Documentation evaluation model for social science data

Information technology and data sharing policies have made more and more social science data available for secondary analysis. In secondary data analysis, documentation plays a critical role in transferring knowledge about data from data producers to secondary users. Despite its importance, documentation of social science data has rarely been the focus of existing studies. In this paper, based on an introduction of the concept of documentation and its role in secondary data analysis, the authors proposed the Documentation Evaluation Model(DEM) for social science data. In the model, two indicators are used to evaluate the documentation for social science data: sufficiency and ease-of-use. Then the authors review the sufficiency problems of documentation, identify three factors that affect the sufficiency of documentation: users, data, and the ease-of-use of documentation, and formulate hypotheses about how those factors affect the sufficiency of documentation. In future research, a survey instrument will be created based on the model and the factors affecting the sufficiency of documentation. The survey instrument will then be applied to the secondary users of social science data. Hypotheses will be tested based on the survey data.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63090/1/1450450223_ftp.pd

Overcoming inadequate documentation

Secondary data users need three types of knowledge to analyze secondary data: knowledge about data, background knowledge necessary to understand and interpret data, and data analysis skills. Part of knowledge about data is provided by the documentation of data. Background knowledge and data analysis skills are internalized as users' absorptive capacity. When documentation and their absorptive capacity are inadequate, users need to seek outside information to use secondary data. In this paper, causes of inadequate documentation were analyzed, why and how secondary users seek outside information were reported. Then based on the findings, implications about how to facilitate secondary data use were discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78325/1/145046024_ftp.pd

What data characteristics are needed for data reuse in the domain of social sciences in Korea?

With the benefits of data sharing and reuse, data reuse have been promoted in various domains. While there are practices and discussions regarding data sharing and reuse, we still have little knowledge on what characteristics of data impact decisions on data reuse. In this sense, we aim to explore data characteristics in the context of data reuse within the domain of social sciences in Korea. For the purpose of this study, we conducted in-depth interviews with twelve re-searchers in the field of social science in terms of six dimensions: data producer, country/language, data type/collection method, procedure, accessibility, size/currency. For the producer dimension, social scientists preferred data that have been produced by an institution rather than an individual researcher. In language used in the data sets, English were more favored because researchers preferred English than any other languages. In terms of data type, quantitative and survey data types are preferred. For the procedure of data, researchers preferred original raw data with plenty of metadata and demographic information for analysis. For accessibility, there was less preference for restricted data. Lastly, for size/currency, researchers showed a preference for big size data and current data. These preliminary findings can provide better understanding about data reuse and guide improved data reuse services

Role of Communication in Data Reuse

In acknowledging the potentials of existing data, researchers’ interests in sharing and reusing data have recently emerged. However, sharing and reusing data is not a simple one-step process for researchers. Because data reusers build their work on other researchers’ findings, the process of data reuse involves various interactions and communications with other relevant parties. Exploring the nature of communications around data is thus important to fully understand data reuse practices and to support smoother processes of data reuse. This study investigates communications occurring around data during data reusers’ experiences through qualitative interview studies involving this group. This study’s results show that the communications with different stakeholders mainly support data reuse in three areas: searching, learning, and problem solving. The findings provide valuable insights into the domain of scholarly communication, data reuse, and data services

Report from the Digital Curation Curriculum Symposium (DigCCurr) 2009

The second Digital Curation Curriculum Symposium was held on April 1-3, 2009, in Chapel Hill, North Carolina, with the theme "Digital Curation Practice, Promise and Prospects". The Symposium featured sessions dealing with issues from the cutting edge of digital curation research, while others showcased recent developments in digital curation tools. At the same time, the Symposium also considered how to equip the new generation of information professionals with the necessary skills to put this research and development into practice

Factors Influencing Research Data Reuse in the Social Sciences: An Exploratory Study

The development of e-Research infrastructure has enabled data to be shared and accessed more openly. Policy mandates for data sharing have contributed to the increasing availability of research data through data repositories, which create favourable conditions for the re-use of data for purposes not always anticipated by original collectors. Despite the current efforts to promote transparency and reproducibility in science, data re-use cannot be assumed, nor merely considered a ‘thrifting’ activity where scientists shop around in data repositories considering only the ease of access to data. The lack of an integrated view of individual, social and technological influential factors to intentional and actual data re-use behaviour was the key motivator for this study. Interviews with 13 social scientists produced 25 factors that were found to influence their perceptions and experiences, including both their unsuccessful and successful attempts to re-use data. These factors were grouped into six theoretical variables: perceived benefits, perceived risks, perceived effort, social influence, facilitating conditions, and perceived re-usability. These research findings provide an in-depth understanding about the re-use of research data in the context of open science, which can be valuable in terms of theory and practice to help leverage data re-use and make publicly available data more actionable.

The Voices of Rural High School Youth: Qualitative Secondary Analysis and Youth Leadership Modeling

Preparation of youth who are ready to lead in learning, citizenship, and future careers requires development of various qualities, skills, and leadership. Leadership development during adolescence predetermines youth readiness to pursue leadership opportunities and engage in personal, professional, and community-oriented activities. Although research has offered an array of models and educational practices to foster youth leadership, there is a need for incorporating youth voice into leadership conceptualization and education. The purpose of this study was to gain an understanding of characteristics of leadership and practices of its development from a rural high school youth perspective. Secondary analysis using longitudinal qualitative data was conducted using interview, observational, and documented materials. The dataset used included youth perspectives on leadership, their motivation in and attitude toward leadership development, and their leadership behavior. The findings from this study indicate rural youth find leadership crucial to personal, family, and community change, with a focus on the development of leadership within themselves and others. Several practices of leadership development were identified by students as most contributing to their leadership. Those included volunteering and community service, collaboration with school administration and faculty, participation in leadership development course activities, and self-reflection. Through the findings of this study a working model of youth leadership was developed. Findings confirm existing scholarship concerning the need of youth leadership development and also reveal how rural high school students define leadership and encourage its development within self, an area currently not addressed in previous research on youth leadership modeling and education. A secondary goal of this study was to describe the process of qualitative secondary analysis. A discussion on differences between quantitative and qualitative secondary analyses is presented along with criteria for evaluation of qualitative data that can be used for secondary analysis. Resulting methodological suggestions can assist researchers in understanding and assessing the research capacity of qualitative secondary analysis

Using Cultural Cognition for Learning English: A Mexican Immigrant Family\u27s Perspective

The research problem focused on the 11 million Mexican immigrant families in the United States who speak little or no English. Their stated needs for English literacy, socioeconomic and academic success, and the increasing calls for xenophobic legislation throughout the nation indicated a need to investigate alternative pedagogies to compel positive social change through language fluency. In this case study, Mexican immigrant second-language learners and their descendants were asked how they wanted to learn English and if using native culture as a learning tool would help in achieving their literacy goals. Prior researchers had not asked those questions. Three adults from a 3-generation Mexican immigrant family living in Florida gave interviews to address this gap. The participants, 2 of whom were native Spanish speakers, were recruited via a Facebook call for participation, and interviews were conducted by telephone. Cultural theory served as a conceptual framework for understanding the relationship between culture and language, and for interpreting and respecting participants\u27 articulations of their experiences and opinions. Analyses of interviews and language background questionnaires were completed using pattern matching and SPSS, respectively. The key finding was that participants agreed a cultural pedagogy would be helpful in learning English. A recommendation is made to implement an experimental teaching study using cultural pedagogy as its framework. Achieving positive social change begins with removing the barriers of cultural language discrimination and allowing immigrants to reach their stated goals without loss of their cultural heritage

Beyond “Data Thrifting”: An Investigation of Factors Influencing Research Data Reuse In the Social Sciences

The development of e-Research infrastructure has enabled data to be shared and accessed more openly. Policy mandates for data sharing have contributed to the increasing availability of research data through data repositories, which create favorable conditions for the reuse of data for purposes not always anticipated by original collectors. Despite the current efforts to promote transparency and reproducibility in science, data reuse cannot be assumed, nor merely considered a “thrifting” activity where scientists shop around in data repositories considering only the ease of access to data. This research was driven by three main questions: 1) What are the factors that influence scientists’ research data reuse? 2) To what degree do these factors influence scientists’ research data reuse? and 3) To what extent do scientists reuse research data? Following a sequential mixed-method approach, this study sought to provide a more nuanced view of the underlying factors that affect social scientists’ intentions to reuse data, as well as the impact of these factors on the actual reuse of data. Findings from a preliminary small-scale exploratory study with 13 social scientists produced 25 factors that were found to influence their perceptions and experiences, including both their unsuccessful and successful attempts to reuse data. These factors were grouped into six theoretical variables: perceived benefits, perceived risks, perceived effort, social influence, facilitating conditions, and perceived reusability. The variables were articulated in a conceptual model drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT) in order to examining social scientists’ intentions and behaviors towards the reuse of research data. The proposed hierarchical component model and the research hypotheses were validated through a survey, which was distributed to 4,500 social scientists randomly selected from the Pivot/Community of Science (CoS) database. A total of 743 social scientists participated in the survey, of which 564 cases were included in the analysis. The survey data were analyzed using the Partial Least Square Structural Equation Modeling (PLS-SEM) technique, and supplemented by ad-hoc group comparison analyses. Survey results demonstrated that social scientists’ data reuse intention and reuse behavior were indeed influenced by different factors beyond frugality. More specifically, the more practical and social benefits social scientists perceive from reusing research data, the more likely they intended to reuse data. Similarly, peer and disciplinary influence had a positive effect on social scientists’ intention to reuse data collected/produced by others. On the contrary, the construct perceived risks was found to negatively influence social scientists’ intention to reuse existing research data collected by others. Facilitating conditions and intention to reuse were found to positively correlate to actual data reuse behavior. Perceived effort was found not statistically significant, indicating that reusing data from others did not involve as much effort as collecting/producing primary data. Perceived reusability failed to be measured, due to the lack of convergent validity. Ad-hoc group comparison tests found that intention and data reuse behavior depended on sub-disciplines’ traditions and the methodological approach social scientists followed. The findings of this research provide an in-depth understanding about the reuse of research data in the context of open science, and provide a collection of factors that influence social scientists’ decisions to reuse research data collected by others. Additionally, they update our knowledge of data reuse behavior and contribute to the body of data reuse literature by establishing a conceptual model that can be validated by future research. In terms of practice, it offers recommendations for policy makers, data scientists, and stakeholders from data repositories on defining strategies and initiatives to leverage data reuse and make publicly available data more actionable

A causal model to explain data reuse in science: a study in health disciplines

[EN] Investments in data infrastructures, data management, data repositories, and Open Data sharing policies and recommendations are viewed as increasingly important for scientific knowledge production. One of the underlying assumptions justifying these investments is that the more available Open Data becomes, then the greater the possibilities for creating new knowledge that can advance both science and human wellbeing. Yet efforts and investments in Open Data and other ways of data sharing only have value if data are actually reused. Recent scholarly efforts have brought forth some of the challenges and facilitators related to the reuse of data, in order to inform current and future policies and investments. However, despite these efforts, we still do not know why and how some researchers are successful in reusing data, despite the challenges they face, and why some researchers abandon the process of reusing data when facing such challenges. This dissertation aims to fill this gap by focusing on a causal explanation of the data reuse process, which it understands as being nested in broader patterns of researchers' motivations, scientific goals and decision-making strategies. The dissertation is comprised of three main elements. First, it proposes a heuristic model of the scientific actor, the bounded individual horizon (BIH) model, which understands that, on the one hand, researchers' work and careers are structured by their motivation to produce scientific contributions and rewards systems that prioritizes certain types of contributions. On the other hand, researchers' struggles to achieve their objective of creating new findings that accrue recognition and rewards occur within a frame of limited information and resources, conditioned by multiple institutional, social, and other factors. Second, the study proposes a mechanistic causal theoretical explanation that enables us to understand the data reuse process and its effects (outcomes). The data-reuse mechanism as it is called, enables us to understand how the satisficing behavior that characterizes scientific decision-making applies to the specific conditions and processes of data reuse. Third, a set of ten empirical case studies of data reuse in health research were conducted and are reported in the dissertation. These cases are analyzed and interpreted using the complementary theoretical lenses of the bounded individual horizon and the data-reuse mechanism approaches. The main findings explain that there is an apparent association between the extent and types of efforts required to reuse data, researchers' contextualized motivations, and broader goal-setting and decision-making frames. Access to data is a necessary condition for the reuse of data, yet is not sufficient for the reuse to happen. Characteristics of available data, including the context of their production, the extent of the preparation and stewarding of these data and their potential value in relation to researchers' motivations to make new scientific claims or generate background knowledge are found to be essential elements for understanding why some data reuse processes persist and succeed, while others do not. The thesis concludes that efforts and investments designed to reap the benefits of data reuse should also be expanded to include training researchers in data reuse, including to efficiently recognize opportunities, navigate the challenges of the reuse process, and be aware of and acknowledge the limitations of the use of secondary data. Without such investments, the promises and expectations linked to emerging data infrastructures, data repositories, data management guidelines and open science practices are argued to be far less likely to reach their full potential.[ES] Las inversiones en infraestructuras de datos, gestión de datos, repositorios de datos y políticas y recomendaciones de intercambio de Datos Abiertos (Open Data) se consideran cada vez más importantes para la producción del conocimiento científico. Una de las razones que justifica estas inversiones es que cuanto más Datos Abiertos haya, mayores serán las posibilidades de crear nuevo conocimiento que pueda hacer avanzar tanto la ciencia como el bienestar humano. Sin embargo, los esfuerzos y la inversión en Datos Abiertos y otras formas de compartirlos sólo tienen valor si se reutilizan realmente. Recientes trabajos académicos han puesto de manifiesto algunos de los retos y factores facilitadores relacionados con la reutilización de los datos, a fin de asesorar las políticas e inversiones actuales y futuras. Sin embargo, a pesar de esos esfuerzos, todavía desconocemos por qué y cómo algunos/as investigadores/as logran reutilizar los datos, a pesar de los retos a los que enfrentan, y por qué otros/as investigadores/as abandonan el proceso de reutilización de los datos. La presente tesis tiene por objeto llenar este vacío centrándose en una explicación causal del proceso de reutilización de los datos, que se entiende está inmersa en pautas de conducta más amplias que se relacionan con las motivaciones, los objetivos científicos y las estrategias de toma de decisiones de los/as investigadores/as. Este estudio consta de tres elementos principales. En primer lugar, propone un modelo heurístico del actor científico, el modelo del horizonte individual delimitado (BIH por su nombre en inglés, bounded individual horizon). En él se entiende que, por una parte, el trabajo y la carrera de los/as investigadores/as se estructuran en función de su motivación para producir contribuciones científicas y de los sistemas de recompensa que dan prioridad a determinados tipos de contribuciones. Por otra parte, los esfuerzos de los/as investigadores/as para lograr su objetivo de crear nuevos hallazgos que acumulen reconocimiento y recompensas se producen en un marco de información y recursos limitados, condicionados por múltiples factores institucionales, sociales y de otra índole. En segundo lugar, esta tesis propone una explicación teórica causal mecanicista que permite comprender el proceso de reutilización de los datos y sus efectos (resultados). El mecanismo de reutilización de datos (datareuse mechanism), como se denomina, nos permite comprender cómo la toma de decisiones científicas está caracterizada por una conducta que tiende a satisfacer esos objetivos en unas condiciones y procesos específicos de reutilización de datos. En tercer lugar, este estudio incluye los resultados del estudio empírico de diez estudios de casos de reutilización de datos en ciencias de la salud. Estos casos se han analizado e interpretado utilizando el modelo teórico del horizonte individual delimitado y los enfoques del mecanismo de reutilización de datos. Los resultados principales explican que existe una aparente asociación entre el alcance el alcance y tipo de esfuerzo requerido para reutilizar datos, las motivaciones contextualizadas de los/as investigadores/as y marcos más amplios de fijación de objetivos y toma de decisiones. El acceso a los datos es una condición necesaria para su reutilización, pero no es suficiente para que ésta se produzca. Para comprender por qué algunos procesos de reutilización de datos persisten y tienen éxito, mientras que otros no,son elementos esenciales: las características de los datos disponibles, incluido el contexto de su producción; el grado de preparación y administración de esos datos; y su potencial valor en relación con las motivaciones de los investigadores para hacer nuevas afirmaciones científicas o generar conocimientos de base. Este estudio concluye que los esfuerzos e inversiones destinados a aprovechar los beneficios de la reutilización de los datos también deberían ampliarse para incluir la capacitación de los/as investigadores/as en materia de reutilización de datos. En particular, debe insistirse en la capacidad para reconocer eficientemente las oportunidades, sortear los problemas del proceso de reutilización y ser conscientes y reconocer las limitaciones de la utilización de datos secundarios. Sin estas inversiones, las promesas y expectativas vinculadas a las emergentes infraestructuras de datos, los repositorios de datos, las directrices de gestión de datos y las prácticas científicas abiertas tienen muchas menos probabilidades de alcanzar su pleno potencial.[CA] Les inversions en infraestructures de dades, gestió de dades, repositoris de dades i polítiques i recomanacions d'intercanvi de Dades Obertes (Open Data) es consideren cada vegada més importants per a la producció del coneixement científic. Un dels supòsits subjacents que justifiquen aquestes inversions és que com més disponibles siguen les Dades Obertes, majors seran les possibilitats de crear nou coneixement que pugui fer avançar tant la ciència com el benestar humà. No obstant això, els esforços i les inversions en les Dades Obertes i altres maneres de compartir dades només tenen valor si les dades es reutilitzen realment. Recents investigacions acadèmics han posat de manifest alguns dels reptes i dels factors facilitadors relacionats amb la reutilització de les dades, a fi d'informar les polítiques i inversions actuals i futures. No obstant això, encara desconeixem per què i com alguns/es investigador(e)s aconsegueixen reutilitzar les dades, malgrat els reptes als quals s’enfronten, i per què altres investigador(e)s abandonen el procés de reutilització de les dades quan s'enfronten a aquests reptes. La present tesi té com a objectiu omplir aquest buit centrant-se en una explicació causal del procés de reutilització de dades, que s'entén que està associada amb pautes més àmplies derivades de les motivacions, els objectius científics i les estratègies de presa de decisions d’els/les investigador(e)s. La tesi consta de tres elements principals. En primer lloc, proposa un model heurístic de l'actor científic, el model de l'horitzó individual delimitat (BIH pel nom anglès, bounded individual horizon), que entén que, d'una banda, el treball i la carrera d’els/les investigador(e)s s'estructuren en funció de la seua motivació per a produir contribucions científiques i dels sistemes de recompensa que prioritzen determinats tipus de contribucions. D'altra banda, els esforços d’els/les investigador(e)s per aconseguir el seu objectiu d’obtenir nous resultats que acumulin reconeixement i recompenses es produeixen en un marc d'informació i recursos limitats, condicionats per múltiples factors institucionals, socials i d'altra índole. En segon lloc, aquesta tesi proposa una explicació teòrica causal mecanicista que permet comprendre el procés de reutilització de les dades i els seus efectes (resultats). El mecanisme de reutilització de dades (data-reuse mechanism), com es denomina, ens permet comprendre com el comportament satisfactori que caracteritza la presa de decisions científiques s'aplica a les condicions i processos específics de reutilització de dades. En tercer lloc, aquesta tesi inclou l'estudi empíric d'un conjunt de deu estudis de casos de reutilització de dades en ciències de la salut, així com també els resultats d’aquest estudi.. Aquests casos s'han analitzat i interpretat utilitzant les lents teòriques complementàries de l'horitzó individual delimitat i els enfocaments del mecanisme de reutilització de dades. Les principals conclusions expliquen que existeix una aparent associació entre l'abast i els tipus d'esforços necessaris per a reutilitzar dades, les motivacions contextualitzades d’els/les investigador(e)s i els marcs més amplis de fixació d'objectius i presa de decisions. L'accés a les dades és una condició necessària per a la seua reutilització, però no és suficient perquè aquesta es produeixi. Es considera que les característiques de les dades disponibles, inclòs el context de la seua producció, el grau de preparació i administració d'aquestes dades i el seu potencial valor en relació amb les motivacions d’els/les investigador(e)s per a fer noves afirmacions científiques o generar coneixements de base, són elements essencials per a comprendre per què alguns processos de reutilització de dades persisteixen i tenen èxit, mentre que uns altres no. Aquest estudi conclou que els esforços i inversions destinats a aprofitar els beneficis de la reutilització de dades també haurien d'ampliar-se per a incloure la capacitació d’els/les investigador(e)s en matèria de reutilització de dades, en particular per a reconèixer eficientment les oportunitats, superar els problemes del procés de reutilització i ser conscients i reconèixer les limitacions de la reutilització de dades secundàries. Sense aquests esforços i inversions, les promeses i expectatives vinculades a les infraestructures, repositoris i directrius de gestió de dades i les pràctiques científiques obertes tenen moltes menys probabilitats d'aconseguir el seu ple potencial.Aleixos Borrás, MI. (2020). A causal model to explain data reuse in science: a study in health disciplines [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/153164TESI