Purpose: The purpose of this paper was to examine the potential opportunities and risks of sharing agricultural research data in Tanzania identified in the existing research literature. Design/methodology/approach: The study involved a review of the literature on research data sharing practices. Findings: The findings indicate that, research data sharing have significant positive benefits among researchers such as increase high research impact; enhancing international community collaboration among researchers with same interests; improving scientific transparency and accuracy of data (Rappert and Bezuidenhout, 2016); increasing research output whereby a single dataset can be used to generate more than one article by different authors; and many more. The risks hampering data sharing practices includes researchers’ fears that data will be scooped, poached or misused (Onyancha, 2016); unreliable electric power; lack of fund to support research data sharing activities; absence of institutional governmental support for data management; perceived lack of evidence benefits (Leonelli, Rappert and Bezuidenhout, 2018); and others. However, in Tanzania research data sharing is relatively new, thus, no any governmental agency mandating or encouraging research data sharing; therefore, there is no research data management; no research open data repositories and no research data sharing policy at any agricultural institution in Tanzania. The study recommends that agricultural researchers should be sensitized to share their data, research data policy and data repositories should also be established to support data sharing practices in Tanzania. Originality and usefulness: From the available literature, this has been the first time that an effort has been made to examine the potential opportunities and risks of sharing agricultural research data in Tanzania. The study could be used by agricultural institutions and other institutions to assess the researchers’ needs in supporting research data sharing. Also, it can be used by the government and institutions to see the need of establishing open data repositories and open data policies to support research data sharing.
{"title":"Potential opportunities and risks of sharing agricultural research data in Tanzania","authors":"A. S. Katabalwa, J. Bates, Pamela Y. Abbott","doi":"10.29173/iq997","DOIUrl":"https://doi.org/10.29173/iq997","url":null,"abstract":"Purpose: The purpose of this paper was to examine the potential opportunities and risks of sharing agricultural research data in Tanzania identified in the existing research literature. \u0000Design/methodology/approach: The study involved a review of the literature on research data sharing practices. \u0000Findings: The findings indicate that, research data sharing have significant positive benefits among researchers such as increase high research impact; enhancing international community collaboration among researchers with same interests; improving scientific transparency and accuracy of data (Rappert and Bezuidenhout, 2016); increasing research output whereby a single dataset can be used to generate more than one article by different authors; and many more. The risks hampering data sharing practices includes researchers’ fears that data will be scooped, poached or misused (Onyancha, 2016); unreliable electric power; lack of fund to support research data sharing activities; absence of institutional governmental support for data management; perceived lack of evidence benefits (Leonelli, Rappert and Bezuidenhout, 2018); and others. However, in Tanzania research data sharing is relatively new, thus, no any governmental agency mandating or encouraging research data sharing; therefore, there is no research data management; no research open data repositories and no research data sharing policy at any agricultural institution in Tanzania. The study recommends that agricultural researchers should be sensitized to share their data, research data policy and data repositories should also be established to support data sharing practices in Tanzania. \u0000Originality and usefulness: From the available literature, this has been the first time that an effort has been made to examine the potential opportunities and risks of sharing agricultural research data in Tanzania. The study could be used by agricultural institutions and other institutions to assess the researchers’ needs in supporting research data sharing. Also, it can be used by the government and institutions to see the need of establishing open data repositories and open data policies to support research data sharing.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41695479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The emergence of data-driven research and demands for the establishment of Research Data Management (RDM) has created interest in academic institutions and research organizations globally. Some of the libraries especially in developed countries have started offering RDM services to their communities. Although lagging behind, some academic libraries in developing countries are at the stage of planning or implementing the service. However, the level of RDM awareness is very low among researchers, librarians and other data practitioners. The objective of this paper is to present available open resources for different data practitioners particularly researchers and librarians. It includes training resources for both researchers and librarians, Data Management Plan (DMP) tool for researchers; data repositories available for researchers to freely archive and share their research data to the local and international communities. A case study with a survey was conducted at the University of Dodoma to identify relevant RDM services so that librarians could assist researchers to make their data accessible to the local and international community. The study findings revealed a low level of RDM awareness among researchers and librarians. Over 50% of the respondent indicated their perceived knowledge as poor in the following RDM knowledge areas; DMP, data repository, long term digital preservation, funders RDM mandates, metadata standards describing data and general awareness of RDM. Therefore, this paper presents available open resources for different data practitioners to improve RDM knowledge and boost the confidence of academic and research libraries in establishing the service.
{"title":"Research data management and services: Resources for different data practitioners","authors":"G. Mushi","doi":"10.29173/iq995","DOIUrl":"https://doi.org/10.29173/iq995","url":null,"abstract":"The emergence of data-driven research and demands for the establishment of Research Data Management (RDM) has created interest in academic institutions and research organizations globally. Some of the libraries especially in developed countries have started offering RDM services to their communities. Although lagging behind, some academic libraries in developing countries are at the stage of planning or implementing the service. However, the level of RDM awareness is very low among researchers, librarians and other data practitioners. \u0000The objective of this paper is to present available open resources for different data practitioners particularly researchers and librarians. It includes training resources for both researchers and librarians, Data Management Plan (DMP) tool for researchers; data repositories available for researchers to freely archive and share their research data to the local and international communities. \u0000A case study with a survey was conducted at the University of Dodoma to identify relevant RDM services so that librarians could assist researchers to make their data accessible to the local and international community. \u0000The study findings revealed a low level of RDM awareness among researchers and librarians. Over 50% of the respondent indicated their perceived knowledge as poor in the following RDM knowledge areas; DMP, data repository, long term digital preservation, funders RDM mandates, metadata standards describing data and general awareness of RDM. Therefore, this paper presents available open resources for different data practitioners to improve RDM knowledge and boost the confidence of academic and research libraries in establishing the service.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45376750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Academic performance in primary education plays a crucial role in obtaining further educational opportunities. Despite increased focus on addressing the inequality gaps in access to education, a number of studies have shown that children living in poor families with mothers who have low educational attainments experience less success, both in school and later as adults in the workforce, than children living in more advantaged circumstances. This paper analyses the effect of mothers’ education on the numeracy and literacy learning outcomes among children in Uganda. Mining data from the 2018 Uwezo Uganda Learning Assessment survey, we explore the influence of maternal education on learning outcomes. The findings showed that the proportion of children who demonstrated the ability of competently reading and comprehending a story of primary two level increased with increasing maternal education. Whereas only 13.6% of the primary four children whose mothers had never been to school were able to read and comprehend a story (the highest level in literacy assessment), more than four times (50.7%) of the children whose mother had above senior four qualification had similar abilities. A similar trend was seen with performance in numeracy where 31.9% of primary four children whose mothers had no education at all were able to attain the highest numeracy level, compared to 59.1% for children whose mothers level of education was beyond senior four. It was further observed that slightly more than one in three (35.6%) of the primary one/two children whose mothers had never been to school were completely non numerate compared to less than one in ten (9.0%) of the children whose mothers had studied beyond senior four who were non-numerate. Given the changes in access to schooling and impact on learning yielding from the global COVID 19 pandemic, whereas the data mined was collected before this pandemic, there is need for reflection on the home schooling approach being proposed by government and other stakeholders considering that this is likely to benefit more children whose mothers have higher levels of education than those with less education or never
{"title":"Learning outcome in literacy and numeracy in Uganda: Mining Uwezo assessment data to demonstrate the importance of maternal education","authors":"Y. Lubaale, Goretti Nakabugo, Faridah Nassereka","doi":"10.29173/iq1001","DOIUrl":"https://doi.org/10.29173/iq1001","url":null,"abstract":"Academic performance in primary education plays a crucial role in obtaining further educational opportunities. Despite increased focus on addressing the inequality gaps in access to education, a number of studies have shown that children living in poor families with mothers who have low educational attainments experience less success, both in school and later as adults in the workforce, than children living in more advantaged circumstances. This paper analyses the effect of mothers’ education on the numeracy and literacy learning outcomes among children in Uganda. Mining data from the 2018 Uwezo Uganda Learning Assessment survey, we explore the influence of maternal education on learning outcomes.\u0000The findings showed that the proportion of children who demonstrated the ability of competently reading and comprehending a story of primary two level increased with increasing maternal education. Whereas only 13.6% of the primary four children whose mothers had never been to school were able to read and comprehend a story (the highest level in literacy assessment), more than four times (50.7%) of the children whose mother had above senior four qualification had similar abilities. A similar trend was seen with performance in numeracy where 31.9% of primary four children whose mothers had no education at all were able to attain the highest numeracy level, compared to 59.1% for children whose mothers level of education was beyond senior four. It was further observed that slightly more than one in three (35.6%) of the primary one/two children whose mothers had never been to school were completely non numerate compared to less than one in ten (9.0%) of the children whose mothers had studied beyond senior four who were non-numerate. Given the changes in access to schooling and impact on learning yielding from the global COVID 19 pandemic, whereas the data mined was collected before this pandemic, there is need for reflection on the home schooling approach being proposed by government and other stakeholders considering that this is likely to benefit more children whose mothers have higher levels of education than those with less education or never","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41872077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data plays a big role in educating the population on various issues that contribute to development. One of the major activities conducted after data collection is, dissemination of the data to different stakeholders. Uganda Bureau of Statistics disseminates data to its users through a number of channels. This paper discusses each method in detail and how it's used during this process. The major channel of sharing data with users is through dissemination workshops and the website. Other channels used for dissemination include the library and resource centre, social media and physical delivery to stakeholders in district public libraries. Having the above-mentioned channels of data dissemination in place, has helped UBOS remain the centre of excellence in dissemination of data to users, countrywide and in Africa.
{"title":"Data dissemination by Uganda Bureau of Statistics","authors":"Judith Nyangoma","doi":"10.29173/iq991","DOIUrl":"https://doi.org/10.29173/iq991","url":null,"abstract":"Data plays a big role in educating the population on various issues that contribute to development. One of the major activities conducted after data collection is, dissemination of the data to different stakeholders. Uganda Bureau of Statistics disseminates data to its users through a number of channels. This paper discusses each method in detail and how it's used during this process. The major channel of sharing data with users is through dissemination workshops and the website. Other channels used for dissemination include the library and resource centre, social media and physical delivery to stakeholders in district public libraries. Having the above-mentioned channels of data dissemination in place, has helped UBOS remain the centre of excellence in dissemination of data to users, countrywide and in Africa.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46548874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Universities within the California State University System are given the mandate to teach the students of the state, as is the case with many regional, public universities. This mandate places teaching first; however, research and scholarship are still required activities for reaching retention, tenure, and promotion, as well as important skills for students to practice. Data management instruction for both faculty and undergraduates is often omitted at these institutions, which fall outside of the R1 designation. This happens for a variety of reasons, including personnel and resource limitations. Such limitations disproportionately burden students from underrepresented populations, who are more heavily represented at these institutions. These students have pathways to graduate school and the digital economy, like their counterparts at R1s; thus, they are also in need of research data management skills. This paper describes and provides a scalable, low-resource model for data management instruction from the university library and integrated into a department’s capstone or final project curriculum. In the case study, students and their instructors participated in workshops and submitted data management plans as a requirement of their final project. The analysis will analyze the results of the project and focus on the broader implications of integrating research data management into undergraduate curriculum at public, regional universities. By working with faculty to integrate data management practices into their curricula, librarians reach both students and faculty members with best practices for research data management. This work also contributes to a more equitable and sustainable research landscape.
{"title":"Outside the R1: Equitable data management at the undergraduate level","authors":"Elizabeth Blackwood","doi":"10.29173/iq1011","DOIUrl":"https://doi.org/10.29173/iq1011","url":null,"abstract":"Universities within the California State University System are given the mandate to teach the students of the state, as is the case with many regional, public universities. This mandate places teaching first; however, research and scholarship are still required activities for reaching retention, tenure, and promotion, as well as important skills for students to practice. Data management instruction for both faculty and undergraduates is often omitted at these institutions, which fall outside of the R1 designation. This happens for a variety of reasons, including personnel and resource limitations. Such limitations disproportionately burden students from underrepresented populations, who are more heavily represented at these institutions. These students have pathways to graduate school and the digital economy, like their counterparts at R1s; thus, they are also in need of research data management skills. This paper describes and provides a scalable, low-resource model for data management instruction from the university library and integrated into a department’s capstone or final project curriculum. In the case study, students and their instructors participated in workshops and submitted data management plans as a requirement of their final project. The analysis will analyze the results of the project and focus on the broader implications of integrating research data management into undergraduate curriculum at public, regional universities. By working with faculty to integrate data management practices into their curricula, librarians reach both students and faculty members with best practices for research data management. This work also contributes to a more equitable and sustainable research landscape.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42395574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daria Orlowska, Colleen Fallaw, Yali Feng, Livia Garza, Ashley Hetrick, Heidi J. Imker, Hoa Luong
How do you help people improve their data management skills? For our team at the University of Illinois at Urbana-Champaign, we decided the answer was "one nudge at a time”. A study conducted by Wiley and Mischo (2016) found that Illinois researchers are aware of data services available but under-utilize them. Many researchers do not consider data management as a concern distinct from researching and producing scholarly work products. In 2017, the RDS piloted the Data Nudge – a monthly, opt-in email service to “nudge” Illinois researchers toward good data management practices, and towards utilizing data services on campus. The aim of the Data Nudge was to address the gap between knowing about a service and using it by highlighting best practices and campus resources. The topics covered in the Data Nudge center around data. Some topics are applicable to everyone, such as data back-up, documentation, and file naming conventions. Other topics are specific to Illinois, like storage options, events, and conferences. After four years, the Data Nudge has accumulated over 400 subscribers through word-of-mouth, marketing channels on campus and inclusion in subject liaisons' instructional workshops. It receives stable open rates averaging at 52% (compared to 19.44% average industry rate for Higher Education*) and many compliments from subscribers. We expect the Data Nudge to continue supplementing workshops and training as an effective means of communication to reach researchers on our campus. In the spirit of re-use, we are in the process of archiving the Data Nudge topics in a reusable format, readily adaptable by other institutions. Data Nudge link: https://go.illinois.edu/past_nudges
{"title":"Better data management, one nudge at a time","authors":"Daria Orlowska, Colleen Fallaw, Yali Feng, Livia Garza, Ashley Hetrick, Heidi J. Imker, Hoa Luong","doi":"10.29173/iq1010","DOIUrl":"https://doi.org/10.29173/iq1010","url":null,"abstract":"How do you help people improve their data management skills? For our team at the University of Illinois at Urbana-Champaign, we decided the answer was \"one nudge at a time”. \u0000A study conducted by Wiley and Mischo (2016) found that Illinois researchers are aware of data services available but under-utilize them. Many researchers do not consider data management as a concern distinct from researching and producing scholarly work products. In 2017, the RDS piloted the Data Nudge – a monthly, opt-in email service to “nudge” Illinois researchers toward good data management practices, and towards utilizing data services on campus. The aim of the Data Nudge was to address the gap between knowing about a service and using it by highlighting best practices and campus resources. \u0000The topics covered in the Data Nudge center around data. Some topics are applicable to everyone, such as data back-up, documentation, and file naming conventions. Other topics are specific to Illinois, like storage options, events, and conferences. \u0000After four years, the Data Nudge has accumulated over 400 subscribers through word-of-mouth, marketing channels on campus and inclusion in subject liaisons' instructional workshops. It receives stable open rates averaging at 52% (compared to 19.44% average industry rate for Higher Education*) and many compliments from subscribers. We expect the Data Nudge to continue supplementing workshops and training as an effective means of communication to reach researchers on our campus. In the spirit of re-use, we are in the process of archiving the Data Nudge topics in a reusable format, readily adaptable by other institutions. \u0000Data Nudge link: https://go.illinois.edu/past_nudges","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46033572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a standard documentation framework for Data Science projects, called Databook. It is a result of five years of action-research on multiple projects in several sectors of activity in France, and of a confrontation of standard theoretical Data Science processes, such as CRISP_DM, with the reality of the field. As a vector for knowledge sharing and capitalisation, the Databook has been identified as one of the main facilitators of Human Data Mediation. Transformed into an operational prototype of simple and minimalist documentation, it has since been tested then on about a hundred Data Science projects, has proven its benefits for the internal and external efficiency of Data Science projects, and can be turned into a more ambitious standard framework for data patrimony valorisation and data quality governance.
{"title":"DATABOOK : a standardised framework for dynamic documentation of algorithm design during Data Science projects","authors":"Anna Nesvijevskaia","doi":"10.29173/iq989","DOIUrl":"https://doi.org/10.29173/iq989","url":null,"abstract":"This paper proposes a standard documentation framework for Data Science projects, called Databook. It is a result of five years of action-research on multiple projects in several sectors of activity in France, and of a confrontation of standard theoretical Data Science processes, such as CRISP_DM, with the reality of the field. As a vector for knowledge sharing and capitalisation, the Databook has been identified as one of the main facilitators of Human Data Mediation. Transformed into an operational prototype of simple and minimalist documentation, it has since been tested then on about a hundred Data Science projects, has proven its benefits for the internal and external efficiency of Data Science projects, and can be turned into a more ambitious standard framework for data patrimony valorisation and data quality governance.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43417421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data management for students, researchers, and data science projects","authors":"K. Rasmussen","doi":"10.29173/iq1018","DOIUrl":"https://doi.org/10.29173/iq1018","url":null,"abstract":"","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45378332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johannes Breuer, Tarek Al Baghal, Luke S Sloan, L. Bishop, Dimitra Kondyli, Apostolos Linardis
Linking social media data with survey data is a way to combine the unique strengths and address some of the respective limitations of these two data types. As such linked data can be quite disclosive and potentially sensitive, it is important that researchers obtain informed consent from the individuals whose data are being linked. When formulating appropriate informed consent, there are several things that researchers need to take into account. Besides legal and ethical questions, key aspects to consider are the differences between platforms and data types. Depending on what type of social media data is collected, how the data are collected, and from which platform(s), different points need to be addressed in the informed consent. In this paper, we present three case studies in which survey data were linked with data from 1) Twitter, 2) Facebook, and 3) LinkedIn and discuss how the specific features of the platforms and data collection methods were covered in the informed consent. We compare the key attributes of these platforms that are relevant for the formulation of informed consent and also discuss scenarios of social media data collection and linking in which obtaining informed consent is not necessary. By presenting the specific case studies as well as general considerations, this paper is meant to provide guidance on informed consent for linked survey and social media data for both researchers and archivists working with this type of data.
{"title":"Informed consent for linking survey and social media data - Differences between platforms and data types","authors":"Johannes Breuer, Tarek Al Baghal, Luke S Sloan, L. Bishop, Dimitra Kondyli, Apostolos Linardis","doi":"10.29173/IQ988","DOIUrl":"https://doi.org/10.29173/IQ988","url":null,"abstract":"Linking social media data with survey data is a way to combine the unique strengths and address some of the respective limitations of these two data types. As such linked data can be quite disclosive and potentially sensitive, it is important that researchers obtain informed consent from the individuals whose data are being linked. When formulating appropriate informed consent, there are several things that researchers need to take into account. Besides legal and ethical questions, key aspects to consider are the differences between platforms and data types. Depending on what type of social media data is collected, how the data are collected, and from which platform(s), different points need to be addressed in the informed consent. In this paper, we present three case studies in which survey data were linked with data from 1) Twitter, 2) Facebook, and 3) LinkedIn and discuss how the specific features of the platforms and data collection methods were covered in the informed consent. We compare the key attributes of these platforms that are relevant for the formulation of informed consent and also discuss scenarios of social media data collection and linking in which obtaining informed consent is not necessary. By presenting the specific case studies as well as general considerations, this paper is meant to provide guidance on informed consent for linked survey and social media data for both researchers and archivists working with this type of data.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43225979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we address how Natural Language Processing (NLP) approaches and language technology can contribute to data services in different ways; from providing social science users with new approaches and tools to explore oral and textual data, to enhancing the search, findability and retrieval of data sources. By using linguistic approaches we are able to process data, for example using Automated Speech Recognition (ASR) and named entity recognizers (NER), extract key concepts and terms, and improve search strategies. We provide examples of how computational linguistics contribute to and facilitate the mining and analysis of oral or textual material, for example (transcribed) interviews or oral histories, and show how free open source (OS) tools can be used very easily to gain a quick overview of the key features of text, which can be further exploited as useful metadata.
{"title":"recommendation to the SSH community: Take a linguist on board","authors":"J. Beeken","doi":"10.29173/IQ992","DOIUrl":"https://doi.org/10.29173/IQ992","url":null,"abstract":"In this paper we address how Natural Language Processing (NLP) approaches and language technology can contribute to data services in different ways; from providing social science users with new approaches and tools to explore oral and textual data, to enhancing the search, findability and retrieval of data sources. By using linguistic approaches we are able to process data, for example using Automated Speech Recognition (ASR) and named entity recognizers (NER), extract key concepts and terms, and improve search strategies. We provide examples of how computational linguistics contribute to and facilitate the mining and analysis of oral or textual material, for example (transcribed) interviews or oral histories, and show how free open source (OS) tools can be used very easily to gain a quick overview of the key features of text, which can be further exploited as useful metadata.","PeriodicalId":84870,"journal":{"name":"IASSIST quarterly","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44725701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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