Loading…
Dataset for an analysis of communicative aspects of finance
The article describes a step-by-step strategy for designing a universal comprehensive vision of a vast majority of financial research topics. The strategy is focused around the analysis of the retrieval results of the word processing system Serelex which is based on the semantic similarity measure....
Saved in:
Published in: | Data in brief 2017-04, Vol.11 (C), p.197-203 |
---|---|
Main Author: | |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c517t-fc7741157b96435ef3fe8fc32e0dbf28c497459d0b4df4e66d19828b4b4d81293 |
---|---|
cites | cdi_FETCH-LOGICAL-c517t-fc7741157b96435ef3fe8fc32e0dbf28c497459d0b4df4e66d19828b4b4d81293 |
container_end_page | 203 |
container_issue | C |
container_start_page | 197 |
container_title | Data in brief |
container_volume | 11 |
creator | Natalya Zavyalova |
description | The article describes a step-by-step strategy for designing a universal comprehensive vision of a vast majority of financial research topics. The strategy is focused around the analysis of the retrieval results of the word processing system Serelex which is based on the semantic similarity measure. While designing a research topic, scientists usually employ their individual background. They rely in most cases on their individual assumptions and hypotheses. The strategy, introduced in the article, highlights the method of identifying components of semantic maps which can lead to a better coverage of any scientific topic under analysis. On the example of the research field of finance we show the practical and theoretical value of semantic similarity measurements, i.e., a better coverage of the problems which might be included in the scientific analysis of financial field. At the designing stage of any research scientists are not immune to an insufficient and, thus, erroneous spectrum of problems under analysis. According to the famous maxima of St. Augustine, ‘Fallor ergo sum’, the researchers’ activities are driven along the way from one mistake to another. However, this might not be the case for the 21st century science approach. Our strategy offers an innovative methodology, according to which the number of mistakes at the initial stage of any research may be significantly reduced. The data, obtained, was used in two articles (N. Zavyalova, 2017) [7], (N. Zavyalova, 2015) [8]. The second stage of our experiment was driven towards analyzing the correlation between the language and income level of the respondents. The article contains the information about data processing. |
doi_str_mv | 10.1016/j.dib.2017.01.012 |
format | article |
fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_9e6b440f6eea4897bcbfbf2dd2cb3579</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S2352340917300124</els_id><doaj_id>oai_doaj_org_article_9e6b440f6eea4897bcbfbf2dd2cb3579</doaj_id><sourcerecordid>1872583528</sourcerecordid><originalsourceid>FETCH-LOGICAL-c517t-fc7741157b96435ef3fe8fc32e0dbf28c497459d0b4df4e66d19828b4b4d81293</originalsourceid><addsrcrecordid>eNp9UV1LHDEUDcVSxfoD-iLz2Jfd5nOSIBREWysIfWmfQz5ubJaZyZrMLuy_N-uq6EvhQJJ7zz03nIPQF4KXBJP-22oZkltSTOQSkwb6AZ1QJuiCcayP3tyP0VmtK4wxEbwVxSd0TBVlumf8BF1c29lWmLuYS2enBjvsaqpdjp3P47iZkrdz2kJn6xr8_NSIabKTh8_oY7RDhbPn8xT9_fnjz9Wvxd3vm9ury7uFF0TOi-il5IQI6XTPmYDIIqjoGQUcXKTKcy250AE7HiKHvg9EK6ocb29FqGan6PagG7JdmXVJoy07k20yT4Vc7o0tc_IDGA294xzHHsBypaXzLrYdIVDvmJB7re8HrfXGjRA8THOxwzvR950p_TP3eWsEa_axvgl8fRYo-WEDdTZjqh6GwU6QN9UQJalQzXvVqORA9SXXWiC-riHY7DM0K9MyNPsMDSYNtM2cv_3f68RLYo1wcSBAc3yboJjqE7Q0Qiotn2ZJ-o_8Izl-rKg</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1872583528</pqid></control><display><type>article</type><title>Dataset for an analysis of communicative aspects of finance</title><source>Open Access: PubMed Central</source><source>ScienceDirect®</source><creator>Natalya Zavyalova</creator><creatorcontrib>Natalya Zavyalova</creatorcontrib><description>The article describes a step-by-step strategy for designing a universal comprehensive vision of a vast majority of financial research topics. The strategy is focused around the analysis of the retrieval results of the word processing system Serelex which is based on the semantic similarity measure. While designing a research topic, scientists usually employ their individual background. They rely in most cases on their individual assumptions and hypotheses. The strategy, introduced in the article, highlights the method of identifying components of semantic maps which can lead to a better coverage of any scientific topic under analysis. On the example of the research field of finance we show the practical and theoretical value of semantic similarity measurements, i.e., a better coverage of the problems which might be included in the scientific analysis of financial field. At the designing stage of any research scientists are not immune to an insufficient and, thus, erroneous spectrum of problems under analysis. According to the famous maxima of St. Augustine, ‘Fallor ergo sum’, the researchers’ activities are driven along the way from one mistake to another. However, this might not be the case for the 21st century science approach. Our strategy offers an innovative methodology, according to which the number of mistakes at the initial stage of any research may be significantly reduced. The data, obtained, was used in two articles (N. Zavyalova, 2017) [7], (N. Zavyalova, 2015) [8]. The second stage of our experiment was driven towards analyzing the correlation between the language and income level of the respondents. The article contains the information about data processing.</description><identifier>ISSN: 2352-3409</identifier><identifier>EISSN: 2352-3409</identifier><identifier>DOI: 10.1016/j.dib.2017.01.012</identifier><identifier>PMID: 28239634</identifier><language>eng</language><publisher>Netherlands: Elsevier Inc</publisher><subject>Communication ; Data ; Finance ; Information retrieval ; Semantic similarity measure</subject><ispartof>Data in brief, 2017-04, Vol.11 (C), p.197-203</ispartof><rights>2017 The Author</rights><rights>2017 The Author 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c517t-fc7741157b96435ef3fe8fc32e0dbf28c497459d0b4df4e66d19828b4b4d81293</citedby><cites>FETCH-LOGICAL-c517t-fc7741157b96435ef3fe8fc32e0dbf28c497459d0b4df4e66d19828b4b4d81293</cites><orcidid>0000-0002-9982-9753</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5315436/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S2352340917300124$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,3549,27924,27925,45780,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28239634$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Natalya Zavyalova</creatorcontrib><title>Dataset for an analysis of communicative aspects of finance</title><title>Data in brief</title><addtitle>Data Brief</addtitle><description>The article describes a step-by-step strategy for designing a universal comprehensive vision of a vast majority of financial research topics. The strategy is focused around the analysis of the retrieval results of the word processing system Serelex which is based on the semantic similarity measure. While designing a research topic, scientists usually employ their individual background. They rely in most cases on their individual assumptions and hypotheses. The strategy, introduced in the article, highlights the method of identifying components of semantic maps which can lead to a better coverage of any scientific topic under analysis. On the example of the research field of finance we show the practical and theoretical value of semantic similarity measurements, i.e., a better coverage of the problems which might be included in the scientific analysis of financial field. At the designing stage of any research scientists are not immune to an insufficient and, thus, erroneous spectrum of problems under analysis. According to the famous maxima of St. Augustine, ‘Fallor ergo sum’, the researchers’ activities are driven along the way from one mistake to another. However, this might not be the case for the 21st century science approach. Our strategy offers an innovative methodology, according to which the number of mistakes at the initial stage of any research may be significantly reduced. The data, obtained, was used in two articles (N. Zavyalova, 2017) [7], (N. Zavyalova, 2015) [8]. The second stage of our experiment was driven towards analyzing the correlation between the language and income level of the respondents. The article contains the information about data processing.</description><subject>Communication</subject><subject>Data</subject><subject>Finance</subject><subject>Information retrieval</subject><subject>Semantic similarity measure</subject><issn>2352-3409</issn><issn>2352-3409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9UV1LHDEUDcVSxfoD-iLz2Jfd5nOSIBREWysIfWmfQz5ubJaZyZrMLuy_N-uq6EvhQJJ7zz03nIPQF4KXBJP-22oZkltSTOQSkwb6AZ1QJuiCcayP3tyP0VmtK4wxEbwVxSd0TBVlumf8BF1c29lWmLuYS2enBjvsaqpdjp3P47iZkrdz2kJn6xr8_NSIabKTh8_oY7RDhbPn8xT9_fnjz9Wvxd3vm9ury7uFF0TOi-il5IQI6XTPmYDIIqjoGQUcXKTKcy250AE7HiKHvg9EK6ocb29FqGan6PagG7JdmXVJoy07k20yT4Vc7o0tc_IDGA294xzHHsBypaXzLrYdIVDvmJB7re8HrfXGjRA8THOxwzvR950p_TP3eWsEa_axvgl8fRYo-WEDdTZjqh6GwU6QN9UQJalQzXvVqORA9SXXWiC-riHY7DM0K9MyNPsMDSYNtM2cv_3f68RLYo1wcSBAc3yboJjqE7Q0Qiotn2ZJ-o_8Izl-rKg</recordid><startdate>20170401</startdate><enddate>20170401</enddate><creator>Natalya Zavyalova</creator><general>Elsevier Inc</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-9982-9753</orcidid></search><sort><creationdate>20170401</creationdate><title>Dataset for an analysis of communicative aspects of finance</title><author>Natalya Zavyalova</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c517t-fc7741157b96435ef3fe8fc32e0dbf28c497459d0b4df4e66d19828b4b4d81293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Communication</topic><topic>Data</topic><topic>Finance</topic><topic>Information retrieval</topic><topic>Semantic similarity measure</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Natalya Zavyalova</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Data in brief</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Natalya Zavyalova</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dataset for an analysis of communicative aspects of finance</atitle><jtitle>Data in brief</jtitle><addtitle>Data Brief</addtitle><date>2017-04-01</date><risdate>2017</risdate><volume>11</volume><issue>C</issue><spage>197</spage><epage>203</epage><pages>197-203</pages><issn>2352-3409</issn><eissn>2352-3409</eissn><abstract>The article describes a step-by-step strategy for designing a universal comprehensive vision of a vast majority of financial research topics. The strategy is focused around the analysis of the retrieval results of the word processing system Serelex which is based on the semantic similarity measure. While designing a research topic, scientists usually employ their individual background. They rely in most cases on their individual assumptions and hypotheses. The strategy, introduced in the article, highlights the method of identifying components of semantic maps which can lead to a better coverage of any scientific topic under analysis. On the example of the research field of finance we show the practical and theoretical value of semantic similarity measurements, i.e., a better coverage of the problems which might be included in the scientific analysis of financial field. At the designing stage of any research scientists are not immune to an insufficient and, thus, erroneous spectrum of problems under analysis. According to the famous maxima of St. Augustine, ‘Fallor ergo sum’, the researchers’ activities are driven along the way from one mistake to another. However, this might not be the case for the 21st century science approach. Our strategy offers an innovative methodology, according to which the number of mistakes at the initial stage of any research may be significantly reduced. The data, obtained, was used in two articles (N. Zavyalova, 2017) [7], (N. Zavyalova, 2015) [8]. The second stage of our experiment was driven towards analyzing the correlation between the language and income level of the respondents. The article contains the information about data processing.</abstract><cop>Netherlands</cop><pub>Elsevier Inc</pub><pmid>28239634</pmid><doi>10.1016/j.dib.2017.01.012</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0002-9982-9753</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2352-3409 |
ispartof | Data in brief, 2017-04, Vol.11 (C), p.197-203 |
issn | 2352-3409 2352-3409 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_9e6b440f6eea4897bcbfbf2dd2cb3579 |
source | Open Access: PubMed Central; ScienceDirect® |
subjects | Communication Data Finance Information retrieval Semantic similarity measure |
title | Dataset for an analysis of communicative aspects of finance |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T00%3A00%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Dataset%20for%20an%20analysis%20of%20communicative%20aspects%20of%20finance&rft.jtitle=Data%20in%20brief&rft.au=Natalya%20Zavyalova&rft.date=2017-04-01&rft.volume=11&rft.issue=C&rft.spage=197&rft.epage=203&rft.pages=197-203&rft.issn=2352-3409&rft.eissn=2352-3409&rft_id=info:doi/10.1016/j.dib.2017.01.012&rft_dat=%3Cproquest_doaj_%3E1872583528%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c517t-fc7741157b96435ef3fe8fc32e0dbf28c497459d0b4df4e66d19828b4b4d81293%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1872583528&rft_id=info:pmid/28239634&rfr_iscdi=true |