Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded as knowledge commons, but research policies that better incentivise data sharing are needed to improve the quality of research results and foster scientific progress.

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What drives academic data sharing

Despite widespread support from policy makers, funding agencies, and scientific journals, academic researchers rarely make their research data available to others. At the same time, data sharing in research is attributed a vast potential for scientific progress. It allows the reproducibility of study results and the reuse of old data for new research questions. Based on a systematic review of 98 scholarly papers and an empirical survey among 603 secondary data users, we develop a conceptual framework that explains the process of data sharing from the primary researcher’s point of view. We show that this process can be divided into six descriptive categories: Data donor, research organization, research community, norms, data infrastructure, and data recipients. Drawing from our findings, we discuss theoretical implications regarding knowledge creation and dissemination as well as research policy measures to foster academic collaboration. We conclude that research data cannot be regarded a knowledge commons, but research policies that better incentivize data sharing are needed to improve the quality of research results and foster scientific progress.

What Drives Academic Data Sharing

A new spatial multi-criteria decision support tool for site selection for implementation of managed aquifer recharge.

Modelling the flood in watersheds and reducing the damages caused by this natural disaster is one of the primary objectives of watershed management. This study aims to investigate the application o...

Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models

Spatial variations in ground water quality in the corporation area of Gulbarga City located in the northern part of Karnataka State, India, have been studied using geographic information system (GIS) technique. GIS, a tool which is used for storing, analyzing and displaying spatial data is also used for investigating ground water quality information. For this study, water samples were collected from 76 of the bore wells and open wells representing the entire corporation area. The water samples were analyzed for physico-chemical parameters like TDS, TH, Cl- and NO3-, using standard techniques in the laboratory and compared with the standards. The ground water quality information maps of the entire study area have been prepared using GIS spatial interpolation technique for all the above parameters. The results obtained in this study and the spatial database established in GIS will be helpful for monitoring and managing ground water pollution in the study area. Mapping was coded for potable zones, in the absence of better alternate source and non-potable zones in the study area, in terms of water quality.

	 

	Key words: Groundwater pollution, drinking-water, physico-chemical parameters, spatial interpolation

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Groundwater quality mapping using geographic information system (GIS): A case study of Gulbarga City, Karnataka, India

In multi-sensor data fusion based on multiple features, the high dimensionality of feature space increases the runtime and computational complexity. The present study proposes a new algorithm based on the combination of random subspace (RS), linear discriminant analysis and sparse regularisation (LDASR), namely RS–LDASR for feature space dimensionality reduction, supervised feature selection, and learning. The use of RSs can effectively solve the problem of high dimensionality and high feature-to-instance ratio. The extraction of multiple features from the images raises the possibility of a correlation between features which reduce classification accuracy. In this study, after the construction of several RSs, supervised feature selection and learning based on LDASR were applied with very high accuracy. Classification and image fusion for remote sensing data analysis were tested by the implementation of feature-based fusion on two pairs of fused synthetic aperture radar and optical data. Four feature matrices were constructed using attribute profiles (APs), multi-APs (MAPs), non-negative matrix factorisation (NMF), and textural features. Support vector machine and rotation forest were applied as the base classifiers. The results show that use of RS–LDASR significantly improved the classification accuracy based on NMF plus texture features and even NMF alone.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rsn.2016.0346

Effective supervised multiple-feature learning for fused radar and optical data classification

The development of coastline detection has been the subject of several reports. An optimized fuzzy cellular automata (FCA) algorithm for SAR image edge detection, which combines newly defined cellular automata (CAs) and fuzzy rules, is proposed. An extended Moore neighborhood is used for cellular automaton. Twelve custom masks were defined to study the edge angles of the radius two neighborhood of the main pixel. Comparing these edge angles with the radius, one neighborhood of the main pixel is useful when deciding whether or not a pixel is an edge pixel. The model was tested on two sets of images. The first dataset contained optical and simulated SAR images and the second contained an Envisat ASAR image and a ScanSAR image. A 3  ×  3 Lee filter (as a preprocessing phase) was applied to each subimage containing coastlines, and the subimages were then processed using an FCA edge detector. The results were compared with those from a Sobel edge detector, Roberts edge detector, wavelet transform edge detector, and classic CA model. The results showed that the proposed method is more appropriate for edge detection of SAR images when compared with classic methods. The proposed method and wavelet transform edge detector showed good continuity, but the proposed method dealt better with speckle noise effects.

Optimized fuzzy cellular automata for synthetic aperture radar image edge detection

در این مقاله روش نوینی به منظور ارزیابی فرگشت زمین‌ساخت فعال نسبی بر پایه شاخص‌های ریخت‌سنجی (مورفومتریک) و کاربرد آنها در تکامل زمین ریخت شناسی و توپوگرافی ارائه شده است. شاخص‌های مورد استفاده شامل نسبت انشعاب، برجستگی حوضه، تراکم آبراهه، عدد ناهمواری، فراوانی آبراهه، ضریب شکل، عامل شکل، چم وخم آبراهه و نسبت کشیدگی هستند. نتیجه جمع‌بندی شاخص‌های بیان شده و تحلیل آنها، منجر به تقسیم‌بندی نسبی فعالیت زمین‌ساختی گستره مورد مطالعه از رده‌های بالا به پایین شد. حوضه آبریز رودخانه دز واقع در کمربند چین‌خورده - رانده شده زاگرس در جنوب باختری ایران، یک پهنه بسیار ایده‌آل برای آزمودن مفهوم این شاخص‌ها در پیش بینی فعالیت نسبی زمین‌ساختی بر اساس بررسی سامانه رودخانه‌ای یا جبهه کوهستان است. گستره مورد مطالعه دارای نرخ‌های متفاوتی از فعالیت زمین‌ساخت است که این مورد ناشی از برخورد قاره‌ای بین صفحه عربی و بلوک ایران که بخشی از صفحه اوراسیاست، می‌باشد.

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Assessment of relative active tectonics using morphometric analysis, case study of dez river (southwestern, iran)

The authors present the results of a study of the geomorphology of the tectonically active Zagros Structural Belt (ZSB) of southwestern Iran. More specifically the tectonic structure of the ZSB is examined using a digital elevation model (DEM) constructed from Landsat-5 Enhanced Thematic Mapper (ETM) bands 7-4-1 and ground truth. The emphasis was on detecting net erosion rates and constructing a graded profile of the Dez River that indicates that the area has been tectonically disturbed. Three topographic profiles were plotted on the DEM that are approximately perpendicular to the NW-SE direction of the Zagros main trend. Based on the initial landform shape, topography, and initial folding, net erosion rates of the study were calculated.

Remote Sensing and GIS Study of Tectonics and Net Erosion Rates in the Zagros Structural Belt, Southwestern Iran

Available hydrogeological data for the Baghmalek aquifer, southwest Iran, suggest that in order to counter progressive depletion of groundwater, but at the same time meet the growing demand on groundwater resources, artificial recharge should be considered The success of artificial recharge schemes requires the integration of many types of data and information The prime prerequisite in establishing an artificial recharge scheme using the surface spreading technique is identification of a suitable site Therefore, an integrated investigation, including a detailed assessment of surface and sub-surface geology, and hydrogeological surveys were undertaken The potential of harvesting storm waters, considering their quality and suspended loads, has been evaluated Using this background, three sites (including basins and check dam) for artificial recharge are suggested in the north and northeast of the area, where the thickness of coarse alluvium is greatest On the basis of the collected data, (1) the rate of recharge that can be achieved at the three sites is approximately 22 million m3 per year, (2) the cost–benefit ratio is 1:132, and (3) the analysis suggests that the project could recover the investment within 3 years

Kazem Rangzan

Papers

Effective supervised multiple-feature learning for fused radar and optical data classification

Optimized fuzzy cellular automata for synthetic aperture radar image edge detection

Assessment of relative active tectonics using morphometric analysis, case study of dez river (southwestern, iran)

Remote Sensing and GIS Study of Tectonics and Net Erosion Rates in the Zagros Structural Belt, Southwestern Iran

Site selection and cost-benefit analysis for artificial recharge in the Baghmalek plain, Khuzestan Province, southwest Iran