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"Summary: A comprehensive guide for scientific researchers providing everything they need to know about data management and how to organize, document, use and reuse their data."

"Manajemen data memilki peranan kunci dalam bagaimana kita mengakses, mengatur, dan mengintegrasikan data. Komunitas riset adalah salah satu domain dimana data disebarkan, contohnyadistribusi data dalam proyek, publikasi dan anggota. Tidak ada standar yang mengatur distribusi data selama ini.Oleh karena itu,value dari data cenderung menurun, contohnya dalam konteksaccessibility, discoverability, dan usability. LinkedLab merupakan sebuah usulanplatform untuk mengelola data untuk komunitas riset dengan menggunakan teknik Linked Data. Kegunaan Linked Data adalah sebuah cara yang efektif untuk mengakses, mengatur, dan mengitegrasikan data.

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Data management has a key role on how we access, organize, and integrate data. Research community is one of the domain on which data is disseminated, e.g., projects, publications, and members.There is no well-established standard for doing so, and therefore the value of the data decreases, e.g. in terms of accessibility, discoverability, and reusability. LinkedLab proposes a platform to manage data for research communites using Linked Data technique. The use of Linked Data affords a more effective way to access, organize, and integrate the data."

""It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations"-"

"Data penelitian merupakan output dari kegiatan penelitian dan aset penting bagi institusi penelitian. Research data management (RDM) merupakan aktivitas penyimpanan, akses, dan pelestarian dari data yang dihasilkan dari proyek penelitian. Implementasi RDM di institusi penting dalam mendukung berbagi data dan kolaborasi. Tujuan dari penelitian ini yaitu membangun model penilaian kesiapan RDM. Model yang dapat digunakan untuk membantu institusi penelitian dalam menilai tingkat kesiapan dan mengidentifikasi kesenjangan untuk mengembangkan strategi dalam menerapkan RDM. Model penilaian kesiapan RDM terdiri dari dua komponen, yaitu model kesiapan dan metode penilaian kesiapan. Model kesiapan dibentuk dari sejumlah faktor yang merupakan standar kriteria untuk menyiapkan institusi dalam menerapkan RDM. Kerangka kerja technology, organization, people, dan environment (TOPE) digunakan sebagai panduan dalam memilih faktor dan indikator. Fuzzy Delphi Method digunakan untuk memvalidasi faktor dan indikator yang diturunkan dari literatur. Faktor yang dihasilkan kemudian diintegrasikan dengan faktor yang diperoleh dari hasil wawancara dengan pengelola data penelitian di beberapa institusi penelitian di Indonesia. Setelah dilakukan validasi pakar, hasil akhir dari model kesiapan RDM terdiri dari empat dimensi, 13 faktor dan 42 indikator. Penelitian ini mengungkapkan bahwa lingkungan merupakan faktor kunci dari kesiapan RDM, faktor ini belum dibahas pada penelitian sebelumnya. Komponen kedua dari model penilaian kesiapan RDM yaitu metode penilaian yang terdiri dari pembobotan kriteria, instrumen penilaian, dan klasifikasi level kesiapan. Bobot dari dimensi dan faktor kesiapan ditentukan dengan menggunakan best worst method. Urutan dimensi berdasarkan besaran bobot yaitu technology, people, organization, dan environment. Besaran dari rentang nilai pada level kesiapan diperoleh berdasarkan pendapat dari para pakar. Kategorisasi dari level kesiapan RDM yaitu rendah (0 - 1,55), sedang (1,56 - 3,45), dan tinggi (3,46 - 5.00). Dalam penelitian ini, purwarupa dikembangkan sebagai sarana uji validasi dari model penilaian kesiapan yang dikembangkan. Pengujian black box menunjukkan bahwa fungsionalitas antar muka dari purwarupa berjalan dengan baik. Nilai system usability scale (SUS) sebesar 73,57 mengindikasikan bahwa antar muka dapat diterima. Sepanjang pengetahuan dari peneliti, model penilaian kesiapan yang siap pakai, dilengkapi dengan bobot dari dimensi dan faktor, dan level kesiapan belum ditemukan untuk konteks RDM khususnya untuk konteks Indonesia. Hasil dari penelitian ini dapat digunakan oleh institusi penelitian untuk menilai kesiapan mereka dan mengidentifikasi area perbaikan dan mengurangi potensi kegagalan dalam implementasi RDM.

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Research data is the output of research activities and an important asset for research institutions. Research data management (RDM) is the activity of storing, accessing, and preserving data generated from research projects. RDM adoption in institutions is crucial for fostering data sharing and collaboration. The aim of this study is to provide a model for evaluating RDM preparedness. A model that can be used to help research institutes evaluate their level of preparedness and identify any gaps before developing strategies for implementing RDM. The RDM readiness assessment model consists of two components, namely the readiness model and the readiness assessment method. The readiness model is composed of a number of factors that are prerequisites for preparing institutions to implement RDM. The technology, organization, people, and environment (TOPE) framework is used as a guide in selecting factors and indicators. The Fuzzy Delphi Method is employed to validate the factors and indicators derived from the literature. The derived factors are then integrated with those learned from interviews with research data managers at various research institutions in Indonesia. The RDM readiness model ultimately consists of four dimensions, 13 factors, and 42 indicators after expert validation. The environment, which was not previously covered in studies, is revealed in this study to be a critical aspect in RDM readiness. The assessment technique, which is made up of weighting criteria, assessment instruments, and a readiness level categorization, is the second part of the RDM readiness assessment model. The best-worst method is used to calculate the weights of the readiness dimensions and factors. The order of dimensions based on the amount of weight is technology, people, organization, and environment. Expert reviews are used to determine the size of the range of values at the level of readiness. RDM readiness levels are divided into three categories: low (0 - 1.55), medium (1.556 - 3.45), and high (3.46 - 5.00). In this study, a prototype was developed as a means of validity testing of the readiness assessment model. Black box testing shows that the interface functionality of the prototype is running well. The interface has a satisfactory system usability scale (SUS) score of 73.57. To the best of the researchers' knowledge, there are no ready-to-use readiness assessment models for the RDM context, particularly for the Indonesian environment, that include weights from dimensions and components and levels of readiness. The results of this study can be used by research institutions to assess their readiness and identify areas for improvement and reduce potential failures in RDM implementation."

"Author: Amy Mollett; Cheryl Brumley; Chris Gilson; Sierra WilliamsPublisher: Los Angeles SAGE 2017Edition/Format: Print book : EnglishView all editions and formatsSummary:This book will help researchers to maximize the impact and highlight the innovation of their research by showing them how to get the most out of social media when evaluating, presenting and disseminating their work"

"Para peneliti bahasa (language researchers), khususriya yang menekuni bidang tipologi bahasa (language typology), hampir dapat dipastikan mereka pernah mengalami satu situasi dimana mereka memperoleh data lapangan yang berbeda antara satu informan dengan informan lainnya. Artikel ini memiliki dua hipotesis, yaitu, "Mengapa penutur asli (native speakers) dari sebuah bahasa memberikan data yang berbeda dalam penelitian lapangan?" dan "Apakah perbedaan ini muncul akibat tingkat kompetensi (competence) dan performa (performance) dari penutur ash tidak merata?" Hipotesis di alas akan diulas dengan menggunakan satu konsep umum yang diberi nama kriteria antarsubjek (intersubjective criterion)"

"This book is written for behavioral scientists who want to consider adding R to their existing set of statistical tools, or want to switch to R as their main computation tool. The authors aim primarily to help practitioners of behavioral research make the transition to R. The focus is to provide practical advice on some of the widely-used statistical methods in behavioral research, using a set of notes and annotated examples. The book will also help beginners learn more about statistics and behavioral research. These are statistical techniques used by psychologists who do research on human subjects, but of course they are also relevant to researchers in others fields that do similar kinds of research. The authors emphasize practical data analytic skills so that they can be quickly incorporated into readers’ own research."