Automated Author ProfileYaniasih, Yaniasih
Indonesian Institute of Sciences
Yaniasih, Yaniasih
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
<p>Data becomes an important issue in conducting research activities. Each research institute and R & D requires data was documented from previous research, whether derived from the institution itself or other institutions. Currently, each work unit already has several databases, but there is yet one means to store a safe and reliable. Therefore, a large scientific data repository system is required. In addition to being a means of sharing data, the repository is also intended to provide access and preserve data. The repository is expected to support intergovernmental research collaboration.The various data held by Indonesian Institute of Sciences (LIPI)'s work units, especially the Life Science and Earth Science can be categorized as big data because it has a very large volume, variety, and velocity (high speed) needed to process the data. The data are still scattered in part still managed by individually and partly. Individual data management causes lack of access, data is only accessible to a limited audience. Lack of access leads to duplication of research, wasted government funds, and lack of benefits for further research.</p><p>----------------------------------------------------------------------</p><p>Data menjadi masalah yang penting dalam melakukan kegiatan penelitian. Setiap lembaga penelitian dan badan litbang memerlukan data-data yang dokumentasi dari penelitian sebelumnya, baik yang berasal dari institusi sendiri atau institusi lain. Saat ini masing-masing satuan kerja sudah memiliki beberapa pangkalan data, akan tetapi belum ada satu sarana untuk menyimpan yang aman dan handal. Oleh karena itu, perlu dibuat sistem repositori big data ilmiah. Selain sebagai sarana berbagi data, repositori juga dimaksudkan untuk menyediakan akses dan melestarikan data. Dengan repositori diharapkan akan mendukung kolaborasi penelitian antar lembaga. Berbagai macam data yang dimiliki oleh satuan kerja di lingkungan LIPI, khususnya Kedeputian Ilmu Hayati dan Kedeputian Kebumian dapat dikategorikan big data imiah karena memiliki volume yang sangat besar, variety (jenis) yang sangat beragam, dan velocity (kecepatan) tinggi yang dibutuhkan untuk memproses data tersebut. Data-data tersebut masih tersebar sebagian masih dikelola secara individu dan sebagian sudah dikelola oleh satuan kerja. Pengelolaan data secara individu menyebabkan kurangnya akses, data hanya dapat diakses oleh kalangan terbatas. Kurangnya akses menyebabkan terjadinya duplikasi penelitian, dana pemerintah terbuang, dan kurangnya manfaat untuk penelitian lebih lanjut.</p>
Authors
- Riyanto, Slamet ;
- Marlina, Ekawati ;
- Yaniasih, Yaniasih