Exploration of the prevention and management of scientific research data misconduct based on retracted papers in the biomedical field / 中华医学科研管理杂志

Objective:By analyzing the biomedical papers retracted due to the reason that " original data not provided" , to discuss the necessity of preventing scientific data misconduct and the feasible solutions for its management.Methods:Data of the international papers that were published from Jan 2011 to Dec 2021 and retracted due to " original data not provided" were retrieved from Retraction Watch Database. The data of time distribution, institution, journal sources, reasons for retraction, and disciplinary distribution were statistically analyzed and visually processed by using software packages of Excel, Python 3.7, Gephi 0.92.Results:A total number of 529 papers published in the biomedical field were retracted due to " original data not provided" , and the time of publication and retraction occurred mainly in 2019 (27.41%) and 2021 (41.97%). In addition to the reason " original data not provided" used as search term, the reasons for withdrawal were mainly data and image problems caused by scientific data misconduct, and reasons related to the discovery and investigation process of the paper, and these reasons had a strong co-linear relationship. Besides, the 4 disciplinary of biology-cellular, biology-cancer, genetics, medicine-oncology also had a strong co-linear relationship.Conclusions:It is necessary to incorporating scientific research data management into the scientific research code of conduct, strengthen the training on the code of conduct for original scientific research records keeping, establish the scientific research data review mechanism, and promote the prevention and governance of scientific research data misconduct in the biomedical field.

The FAIRness of data management plans: an assessment of some European DMPs / FAIRness dos planos de gestão de dados: uma avaliação de alguns PGDs Europeus / FAIRness de los planes de gestión de datos: una evaluación de algunos PGDs Europeos

The FAIR principles have become a data management instrument for the academic and scientific community, since they provide a set of guiding principles to bring findability, accessibility, interoperability and reusability to data and metadata stewardship. Since their official publication in 2016 by Scientific Data ­ Nature, these principles have received worldwide recognition and have been quickly endorsed and adopted as a cornerstone of data stewardship and research policy. However, when put into practice, they occasionally result in organisational, legal and technological challenges that can lead to doubts and uncertainty as to whether the effort of implementing them is worthwhile. Soon after their publication, the European Commission and other funding agencies started to require that project proposals include a Data Management Plan (DMP) based on the FAIR principles. This paper reports on the adherence of DMPs to the FAIR principles, critically evaluating ten European DMP templates. We observed that the current FAIRness of most of these DMPs is only partly satisfactory, in that they address data best practices, findability, accessibility and sometimes preservation, but pay much less attention to metadata and interoperability.

Os princípios FAIR tornaram-se um instrumento de gestão de dados para a comunidade acadêmica e científica, uma vez que fornecem um conjunto de princípios orientadores que facilitam a localização, acessibilidade, interoperabilidade e reutilização de dados e metadados. Desde sua publicação oficial em 2016 pela Scientific Data - Nature, esses princípios receberam reconhecimento mundial e foram rapidamente endossados e adotados como pilares da gestão de dados e das políticas de pesquisa. No entanto, quando postos em prática, apresentam ocasionalmente desafios organizacionais, jurídicos e tecnológicos que podem levar a dúvidas e incertezas quanto ao esforço em implementá-los. Logo após sua publicação, a Comissão Europeia e outras agências de financiamento começaram a exigir nas suas propostas de projetos um Plano de Gestão de Dados (PGD) com base nos princípios da FAIR. Este artigo relata a aderência dos PGDs aos princípios FAIR, avaliando criticamente dez modelos europeus de PGD. Observamos que o nível de FAIRness da maioria dos PGDs analisados ainda é parcialmente satisfatório, uma vez que abordam as melhores práticas de dados, localização, acessibilidade e, às vezes, preservação, mas dão pouca atenção aos metadados e a interoperabilidade.

Los principios FAIR se han convertido en una herramienta de gestión de datos para la comunidad académica y científica, ya que proporcionan un conjunto de principios rectores que facilitan la localización, accesibilidad, interoperabilidad y reutilización de la gestión de datos y metadatos. Desde su publicación oficial en 2016 por Scientific Data - Nature, estos principios han recibido reconocimiento mundial y fueron rápidamente respaldados y adoptados como pilares de la política de investigación y gestión de datos. Sin embargo, cuando se ponen en práctica, ocasionalmente presentan desafíos organizativos, legales y tecnológicos que pueden generar dudas e incertidumbres sobre el esfuerzo para implementarlos. Poco después de su publicación, la Comisión Europea y otras agencias de financiación comenzaron a exigir en sus propuestas de proyectos un Plan de Gestión de Datos (PGD) basado en los principios de FAIR. Este artículo informa sobre la adherencia de los PGD a los principios FAIR, evaluando críticamente diez modelos europeos de PGD. Observamos que el nivel de FAIRness de la mayoría de los PGD analizados sigue siendo parcialmente insatisfactorio, ya que abordan las mejores prácticas de datos, ubicación, accesibilidad y, a veces, preservación, pero prestan poca atención a los metadatos y la interoperabilidad.

La importancia de la preservación digital para los sistemas de salud / A importância da preservação digital para os sistemas de saúde / The importance of digital preservation for health systems

En esta entrevista a Reciis, Miquel Térmens discute la importancia de la preservación digital para crear un sistema de salud que sea bueno no solo para el futuro, pero para el presente. Estamos en una fase de recopilación y almacenamiento de una gran cantidad de datos sobre el nuevo coronavirus para asegurar su rápida utilización, y su preservación a largo plazo es de interés tanto de los gobiernos como de los grupos de investigación que están trabajando a favor de las soluciones. El gran reto de nuestro presente es investigar cómo hacer preservación digital a una nueva escala, incorporando datos de las redes sociales, datos de investigación y Big Data, pero eso solo va a ser posible con normalización y planificación. Miquel Térmens Graells es doctor en Documentación por la Universidad de Barcelona, es profesor titular y decano de la Facultad de Información y Medios Audiovisuales de la misma universidad.

Compartilhamento de dados de pesquisa em saúde: iniciativas do National Institutes of Health (NIH) / Sharing health research data: National Institutes of Health (NIH) initiatives / Compartición de datos de investigación en salud: iniciativas de los National Institutes of Health (NIH)

Este artigo apresenta um levantamento e uma análise das políticas e infraestruturas de compartilhamento de dados de pesquisa em saúde adotadas pelos institutos e centros que compõem o NIH ­ National Institutes of Health (Institutos Nacionais de Saúde), organismo norte-americano de pesquisa biomédica. A partir de pesquisa bibliográfica sobre definições e conceitos abordados neste estudo, o trabalho empírico consistiu na realização de buscas, nos sites dos institutos, centros e escritório central do NIH, de iniciativas de abertura e compartilhamento de dados de pesquisa. Localizaram-se no escritório central no NIH áreas responsáveis por essas ações, e foram identificados políticas e repositórios de compartilhamento de dados cadastrados no diretório de busca da NLM ­ U.S. National Library of Medicine (Biblioteca Nacional de Medicina dos Estados Unidos). Como resultado, foram feitas análises sobre cada uma dessas iniciativas, considerando: as principais políticas de dados adotadas e seus principais objetivos, tipos e níveis de acesso e de publicação dos dados, formas de inserção e contribuição de dados, existência ou não de códigos de conduta, principais áreas de pesquisa envolvidas, tipos de repositórios (temáticos ou institucionais). As considerações finais fornecem subsídios para debates sobre diferentes tipos e abordagens de abertura e compartilhamento de dados de pesquisa científica, indicando questões pertinentes aos desdobramentos futuros da pesquisa

This paper presents a review and an analysis of health research data sharing policies and frameworks adopted by the institutes and centers of the National Institutes of Health (NIH), an American medical research agency. From a literature study of definitions and concepts approached in this study, the empirical research consisted in searches on the internet sites of the institutes, centers and the Office of the Director (central office at NIH) of initiatives to open access and sharing of research data. The responsible areas for these actions were located at the NIH Office of the Director and data sharing policies and repositories were identified in the National Library of Medicine (NLM) search directory. Then, an analysis of each one of these initiatives were carried out taking account: the main data policies adopted and their main objectives, kinds and levels of access and of published data, forms of insertion and contribution of data, whether or not codes of conduct are observed, main research areas involved, kinds of repositories (thematic or institutional). Final considerations provide inputs for discussions about different kinds and approaches to open access and scientific research data sharing, indicating suitable issues for future research developments

Este artículo presenta un levantamiento y el análisis de las políticas e infraestructuras de compartición de datos de investigación en salud adoptadas por los institutos y centros que componen el National Institutes of Health (NIH), agencia de investigación médica de los Estados Unidos. A partir de la investigación bibliográfica sobre definiciones y conceptos abordados en este estudio, el trabajo empírico consistió en la realización de búsquedas en los sitios de internet de los institutos, centros y oficina central de los NIH, de iniciativas de acceso abierto y compartición de datos de investigación. Se ubicaron en la oficina central de los NIH áreas responsables de esas acciones, y se identificaron políticas y repositorios de compartición de datos registrados en el directorio de búsqueda de la National Library of Medicine (NLM). Como resultado, se realizaron análisis sobre cada una de esas iniciativas, considerando: las principales políticas de datos adoptadas y sus principales objetivos, tipos y niveles de acceso y de publicación de los datos, formas de inserción y contribución de datos, existencia o no de normas de conductas, principales áreas de investigación involucradas, tipos de repositorios (temáticos o institucionales). Las consideraciones finales proporcionan contribuciones para debates sobre diferentes tipos y enfoques de acceso abierto y compartición de datos de investigación científica, indicando cuestiones pertinentes a los desdoblamientos futuros de la investigación

Current studies on data literacy and suggestions for its improvement in domestic colleges and universities / 中华医学图书情报杂志

The theoretical and practical achievements in domestic studies on data literacy were summarized by bib-liometric analysis and network investigation in aspects of the connotation of data literacy, requirements of data liter-acy, assessment of data literacy and education of data literacy with suggestions proposed for working out the policies of data management and data literacy education, implementing the embedded data literacy education, designing the data literacy teaching contents based on the life cycle of scientific research, and constructing the data culture.

Upgrading of the Quality Control System of the Clinical Scientific Research Sharing System / 医学信息学杂志

Based on the research platform constructed in the process of upgrading of the clinical scientific research sharing system,the paper optimizes the scientific research data structure,analyzes the problem of the original data quality,optimizes the original storage mode,and establishes a new data quality control system,so as to connect the clinical scientific research sharing system with the future development orientation of the medical big data era and precision medicine.

Os desafios dos dados de pesquisas abertos / Open research data challenges / Desafíos de los datos de investigaciones abiertos

A gestão, a disponibilização e a preservação dos dados de pesquisas são preocupações cada vez mais centrais para os pesquisadores, as instituições de pesquisas e também para as agências de fomento e revistas cientificas. Todos esses têm o objetivo de desenvolver boas práticas de pesquisa que permitam a verificação e a reutilização dos dados, a valorização dos trabalhos de construção de conjuntos de dados e o desenvolvimento de novas maneiras de pesquisar utilizando dados já produzidos. Este artigo apresenta as diferentes etapas do ciclo de vida dos dados e mostra as iniciativas internacionais e nacionais tomadas para desenvolver esse campo que tem grande interesse para a área da saúde.

Increasingly, the management, availability and preservation of research data concern researchers, research institutions as well as research funding agencies and scientific journals. All of them aim to develop good research practices allowing the data confirmation and their reuse, the improvement of the datas et buildingand the development of new ways of searching through the use of data already produced. This paper presents the different stages of the data life cycle and shows the international and national initiatives to develop these important issues to the health field

La gestión, la disponibilidad y conservación de datos procedentes de la investigación constituyen preocupaciones cada vez mayores para los investigadores, instituciones de investigación, organismos definanciación y revistas científicas. Todos tienen el objetivo de desarrollar buenas prácticas de investigación, permitiendo la verificación y la reutilización de los datos, la valorización de los trabajos de construccióndel conjunto de datos y el desarrollo de nuevas maneras de hacer investigaciones utilizando datos ya producidos. Este artículo presenta las diferentes etapas del ciclo de vida de los datos y muestra las iniciativas internacionales y nacionales para el desarrollo de este campo que tiene gran utilidad para la área de la salud.

Exploration on Development Status and Trend of Clinical Research Data in Chinese Medicine / 世界科学技术-中医药现代化

Data is an effective vehicle for clinical evaluation. High-quality data is the key to ensure universal conclusions. In the context of medical data information, based on problems of clinical research data of traditional Chinese medicine (TCM), the author described the complexity and diversity of clinical data, and existed problem of data access in extant research mode. In the era of big data, by analyzing application status of both domestic and foreign clinical data, as well as the difference of application ways on structured and unstructured data of electronic medical records, and the requirements of data in different models in clinical research, the author suggested to establish development model for data sharing in both clinical practice and scientific research in order to adapt to the diversification and information of data.

Biomedical research data management service in foreign libraries and its enlightenments / 中华医学图书情报杂志

After the scientific research data management service model in Indiana University Library and Massa-chusetts University Library of USA was investigated, the contents, methods and tools of their scientific research data management service were comparatively analyzed and certain suggestions were put forward for scientific research data management service in domestic libraries.

Biomedical research data service abroad and its enlightenments / 中华医学图书情报杂志

Data management service abroad provided by subject librarians in biomedical field for scientific researchers, and the model, contents and efficiency of subject librarians-provided biomedical research data management service in NIH were analyzed according to the need for scientific research data management service in biomedical researchers . The enlightenments of biomedical research data management service abroad on the subject service in our library were elaborated .

Desempenho de bovinos simulado pelo modelo Pampa Corte e obtido por experimentação / Animal performance simulated by Pampa Corte model with experimental records

Este trabalho tem como objetivo verificar a confiabilidade do Modelo Pampa Corte na predição de desempenho de bovinos de corte, em sistemas de pastejo. Para tanto, foram confrontados os valores preditos pelo modelo com dados disponíveis na literatura. Foram verificados coeficientes de correlação acima de 90 por cento entre os dados reais e os simulados em todas as alternativas testadas. O banco de dados do Modelo precisa ser ampliado em termos de alternativas de produtividade das forrageiras, em diferentes condições climáticas. Os parâmetros qualitativos degradabilidade da proteína bruta e fibra em detergente neutro da consorciação aveia preta e azevém necessitam ainda ser pesquisados, assim como o desempenho de animais em pastagens singulares de aveia ou azevém.

This study had the objective to evaluate Pampa Corte Model’s reliability in predicting beef cattle performance in grazing systems. For this purpose, model’s predicted values were compared to available data base of published papers. Correlation coefficients above 90 percent were obtained between simulated and real data in all tested alternatives. Model’s data base should be enlarged by forage productivity data in different climatic conditions. Mixtures of Italian ryegrass and oat need more studies to obtain qualitative parameters (crude protein degradability and neutral detergent fiber), as well, animal performance in the single pastures of oat or Italian ryegrass.

Data analysis in qualitative research: A brief guide to using Nvivo

Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers ‘cut and paste’ and use coloured pens to categorise data. Recently, the use of software specifically designed for qualitative data management greatly reduces technical sophistication and eases the laborious task, thus making the process relatively easier. A number of computer software packages has been developed to mechanise this ‘coding’ process as well as to search and retrieve data. This paper illustrates the ways in which NVivo can be used in the qualitative data analysis process. The basic features and primary tools of NVivo which assist qualitative researchers in managing and analysing their data are described