## 2                 AGRICULTURE           46
## 3  COMMON AGRICULTURAL POLICY           32
## 4          ECOSYSTEM SERVICES           31
## 5                CONSERVATION           28
## 6    AGRI-ENVIRONMENT SCHEMES           27
## 7     AGRI-ENVIRONMENT SCHEME           20
## 8  AGRI-ENVIRONMENTAL SCHEMES           19
## 9         AGRICULTURAL POLICY           18
## 10              WATER QUALITY           18
## 
## 
## 
##   Table of the 10 most productive journal 
## 
##                                             Journal    Frequency
## 1                                   LAND USE POLICY           84
## 2              AGRICULTURE ECOSYSTEMS & ENVIRONMENT           37
## 3               JOURNAL OF ENVIRONMENTAL MANAGEMENT           33
## 4                           BIOLOGICAL CONSERVATION           24
## 5                        JOURNAL OF APPLIED ECOLOGY           21
## 6                              ECOLOGICAL ECONOMICS           17
## 7                          JOURNAL OF RURAL STUDIES           17
## 8                              AGRICULTURAL SYSTEMS           14
## 9  JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT           14
## 10                     LANDSCAPE AND URBAN PLANNING           14
## 
## 
## 
##   Table of the most descriminant keywords 
## 
##        comID                      tag
## 1 com1 (742)                         
## 2                        BIODIVERSITY
## 3                        CONSERVATION
## 4                          MANAGEMENT
## 5                         AGRICULTURE
## 6            AGRI-ENVIRONMENT SCHEMES
## 7                  ECOSYSTEM SERVICES

From this summary, we see that there is 396 papers in my data set which overal cites 16567 different elements. On average, each paper cites 53 elements.

Than we learn that, in this research community, 25% of the papers are cited less than 2 times, 50% are cited less than 9 times and 75% are cited less than ~23 times. There are papers that are cited up to 1333 times. The average citation per paper is ~25. This is much higher than the median (9), thus most paper are cited only a few times and a few papers are profusely cited. When correcting for the age of the paper, we learn that papers are cited 2 times per year on average.

By looking at the most frequent keyword and journals, we learn that this community of research is about biodiversity, agriculture, ecosystem services and policy. Keyword and journal frequency tables efficiently reveal the theme of a scientific community.

Mapping scientific community

The previous characterisation is great, but it is limited if your dataset contains many different scientific communities. By detecting the scientific communities present within a dataset a map of science can be drawn and each cluster can be characterised on its own. The function scimap can be used for this task.

scimap_result <- scimap(scimeetr_list)

The function returns all the data that scimeetr_list contained and more. For example communities have been identified and now if the function summary is used on scim_result. In addition of the previous information. The descriminant keywords of each communities constituating the main community are listed.

summary(scimap_result)

## 
##  # Summary of Scimeetr #
## -----------------------
##     Number of papers:  742
##     Number of different reference:  28526
## 
##     Average number of reference per paper:  51
## 
##     Quantiles of total citation per paper: 
## 
##      0%     25%     50%     75%    100% 
##    0.00    2.00    7.00   19.75 1333.00 
## 
##     Mean number of citation per paper:  19.81536
## 
##     Average number of citation per paper per year:  1.2
## 
## 
##   Table of the 10 most mentionned keywords 
## 
##                       Keyword    Frequency
## 1                BIODIVERSITY           57
## 2                 AGRICULTURE           46
## 3  COMMON AGRICULTURAL POLICY           32
## 4          ECOSYSTEM SERVICES           31
## 5                CONSERVATION           28
## 6    AGRI-ENVIRONMENT SCHEMES           27
## 7     AGRI-ENVIRONMENT SCHEME           20
## 8  AGRI-ENVIRONMENTAL SCHEMES           19
## 9         AGRICULTURAL POLICY           18
## 10              WATER QUALITY           18
## 
## 
## 
##   Table of the 10 most productive journal 
## 
##                                             Journal    Frequency
## 1                                   LAND USE POLICY           84
## 2              AGRICULTURE ECOSYSTEMS & ENVIRONMENT           37
## 3               JOURNAL OF ENVIRONMENTAL MANAGEMENT           33
## 4                           BIOLOGICAL CONSERVATION           24
## 5                        JOURNAL OF APPLIED ECOLOGY           21
## 6                              ECOLOGICAL ECONOMICS           17
## 7                          JOURNAL OF RURAL STUDIES           17
## 8                              AGRICULTURAL SYSTEMS           14
## 9  JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT           14
## 10                     LANDSCAPE AND URBAN PLANNING           14
## 
## 
## 
##   Table of the most descriminant keywords 
## 
##           comID                      tag                   ltag$tag
## 1    com1 (742)                                                    
## 2                           BIODIVERSITY                           
## 3                           CONSERVATION                           
## 4                             MANAGEMENT                           
## 5                            AGRICULTURE                           
## 6               AGRI-ENVIRONMENT SCHEMES                           
## 7                     ECOSYSTEM SERVICES                           
## 8  com1_1 (358)                                                    
## 9                                                          ADOPTION
## 10                                                    PARTICIPATION
## 11                                                      AGRICULTURE
## 12                                                          FARMERS
## 13                                                           POLICY
## 14                                       AGRI-ENVIRONMENTAL SCHEMES
## 15  com1_2 (49)                                                    
## 16                                                      ABANDONMENT
## 17                                                    CEREAL-STEPPE
## 18                                                    MEMBER STATES
## 19                                                   MOUNTAIN AREAS
## 20                                       COMMON AGRICULTURAL POLICY
## 21                                                       CAP REFORM
## 22  com1_6 (62)                                                    
## 23                                              BOVINE TUBERCULOSIS
## 24                                                             RICE
## 25                                                          SCARING
## 26                                             SPECIES DISTRIBUTION
## 27                                                 INVASIVE SPECIES
## 28                                             LANDSCAPE PREFERENCE
## 29 com1_3 (265)                                                    
## 30                                                     BIODIVERSITY
## 31                                         AGRI-ENVIRONMENT SCHEMES
## 32                                                       MANAGEMENT
## 33                                          AGRICULTURAL LANDSCAPES
## 34                                                        DIVERSITY
## 35                                                     CONSERVATION

Except for the last tables, all of the output is identical to the summary output above. Those last tables now reveals that the papers in our database can be clustered in two communities. One that is about x and the other that is about y.

The function plot can be used on the output of the function summary for a graphical representation of the sub-communities.

plot(summary(scimap_result, com_size = 30))

Automatically generating a reading list of seminal papers

Now that we have characterise the main community and seen of which community it is constituted, we can decide if it is the community that we wish to join / review. If it is, we use the function scilist to get reading lists. The defaul readin list will find the seminal papers of each communitiy.

reading_list <- scilist(scimap_result)
reading_list$com1

publication	metric	list_type
KLEIJN D, 2003, J APPL ECOL, 40, 947	113	core_papers
KLEIJN D, 2006, ECOL LETT, 9, 243	73	core_papers
KLEIJN D, 2001, NATURE, 413, 723	57	core_papers
BENTON TG, 2003, TRENDS ECOL EVOL, 18, 182	54	core_papers
PANNELL DJ, 2006, AUST J EXP AGR, 46, 1407	50	core_papers

In depth description of each steps

How to get bibliometric data?

Biliometric data can be obtained from either Scopus or the Web of Science. Most university library have access to either one and some have access to both.

Retrieving data from Scopus

Scopus home page.

Select all and export

Export as CSV file and select all fields for exportation

Following the previous steps will get you one or several .csv files. Then, to import this/these file(s) in R, you need to put it/them in a new folder which contains only the files to import into R

Retrieving data from Web of Science

Web of Science home page. Make sure that Select a database corresponds to Web of Science Core Collection

Web of Science home page. Make sure that Select a database corresponds to Web of Science Core Collection

Save to Other Files Formats

Save to Other Files Formats

You can download only 500 items at a time. You should select Full Record and Cited References. And select the Tab-delimeted (UTF-8) as file format.

You can download only 500 items at a time. You should select Full Record and Cited References. And select the Tab-delimeted (UTF-8) as file format.

Following the previous steps will get you one or several .txt files. Then, to import this/these file(s) in R, you need to put it/them in a new folder which contains only the files to import into R

How to upload bibliometric data into R

The bibliometric data obtained from Scopus or Web of science are either in .csv or .txt format. These are standard file formats and you most likely know them. There are built in function in R that let you import .csv and .txt files. So why does scimeetr provide you with import_scopus_files and import_wos_files? There are four reasons. The main one is that bibliometric data contains author names from around the world, which means that all alphabets are used and this leads to problems with file encoding. Scimeetr's import functions solves that problem. Second, Scopus do not provide standard, uniform and consisten cited reference list. Thus, import_scopus_files has to standardize it at import. This explains the additional time required to load scopus files versus wos files. Third, Scopus and Web of Science do not use the same column names so they have to be homogenized at import. Finally, the data can be transformed into a scimeetr object so that summary, plot and print will know what to do with it.

scimeetr_list <- import_wos_files(files_directory = "/path/to/folder/")
scimeetr_list <- import_scopus_files(files_directory = "/path/to/folder/")

Do not forget that this function take in a path to a folder not a file. Thus, it need a slash at the end of the folder path.

Exploring scimeetr data

Printing and summary

Printing scimeetr_list that we just created will provide some informations about it, but summary will provide more.

scimeetr_list

## 
## # A scimeetr object #
## ---------------------
## Number of papers:  742
## Number of communities:  1
## Names of communities:  com1
## 
## Table of the 5 most mentionned words 
## 
##                  key_words  title_words abstract_words
## 1             BIODIVERSITY CONSERVATION        FARMERS
## 2             CONSERVATION AGRICULTURAL   CONSERVATION
## 3               MANAGEMENT   MANAGEMENT   AGRICULTURAL
## 4              AGRICULTURE       POLICY     MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES      FARMERS         POLICY

summary(scimeetr_list)

## 
##  # Summary of Scimeetr #
## -----------------------
##     Number of papers:  742
##     Number of different reference:  28526
## 
##     Average number of reference per paper:  51
## 
##     Quantiles of total citation per paper: 
## 
##      0%     25%     50%     75%    100% 
##    0.00    2.00    7.00   19.75 1333.00 
## 
##     Mean number of citation per paper:  19.81536
## 
##     Average number of citation per paper per year:  1.2
## 
## 
##   Table of the 10 most mentionned keywords 
## 
##                       Keyword    Frequency
## 1                BIODIVERSITY           57
## 2                 AGRICULTURE           46
## 3  COMMON AGRICULTURAL POLICY           32
## 4          ECOSYSTEM SERVICES           31
## 5                CONSERVATION           28
## 6    AGRI-ENVIRONMENT SCHEMES           27
## 7     AGRI-ENVIRONMENT SCHEME           20
## 8  AGRI-ENVIRONMENTAL SCHEMES           19
## 9         AGRICULTURAL POLICY           18
## 10              WATER QUALITY           18
## 
## 
## 
##   Table of the 10 most productive journal 
## 
##                                             Journal    Frequency
## 1                                   LAND USE POLICY           84
## 2              AGRICULTURE ECOSYSTEMS & ENVIRONMENT           37
## 3               JOURNAL OF ENVIRONMENTAL MANAGEMENT           33
## 4                           BIOLOGICAL CONSERVATION           24
## 5                        JOURNAL OF APPLIED ECOLOGY           21
## 6                              ECOLOGICAL ECONOMICS           17
## 7                          JOURNAL OF RURAL STUDIES           17
## 8                              AGRICULTURAL SYSTEMS           14
## 9  JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT           14
## 10                     LANDSCAPE AND URBAN PLANNING           14
## 
## 
## 
##   Table of the most descriminant keywords 
## 
##        comID                      tag
## 1 com1 (742)                         
## 2                        BIODIVERSITY
## 3                        CONSERVATION
## 4                          MANAGEMENT
## 5                         AGRICULTURE
## 6            AGRI-ENVIRONMENT SCHEMES
## 7                  ECOSYSTEM SERVICES

Characterizing the corpus of papers contained by a scimeetr object

Within the scimeetr package there are several function that help us characterize our corpus of papers.

The corpus of papers can be characterized be:

keywords with characterize_kw()
title-words with characterize_ti()
astract-words with characterize_ab()
journals with characterize_jo()
authors with characterize_au()
universities with characterize_un()
countries with characterize_co()

Characterize the corpus with keywords

To get even more information about the corpus of papers contained within scimeetr_list we can use characterize_kw. This function will generate a list of data frames, one data frame per communities within scimeetr_list. The first column of any of these data frames will contain the keywords themselves. The second column contains the frequency of the keywords (i.e. the number of papers that mentions this keyword).

kw <- characterize_kw(scimeetr_list)
head(kw$com1)

keyword	id_and_de_frequency	de_frequency	id_frequency
BIODIVERSITY	182	57	125
AGRICULTURE	112	46	66
COMMON AGRICULTURAL POLICY	40	32	8
ECOSYSTEM SERVICES	76	31	45
CONSERVATION	155	28	127
AGRI-ENVIRONMENT SCHEMES	103	27	76

Characterize the corpus with journals

We can also use characterize_jo. Just like characterize_kw, this function will generate a list of data frames, one data frame per communities within scimeetr_list. The first column of any of these data frames will contain the journals' names themselves. The other columns contains several journal based metrics.

jo <- characterize_jo(scimeetr_list)
head(jo$com1)

journal	citations	H	impact_factor	papers_cited	papers_within_com
J APPL ECOL	841	13	5.461039	154	21
LAND USE POLICY	533	12	4.801802	111	84
AGR ECOSYST ENVIRON	574	10	3.610063	159	37
J ENVIRON MANAGE	445	10	5.493827	81	33
J RURAL STUD	522	10	5.381443	97	17
SCIENCE	218	9	4.739130	46	NA

Characterize the corpus with authors

We can also use characterize_au. The first column of any of these data frames will contain the authors' names themselves. The other columns contains several author based metrics.

au <- characterize_au(scimeetr_list)
head(au$com1)

AU	HHL	HH	HL	H	Local_cit	Global_cit	nb_papers	local2global	fa_nb_paper_cited	fa_total_cit
HERZOG F	6	10	4	9	57	293	12	0.1945392	5	32
MATZDORF B	5	6	3	5	52	138	7	0.3768116	5	41
SCHUPBACH B	5	8	4	7	42	274	9	0.1532847	1	5
BURTON RJF	4	5	3	5	43	166	5	0.2590361	11	154
DRECHSLER M	4	5	2	4	20	64	5	0.3125000	7	33
JEANNERET P	4	6	4	5	25	143	6	0.1748252	3	10

Scimeetr object structure and navigation

A scimeetr object such as scimeetr_list contains more data than what can be seen with print and summary. A scimeetr object is in fact a list of communities list which are themselves list of up to 9 elements. Each communities contain a data.frame called dfsci. This dataframe contains all the bibliometric data that was importedinto R.

scimeetr_list$com1$dfsci

PT	AU	BA	BE	GP	AF	BF	CA	TI	SO	SE	BS	LA	DT	CT	CY	CL	SP	HO	DE	ID	C1	RP	EM	RI	OI	FU	NR	TC	Z9	U1	U2	PU	PI	PA	SN	EI	BN	J9	JI	PD	PY	VL	IS	PN	SU	SI	MA	BP	EP	AR	DI	D2	PG	WC	SC	GA	UT	PM	RECID
J	Holstead, KL; Kenyon, W; Rouillard, JJ; Hopkins, J; Galan-Diaz, C	NA	NA	NA	Holstead, K. L.; Kenyon, W.; Rouillard, J. J.; Hopkins, J.; Galan-Diaz, C.	NA		Natural flood management from the farmer's perspective: criteria that affect uptake	JOURNAL OF FLOOD RISK MANAGEMENT	NA	NA	English	Article						Natural flood management; catchment management; flood risk management; farmer decision making; land use change	DECISION-MAKING; BEHAVIOR; CONSERVATIONISTS; PARTICIPATION; ATTITUDES; SCHEMES; ENGLAND	[Holstead, K. L.; Kenyon, W.; Hopkins, J.] James Hutton Inst, Social Econ & Geog Sci Grp, Aberdeen AB15 8QH, Scotland; [Rouillard, J. J.] Univ Dundee, Sch Environm, Dundee, Scotland; [Galan-Diaz, C.] Dot Rural Univ Aberdeen, Aberdeen, Scotland	Holstead, KL (reprint author), James Hutton Inst, Social Econ & Geog Sci Grp, Aberdeen AB15 8QH, Scotland.	kirsty.holstead@hutton.ac.uk			Scottish Government's Rural and Environment Science and Analytical Services (RESAS) Division, Work Package 2.4: Methods for mitigating and adapting to flood risk	59	2	2	0	0	WILEY	HOBOKEN	111 RIVER ST, HOBOKEN 07030-5774, NJ USA	1753-318X		NA	J FLOOD RISK MANAG	J. Flood Risk Manag.	JUN	2017	10	2		NA	SI	NA	205	218		10.1111/jfr3.12129	NA	14	Environmental Sciences; Water Resources	Environmental Sciences & Ecology; Water Resources	EU4HB	WOS:000400989300008	NA	HOLSTEAD KL, 2017, J FLOOD RISK MANAG, 10, 205

Making reading lists

If we are confident that the papers contained in scimeetr_list are those for which we want a reading list we can used the function scilist to find various lists of papers. The default list given by scilist contains the seminal papers for the community analysed. That is, it rank the paper by the number of times they were cited by all the papers and list them by citation frequency.

scilist(scimeetr_list)

publication	metric	list_type
KLEIJN D, 2003, J APPL ECOL, 40, 947	113	core_papers
KLEIJN D, 2006, ECOL LETT, 9, 243	73	core_papers
KLEIJN D, 2001, NATURE, 413, 723	57	core_papers
BENTON TG, 2003, TRENDS ECOL EVOL, 18, 182	54	core_papers
PANNELL DJ, 2006, AUST J EXP AGR, 46, 1407	50	core_papers

With the parameter k, we can control the length of the reading list.

scilist(scimeetr_list, k = 3)

publication	metric	list_type
KLEIJN D, 2003, J APPL ECOL, 40, 947	113	core_papers
KLEIJN D, 2006, ECOL LETT, 9, 243	73	core_papers
KLEIJN D, 2001, NATURE, 413, 723	57	core_papers

With the parameter reading_list, we can get any of the following 12 reading lists that fits into three categories:

Core
- core_papers
- core_yr
- core_residual
Experts
- by_expert_LC
- by_expert_TC
- group_of_experts_TC
- group_of_experts_LC
Centrality
- cite_most_others
- betweeness
- closeness
- connectness
- page_rank

The default reading list is core_papers.

Core

I categorise the reading lists as core because they are reading lists of core papers as they are all a variation of the number of times papers within our community of interest refers to the paper listed. Although the number of citation is not a perfect measure of a papers importance for a community it should be a good proxy. A weekness of the number of citation as a measure of papers importance is that not all citations are equal. For example, sometimes a paper is cited because it is criticized or because it contrasts with other findings. This as been realised by others before and some have attempted to fix it by creating the concept of influential citation. Influential citation is a great concept by to be calculated it requires advance text processing and access to the full text of each papers. As it is notoriosly time consuming to get full text and even harder to get it in the right format, we are left with citation count.

Most cited per year

Using scilist with reading_list = "core_yr" will list the most cited paper for each year from three years before present to ten years before present. The parameter k controls the number of paper per year to list.

scilist(scimeetr_list, reading_list = "core_yr", k = 2)

publication	metric	list_type
GILL RJ, 2016, ADV ECOL RES, 54, 135	2	core_yr
KARPOVICH D., 2016, SAGINAW BAY OPTIMIZA	2	core_yr
PEREIRA P, 2016, LAND DEGRAD DEV, 27, 871	2	core_yr
SIMONSEN CE, 2016, J APPL ECOL, 53, 916	2	core_yr
BATARY P, 2015, CONSERV BIOL, 29, 1006	8	core_yr
PRAGER K, 2015, CURR OPIN ENV SUST, 12, 59	4	core_yr
SCHOMERS S, 2015, LAND USE POLICY, 42, 58	4	core_yr
PE'ER G, 2014, SCIENCE, 344, 1090	9	core_yr
MEICHTRY-STIER KS, 2014, AGR ECOSYST ENVIRON, 189, 101	5	core_yr
RIBEIRO PF, 2014, AGR ECOSYST ENVIRON, 183, 138	5	core_yr
BURTON RJF, 2013, LAND USE POLICY, 30, 628	25	core_yr
UTHES S, 2013, ENVIRON MANAGE, 51, 251	16	core_yr
BAUMGART-GETZ A, 2012, J ENVIRON MANAGE, 96, 17	17	core_yr
EMERY SB, 2012, J RURAL STUD, 28, 218	12	core_yr

More cited than expected

Using scilist with reading_list = "core_residual" will list the papers that diverge most from the expected number of citation for this particular paper. This can be visualised in the figure below. The point that have the biggest difference between their frequency value and the fitted blue lines are listed in the core_residual reading list.

Here is an example of the code and its result.

scilist(scimeetr_list, reading_list = "core_residual", k = 3)

publication	metric	list_type
KLEIJN D, 2003, J APPL ECOL, 40, 947	113	core_residual
MORRIS C, 1995, J RURAL STUD, 11, 51	47	core_residual
ERVIN CA, 1982, LAND ECON, 58, 277	15	core_residual

Experts

The reading lists that I categorise as expert are built from authors information. Experts within a community are identified based on the number of papers they published and the number of times each of their papers are cited.

Recent paper of a few experts

Using scilist with reading_list = "by_expert_LC" we will get a list of recent papers by one or a few experts in the community. For the option by_expert_LC, authors are ranked based on their harmonic local H-index. The H-index is a measure of an other productivity and impact. An author with an H-index of 10 means that he has published at least 10 papers with 10 or more citation each. A local H-index means that only citations from other papers in the community are counted. A harmonic local H-index means that authors do not get the full credit for each citation their paper received. It is corrected depending on the authos position in the authors list. First authors gets most of the credit, then the last author gets the second most, and the authors gets credit as a proportion of their position. Once the authors harmonic-local-H-index is found they are ranked and the m most recent publication of the k most 'expert' authors are listed as a reading list.

scilist(scimeetr_list, reading_list = "by_expert_LC", k = 2, m = 2)

publication	metric	list_type
SEREKE F, 2015, AGRON SUSTAIN DEV, 35, 759	Herzog, F h-index : 6	by_expert_LC
KELEMEN E, 2013, LAND USE POLICY, 35, 318	Herzog, F h-index : 6	by_expert_LC
MEYER C, 2016, LAND USE POLICY, 55, 352	Matzdorf, B h-index : 5	by_expert_LC
SCHOMERS S, 2015, SUSTAINABILITY-BASEL, 7, 13856	Matzdorf, B h-index : 5	by_expert_LC
SCHOMERS S, 2015, LAND USE POLICY, 42, 58	Matzdorf, B h-index : 5	by_expert_LC
SCHUPBACH B, 2016, LAND USE POLICY, 53, 27	Schupbach, B h-index : 5	by_expert_LC
AVIRON S, 2011, RESTOR ECOL, 19, 500	Schupbach, B h-index : 5	by_expert_LC
JUNGE X, 2011, BIOL CONSERV, 144, 1430	Schupbach, B h-index : 5	by_expert_LC

Using scilist with reading_list = "by_expert_TC" instead of reading_list = "by_expert_LC", notice the _TC instead of the _LC will based the ranking calculation on total citation of it's publications instead of only the local citations.

Paper of experts group

Using scilist with reading_list = "group_of_experts_LC" we will get a list of papers for which many authors are experts in the community. For this option, authors are assigned a harmonic local H-index like described in the previous section. But this time, a weighted sum of the harmonic-local-H-index of each authors of a paper is calculated.

scilist(scimeetr_list, reading_list = "group_of_experts_LC", k = 5)

publication	metric	list_type
HERZOG F, 2005, AGR ECOSYST ENVIRON, 108, 189	8.678571	group_of_experts_LC
AVIRON S, 2011, RESTOR ECOL, 19, 500	8.383333	group_of_experts_LC
AVIRON S, 2007, AGR ECOSYST ENVIRON, 122, 295	8.166667	group_of_experts_LC
AVIRON S, 2005, GRASSLAND SCI EUR, 10, 340	7.955952	group_of_experts_LC
KAMPMANN D, 2008, J NAT CONSERV, 16, 12	7.926190	group_of_experts_LC

Using scilist with reading_list = "group_of_experts_TC" instead of reading_list = "group_of_experts_LC", notice the _TC instead of the _LC will based the ranking calculation on total citation of it's publications instead of only the local citations.

Centrality

Their are several measures of nodes centrality in graph theory. The most central papers of a community of papers can be found with scilist.

Betweeness

Betweeness measures the importance of a paper in connecting two clusters of papers. Papers with a high betweeness would therefore be a paper that tend to be more interdisciplinary.

scilist(scimeetr_list, reading_list = "betweeness", k = 5)

publication	metric	list_type
UTHES S, 2013, ENVIRON MANAGE, 51, 251	0.4474377	betweeness
FISCHER J, 2012, CONSERV LETT, 5, 167	0.4051355	betweeness
JARVIS DI, 2011, CRIT REV PLANT SCI, 30, 125	0.2773863	betweeness
XIONG Y, 2010, J GEOGR SCI, 20, 389	0.1677135	betweeness
WADE MR, 2008, PHILOS T R SOC B, 363, 831	0.1211110	betweeness

Closeness

Closeness measures the average number of link between a paper and all other papers. Papers with a high closeness would therefore be a paper that tend to have a large and wide list of citations.

scilist(scimeetr_list, reading_list = "closeness", k = 5)

publication	metric	list_type
UTHES S, 2013, ENVIRON MANAGE, 51, 251	0.0327660	closeness
FISCHER J, 2012, CONSERV LETT, 5, 167	0.0327537	closeness
JARVIS DI, 2011, CRIT REV PLANT SCI, 30, 125	0.0327450	closeness
XIONG Y, 2010, J GEOGR SCI, 20, 389	0.0327312	closeness
WADE MR, 2008, PHILOS T R SOC B, 363, 831	0.0327184	closeness

Connectness

Connectness measures the number of links a paper has. Papers with a high connectness would therefore be a paper that tend to have cited what most other studies cited.

scilist(scimeetr_list, reading_list = "connectness", k = 5)

publication	metric	list_type
METTEPENNINGEN E, 2013, LAND USE POLICY, 33, 20	341	connectness
GUILLEM EE, 2013, LAND USE POLICY, 31, 565	317	connectness
UTHES S, 2013, ENVIRON MANAGE, 51, 251	305	connectness
BURTON RJF, 2013, LAND USE POLICY, 30, 628	305	connectness
WADE MR, 2008, PHILOS T R SOC B, 363, 831	295	connectness

Page rank

Page rank was developped by Larry Page at google and it's a way to measure web page importance. The algorithm was applied to directed graph, so I am not sure of the consequence of applying it on the undirected graph that we have here.

scilist(scimeetr_list, reading_list = "page_rank", k = 5)

publication	metric	list_type
MORRIS C, 2004, LAND USE POLICY, 21, 177	0.0355645	page_rank
MATHIJS E, 2003, OUTLOOK AGR, 32, 13	0.0288389	page_rank
LINDEMANN-MATTHIES P, 2010, LANDSCAPE URBAN PLAN, 98, 99	0.0256324	page_rank
WATZOLD F, 2010, BIODIVERS CONSERV, 19, 2053	0.0249663	page_rank
HERZOG F, 2005, AGR ECOSYST ENVIRON, 108, 175	0.0221489	page_rank

Cite most others

With the option cite_most_others, the papers that cite most other papers of the community can be found. This is not a centrality measure but it is also based on papers connection to each other. It should tend to find litterature review and recent papers that have an especially good grasp on the community.

scilist(scimeetr_list, reading_list = "cite_most_others", k = 5)

publication	metric	list_type
UTHES S, 2013, ENVIRON MANAGE, 51, 251	24	cite_most_others
HEJNOWICZ AP, 2016, LAND USE POLICY, 55, 240	16	cite_most_others
SCHOMERS S, 2015, SUSTAINABILITY-BASEL, 7, 13856	13	cite_most_others
SCHOMERS S, 2015, LAND USE POLICY, 42, 58	12	cite_most_others
DEDEURWAERDERE T, 2015, ECOL ECON, 119, 24	11	cite_most_others

Finding the main communities of research

In the previous sections we have looked at only the main research community. But, splitting the main community in sub-communities can provide a more detail picture of the litterature. It can also help identify and then remove irrelevant sub-communities. To achieve any of this, the sub-communities have to be identified and characterized. The function scimap, as in science map, was developped for this task. By default, the graph use bibliographic coupling to calculate connections between papers, but coupling can also be done based on abstract words (abc), title words (tic) or keywords (kec).

summary(scimap(scimeetr_list, coupling_by = 'bic', community_algorithm = 'louvain', min_com_size = 100))

## 
##  # Summary of Scimeetr #
## -----------------------
##     Number of papers:  742
##     Number of different reference:  28526
## 
##     Average number of reference per paper:  51
## 
##     Quantiles of total citation per paper: 
## 
##      0%     25%     50%     75%    100% 
##    0.00    2.00    7.00   19.75 1333.00 
## 
##     Mean number of citation per paper:  19.81536
## 
##     Average number of citation per paper per year:  1.2
## 
## 
##   Table of the 10 most mentionned keywords 
## 
##                       Keyword    Frequency
## 1                BIODIVERSITY           57
## 2                 AGRICULTURE           46
## 3  COMMON AGRICULTURAL POLICY           32
## 4          ECOSYSTEM SERVICES           31
## 5                CONSERVATION           28
## 6    AGRI-ENVIRONMENT SCHEMES           27
## 7     AGRI-ENVIRONMENT SCHEME           20
## 8  AGRI-ENVIRONMENTAL SCHEMES           19
## 9         AGRICULTURAL POLICY           18
## 10              WATER QUALITY           18
## 
## 
## 
##   Table of the 10 most productive journal 
## 
##                                             Journal    Frequency
## 1                                   LAND USE POLICY           84
## 2              AGRICULTURE ECOSYSTEMS & ENVIRONMENT           37
## 3               JOURNAL OF ENVIRONMENTAL MANAGEMENT           33
## 4                           BIOLOGICAL CONSERVATION           24
## 5                        JOURNAL OF APPLIED ECOLOGY           21
## 6                              ECOLOGICAL ECONOMICS           17
## 7                          JOURNAL OF RURAL STUDIES           17
## 8                              AGRICULTURAL SYSTEMS           14
## 9  JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT           14
## 10                     LANDSCAPE AND URBAN PLANNING           14
## 
## 
## 
##   Table of the most descriminant keywords 
## 
##           comID                      tag                   ltag$tag
## 1    com1 (742)                                                    
## 2                           BIODIVERSITY                           
## 3                           CONSERVATION                           
## 4                             MANAGEMENT                           
## 5                            AGRICULTURE                           
## 6               AGRI-ENVIRONMENT SCHEMES                           
## 7                     ECOSYSTEM SERVICES                           
## 8  com1_1 (358)                                                    
## 9                                                          ADOPTION
## 10                                                    PARTICIPATION
## 11                                                      AGRICULTURE
## 12                                                          FARMERS
## 13                                                           POLICY
## 14                                       AGRI-ENVIRONMENTAL SCHEMES
## 15 com1_3 (265)                                                    
## 16                                                     BIODIVERSITY
## 17                                         AGRI-ENVIRONMENT SCHEMES
## 18                                                       MANAGEMENT
## 19                                          AGRICULTURAL LANDSCAPES
## 20                                                        DIVERSITY
## 21                                                     CONSERVATION

summary(scimap(scimeetr_list, coupling_by = 'abc', community_algorithm = 'louvain', min_com_size = 100))

## 
##  # Summary of Scimeetr #
## -----------------------
##     Number of papers:  742
##     Number of different reference:  28526
## 
##     Average number of reference per paper:  51
## 
##     Quantiles of total citation per paper: 
## 
##      0%     25%     50%     75%    100% 
##    0.00    2.00    7.00   19.75 1333.00 
## 
##     Mean number of citation per paper:  19.81536
## 
##     Average number of citation per paper per year:  1.2
## 
## 
##   Table of the 10 most mentionned keywords 
## 
##                       Keyword    Frequency
## 1                BIODIVERSITY           57
## 2                 AGRICULTURE           46
## 3  COMMON AGRICULTURAL POLICY           32
## 4          ECOSYSTEM SERVICES           31
## 5                CONSERVATION           28
## 6    AGRI-ENVIRONMENT SCHEMES           27
## 7     AGRI-ENVIRONMENT SCHEME           20
## 8  AGRI-ENVIRONMENTAL SCHEMES           19
## 9         AGRICULTURAL POLICY           18
## 10              WATER QUALITY           18
## 
## 
## 
##   Table of the 10 most productive journal 
## 
##                                             Journal    Frequency
## 1                                   LAND USE POLICY           84
## 2              AGRICULTURE ECOSYSTEMS & ENVIRONMENT           37
## 3               JOURNAL OF ENVIRONMENTAL MANAGEMENT           33
## 4                           BIOLOGICAL CONSERVATION           24
## 5                        JOURNAL OF APPLIED ECOLOGY           21
## 6                              ECOLOGICAL ECONOMICS           17
## 7                          JOURNAL OF RURAL STUDIES           17
## 8                              AGRICULTURAL SYSTEMS           14
## 9  JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT           14
## 10                     LANDSCAPE AND URBAN PLANNING           14
## 
## 
## 
##   Table of the most descriminant keywords 
## 
##           comID                      tag                   ltag$tag
## 1    com1 (742)                                                    
## 2                           BIODIVERSITY                           
## 3                           CONSERVATION                           
## 4                             MANAGEMENT                           
## 5                            AGRICULTURE                           
## 6               AGRI-ENVIRONMENT SCHEMES                           
## 7                     ECOSYSTEM SERVICES                           
## 8  com1_2 (249)                                                    
## 9                                                          ADOPTION
## 10                                                        AUSTRALIA
## 11                                                    WATER QUALITY
## 12                                                          FARMERS
## 13                                                       INCENTIVES
## 14                                                       MANAGEMENT
## 15 com1_3 (243)                                                    
## 16                                                      AGRICULTURE
## 17                                                    PARTICIPATION
## 18                                                          SCHEMES
## 19                                       COMMON AGRICULTURAL POLICY
## 20                                                           POLICY
## 21                                       AGRI-ENVIRONMENTAL SCHEMES
## 22 com1_1 (249)                                                    
## 23                                                     BIODIVERSITY
## 24                                         AGRI-ENVIRONMENT SCHEMES
## 25                                                        DIVERSITY
## 26                                                     CONSERVATION
## 27                                                       MANAGEMENT
## 28                                          AGRICULTURAL LANDSCAPES

summary(scimap(scimeetr_list, coupling_by = 'tic', community_algorithm = 'louvain', min_com_size = 100))

## 
##  # Summary of Scimeetr #
## -----------------------
##     Number of papers:  742
##     Number of different reference:  28526
## 
##     Average number of reference per paper:  51
## 
##     Quantiles of total citation per paper: 
## 
##      0%     25%     50%     75%    100% 
##    0.00    2.00    7.00   19.75 1333.00 
## 
##     Mean number of citation per paper:  19.81536
## 
##     Average number of citation per paper per year:  1.2
## 
## 
##   Table of the 10 most mentionned keywords 
## 
##                       Keyword    Frequency
## 1                BIODIVERSITY           57
## 2                 AGRICULTURE           46
## 3  COMMON AGRICULTURAL POLICY           32
## 4          ECOSYSTEM SERVICES           31
## 5                CONSERVATION           28
## 6    AGRI-ENVIRONMENT SCHEMES           27
## 7     AGRI-ENVIRONMENT SCHEME           20
## 8  AGRI-ENVIRONMENTAL SCHEMES           19
## 9         AGRICULTURAL POLICY           18
## 10              WATER QUALITY           18
## 
## 
## 
##   Table of the 10 most productive journal 
## 
##                                             Journal    Frequency
## 1                                   LAND USE POLICY           84
## 2              AGRICULTURE ECOSYSTEMS & ENVIRONMENT           37
## 3               JOURNAL OF ENVIRONMENTAL MANAGEMENT           33
## 4                           BIOLOGICAL CONSERVATION           24
## 5                        JOURNAL OF APPLIED ECOLOGY           21
## 6                              ECOLOGICAL ECONOMICS           17
## 7                          JOURNAL OF RURAL STUDIES           17
## 8                              AGRICULTURAL SYSTEMS           14
## 9  JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT           14
## 10                     LANDSCAPE AND URBAN PLANNING           14
## 
## 
## 
##   Table of the most descriminant keywords 
## 
##           comID                      tag                   ltag$tag
## 1    com1 (742)                                                    
## 2                           BIODIVERSITY                           
## 3                           CONSERVATION                           
## 4                             MANAGEMENT                           
## 5                            AGRICULTURE                           
## 6               AGRI-ENVIRONMENT SCHEMES                           
## 7                     ECOSYSTEM SERVICES                           
## 8  com1_6 (175)                                                    
## 9                                                       AGRICULTURE
## 10                                                       MANAGEMENT
## 11                                                     CONSERVATION
## 12                                                     BIODIVERSITY
## 13                                               ECOSYSTEM SERVICES
## 14                                                  LAND-USE CHANGE
## 15 com1_4 (126)                                                    
## 16                                                       MANAGEMENT
## 17                                               ECOSYSTEM SERVICES
## 18                                                  INTENSIFICATION
## 19                                                     BIODIVERSITY
## 20                                                SOIL CONSERVATION
## 21                                        BIODIVERSITY CONSERVATION
## 22 com1_3 (131)                                                    
## 23                                                     BIODIVERSITY
## 24                                              AGRICULTURAL POLICY
## 25                                         AGRI-ENVIRONMENT SCHEMES
## 26                                                     CONSERVATION
## 27                                                         LAND-USE
## 28                                                       INDICATORS
## 29 com1_5 (161)                                                    
## 30                                                          SCHEMES
## 31                                                     BIODIVERSITY
## 32                                                     CONSERVATION
## 33                                                       MANAGEMENT
## 34                                          AGRICULTURAL LANDSCAPES
## 35                                       AGRI-ENVIRONMENTAL SCHEMES

summary(scimap(scimeetr_list, coupling_by = 'kec', community_algorithm = 'louvain', min_com_size = 100))

## 
##  # Summary of Scimeetr #
## -----------------------
##     Number of papers:  742
##     Number of different reference:  28526
## 
##     Average number of reference per paper:  51
## 
##     Quantiles of total citation per paper: 
## 
##      0%     25%     50%     75%    100% 
##    0.00    2.00    7.00   19.75 1333.00 
## 
##     Mean number of citation per paper:  19.81536
## 
##     Average number of citation per paper per year:  1.2
## 
## 
##   Table of the 10 most mentionned keywords 
## 
##                       Keyword    Frequency
## 1                BIODIVERSITY           57
## 2                 AGRICULTURE           46
## 3  COMMON AGRICULTURAL POLICY           32
## 4          ECOSYSTEM SERVICES           31
## 5                CONSERVATION           28
## 6    AGRI-ENVIRONMENT SCHEMES           27
## 7     AGRI-ENVIRONMENT SCHEME           20
## 8  AGRI-ENVIRONMENTAL SCHEMES           19
## 9         AGRICULTURAL POLICY           18
## 10              WATER QUALITY           18
## 
## 
## 
##   Table of the 10 most productive journal 
## 
##                                             Journal    Frequency
## 1                                   LAND USE POLICY           84
## 2              AGRICULTURE ECOSYSTEMS & ENVIRONMENT           37
## 3               JOURNAL OF ENVIRONMENTAL MANAGEMENT           33
## 4                           BIOLOGICAL CONSERVATION           24
## 5                        JOURNAL OF APPLIED ECOLOGY           21
## 6                              ECOLOGICAL ECONOMICS           17
## 7                          JOURNAL OF RURAL STUDIES           17
## 8                              AGRICULTURAL SYSTEMS           14
## 9  JOURNAL OF ENVIRONMENTAL PLANNING AND MANAGEMENT           14
## 10                     LANDSCAPE AND URBAN PLANNING           14
## 
## 
## 
##   Table of the most descriminant keywords 
## 
##           comID                      tag                     ltag$tag
## 1    com1 (742)                                                      
## 2                           BIODIVERSITY                             
## 3                           CONSERVATION                             
## 4                             MANAGEMENT                             
## 5                            AGRICULTURE                             
## 6               AGRI-ENVIRONMENT SCHEMES                             
## 7                     ECOSYSTEM SERVICES                             
## 8  com1_4 (312)                                                      
## 9                                                         AGRICULTURE
## 10                                                           ADOPTION
## 11                                                             POLICY
## 12                                                            FARMERS
## 13                                         AGRI-ENVIRONMENTAL SCHEMES
## 14                                                      PARTICIPATION
## 15 com1_2 (209)                                                      
## 16                                                       BIODIVERSITY
## 17                                           AGRI-ENVIRONMENT SCHEMES
## 18                                            AGRICULTURAL LANDSCAPES
## 19                                                          DIVERSITY
## 20                                                       CONSERVATION
## 21                                       AGRICULTURAL INTENSIFICATION
## 22 com1_3 (138)                                                      
## 23                                                         MANAGEMENT
## 24                                                 ECOSYSTEM SERVICES
## 25                                                       CONSERVATION
## 26                                                            SYSTEMS
## 27                                          BIODIVERSITY CONSERVATION
## 28                                                               LAND

Focusing on a sub-community

With the function focus_on, it is possible to change focus on a sub-community.

scil <- scimap(scimeetr_list)
scil

## 
## # A scimeetr object #
## ---------------------
## Number of papers:  742
## Number of communities:  5
## Names of communities:  com1 com1_1 com1_2 com1_6 com1_3
## 
## Table of the 5 most mentionned words 
## 
##                  key_words  title_words abstract_words
## 1             BIODIVERSITY CONSERVATION        FARMERS
## 2             CONSERVATION AGRICULTURAL   CONSERVATION
## 3               MANAGEMENT   MANAGEMENT   AGRICULTURAL
## 4              AGRICULTURE       POLICY     MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES      FARMERS         POLICY

subscil <- focus_on(scil, grab = 'com1_1')
subscil

## 
## # A scimeetr object #
## ---------------------
## Number of papers:  358
## Number of communities:  1
## Names of communities:  com1_1
## 
## Table of the 5 most mentionned words 
## 
##       key_words       title_words abstract_words
## 1   AGRICULTURE           FARMERS        FARMERS
## 2      ADOPTION      CONSERVATION         POLICY
## 3  CONSERVATION            POLICY  ENVIRONMENTAL
## 4 PARTICIPATION        MANAGEMENT   CONSERVATION
## 5    MANAGEMENT AGRIENVIRONMENTAL   AGRICULTURAL

Dive to a sub-community

With the function dive_to, it is possible to move down to a sub-community and keep it's sub-communities.

scil <- scimap(scimap(scimeetr_list))
scil

## 
## # A scimeetr object #
## ---------------------
## Number of papers:  742
## Number of communities:  11
## Names of communities:  com1 com1_1 com1_1_2 com1_1_1 com1_1_4 com1_2 com1_6 com1_3 com1_3_1 com1_3_3 com1_3_4
## 
## Table of the 5 most mentionned words 
## 
##                  key_words  title_words abstract_words
## 1             BIODIVERSITY CONSERVATION        FARMERS
## 2             CONSERVATION AGRICULTURAL   CONSERVATION
## 3               MANAGEMENT   MANAGEMENT   AGRICULTURAL
## 4              AGRICULTURE       POLICY     MANAGEMENT
## 5 AGRI-ENVIRONMENT SCHEMES      FARMERS         POLICY

subscil <- dive_to(scil, aim_at = 'com1_1')
subscil

## 
## # A scimeetr object #
## ---------------------
## Number of papers:  358
## Number of communities:  4
## Names of communities:  com1_1 com1_1_2 com1_1_1 com1_1_4
## 
## Table of the 5 most mentionned words 
## 
##       key_words       title_words abstract_words
## 1   AGRICULTURE           FARMERS        FARMERS
## 2      ADOPTION      CONSERVATION         POLICY
## 3  CONSERVATION            POLICY  ENVIRONMENTAL
## 4 PARTICIPATION        MANAGEMENT   CONSERVATION
## 5    MANAGEMENT AGRIENVIRONMENTAL   AGRICULTURAL

MaximeRivest / scimeetr

readme