Identifying Data Sharing in Biomedical Literature

Many policies and projects now encourage investigators to share their raw research data with other scientists. Unfortunately, it is difficult to measure the effectiveness of these initiatives because data can be shared in such a variety of mechanisms and locations. We propose a novel approach to finding shared datasets: using NLP techniques to identify declarations of dataset sharing within the full text of primary research articles. Using regular expression patterns and machine learning algorithms on open access biomedical literature, our system was able to identify 61% of articles with shared datasets with 80% precision. A simpler version of our classifier achieved higher recall (86%), though lower precision (49%). We believe our results demonstrate the feasibility of this approach and hope to inspire further study of dataset retrieval techniques and policy evaluation.
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Science on a Shoestring: Building Nursing Knowledge With Limited Funding

Building the science for nursing practice has never been more important. However, shrunken federal and state research budgets mean that investigators must find alternative sources of financial support and develop projects that are less costly to carry out. New investigators often build beginning programs of research with limited funding. This article provides an overview of some cost-effective research approaches and gives suggestions for finding other sources of funding. Examples of more cost-effective research approaches include adding complementary questions to existing funded research projects; conducting primary analysis of electronic patient records and social media content; conducting secondary analysis of data from completed studies; reviewing and synthesizing previously completed research; implementing community-based participatory research; participating in collaborative research efforts such as inter-campus team research, practice-based research networks (PBRNs), and involving undergraduate and doctoral students in research efforts. Instead of relying on funding from the National Institutes of Health (NIH) and other government agencies, nurse researchers may be able to find support for research from local sources such as businesses, organizations, or clinical agencies. Investigators will increasingly have to rely on these and other creative approaches to fund and implement their research programs if granting agency budgets do not significantly expand

Access to Research Data: Addressing the Problem through Journal Data Sharing Policies

There is a growing consensus in the broader research community, including libraries and other information repositories, that sharing of research data is vital both for transparency and possible reuse. Logically the sharing should be in the form of data held in suitable repositories which is linked to effective access points such as library catalogues. The journals in which the research appears have a central role in this process. The JoRD Project atNottinghamUniversityinvestigated the current state of journal data sharing policies through a survey of sample titles, and explored the views and practices of stakeholders including the research community and its funders, publishers and editors. The project identified that although a percentage of journals did have a policy on data sharing, they were in a minority, and policies generally encouraged good practice rather than made it a firm requirement. Many of the policies examined had little to say on standardised formats for data, metadata, or the use of data repositories. If there is to be genuine data sharing, initiatives to encourage journals to set out policies that mandate sharing in well-specified and appropriate forms are essential

Human-Data Interaction: The Human Face of the Data-Driven Society

The increasing generation and collection of personal data has created a complex ecosystem, often collaborative but sometimes combative, around companies and individuals engaging in the use of these data. We propose that the interactions between these agents warrants a new topic of study: Human-Data Interaction (HDI). In this paper we discuss how HDI sits at the intersection of various disciplines, including computer science, statistics, sociology, psychology and behavioural economics. We expose the challenges that HDI raises, organised into three core themes of legibility, agency and negotiability, and we present the HDI agenda to open up a dialogue amongst interested parties in the personal and big data ecosystems

Research data sharing: developing a stakeholder-driven model for journal policies

The conclusions of research articles generally depend on bodies of data that cannot be included in the articles themselves. The sharing of this data is important for reasons of both transparency and possible reuse. Science, Technology and Medicine journals have an obvious role in facilitating sharing, but how they might do that is not yet clear. The Journal Research Data (JoRD) Project was a JISC (Joint Information Systems Committee) funded feasibility study on the possible shape of a central service on journal research data policies. The objectives of the study included, amongst other considerations: to identify the current state of journal data sharing policies and to investigate the views and practices of stakeholders to data sharing. The project confirmed that a large percentage of journals do not have a policy on data sharing, and that there are inconsistencies between the traceable journal data sharing policies. Such a state leaves authors unsure of whether they should deposit data relating to articles and where and how to share that data. In the absence of a consolidated infrastructure for the easy sharing of data, a journal data sharing model policy was developed. The model policy was developed from comparing the quantitative information gathered from analysing existing journal data policies with qualitative data collected from the stakeholders concerned. This article summarises the information gathered, outlines the process by which the model was developed and presents the model journal data sharing policy in full

Compartir los datos de investigación en ciencia: introducción al data sharing

The emergence in the scientific community of an initiative known as data sharing, consisting of sharing research data among researchers and aiming to maximize efforts and resources, is analysed. First, the concept of research data and the related technical difficulties depending on the discipline are reviewed. We also examine the motivations, origins and growth of this movement, which has had an important impact on the scientific community’s behaviour through the creation of reposi- tories and data banks, raising both technical and social challenges. Then we discuss leading funding agencies’ initiatives and scientific journals’ editorial policies promoting these practices. Finally, we examine the impact these major changes in researchers’ habits have for librarians, including the emergence of new professional profiles

Research data sharing: developing a stakeholder-driven model for journal policies

The conclusions of research articles generally depend on bodies of data that cannot be included in the articles themselves. The sharing of this data is important for reasons of both transparency and possible reuse. Science, Technology and Medicine journals have an obvious role in facilitating sharing, but how they might do that is not yet clear. The Journal Research Data (JoRD) Project was a JISC (Joint Information Systems Committee) funded feasibility study on the possible shape of a central service on journal research data policies. The objectives of the study included, amongst other considerations: to identify the current state of journal data sharing policies and to investigate the views and practices of stakeholders to data sharing. The project confirmed that a large percentage of journals do not have a policy on data sharing, and that there are inconsistencies between the traceable journal data sharing policies. Such a state leaves authors unsure of whether they should deposit data relating to articles and where and how to share that data. In the absence of a consolidated infrastructure for the easy sharing of data, a journal data sharing model policy was developed. The model policy was developed from comparing the quantitative information gathered from analysing existing journal data policies with qualitative data collected from the stakeholders concerned. This article summarises the information gathered, outlines the process by which the model was developed and presents the model journal data sharing policy in full

TÉCNICAS DE PROCESSAMENTO DE LINGUAGEM NATURAL APLICADAS AO PROCESSO DE MINERAÇÃO DE TEXTOS: RESULTADOS PRELIMINARES DE UM MAPEAMENTO SISTEMÁTICO

Text mining is an activity that aims to discover knowledge in not-structured data (textual. This process uses itself algorithms as well as known and consolidated techniques, among which can be termed Natural Language Processing (NLP) which has incremented obtained results and has justified the necessary computational effort. Objective: The aim of this study was to identify and evaluate the techniques of NLP available to perform data mining in textual databases. Method: We applied a systematic mapping study to identify, evaluate and interpret relevant studies about this research topic. Results: We identify 24 papers discussing about 11 NLP techniques applied in text mining, in which the ontology was presented as the most efficient technique throughout the years.A mineração de textos é a atividade que surgiu com o propósito de descobrir conhecimento em dados não estruturados (textuais). Este processo utiliza além de algoritmos próprios, técnicas já conhecidas e consolidadas, dentre elas o Processamento de Linguagem Natural (PLN) tem incrementado os resultados obtidos. Objetivo: Este estudo teve como objetivo identificar e avaliar as técnicas de PLN disponíveis para realizar mineração em bases de dados textuais com o intuito de discutir sobre essas técnicas a partir das experiências publicadas neste contexto. Método: Foi utilizada a técnica de mapeamento sistemático, cujo propósito é identificar, avaliar e interpretar estudos disponíveis e relevantes sobre uma determinada questão de pesquisa, executando um processo de revisão rigoroso e confiável. Resultados: Foram analisados 24 estudos aplicando 11 técnicas diferentes de PLN na mineração de textos, sendo que dentre todas essas técnicas, a ontologia se mostrou a mais recorrente e eficiente.

Compartir los datos de investigación en ciencia: introducción al data sharing

The emergence in the scientific community of an initiative known as data sharing, consisting of sharing research data among researchers and aiming to maximize efforts and resources, is analysed. First, the concept of research data and the related technical difficulties depending on the discipline are reviewed. We also examine the motivations, origins and growth of this movement, which has had an important impact on the scientific community’s behaviour through the creation of reposi- tories and data banks, raising both technical and social challenges. Then we discuss leading funding agencies’ initiatives and scientific journals’ editorial policies promoting these practices. Finally, we examine the impact these major changes in researchers’ habits have for librarians, including the emergence of new professional profiles