Open Data Platform Research Articles

Digitalization as a mainstream in the economic sphere in the conditions of the COVID-19 pandemic

Problem setting. The introduction of quarantine restrictions during a pandemic led to active use and development of remote technologies, digital transformation, dynamic network medium, open data platforms and other instruments. Mentioned instruments became effective means to promote innovation in all sphere of life of society. Educational institutions, research institutions, companies, organizations, facing new challenges, are trying to be as open to communication, exchange of ideas and developments, be prepared for transformations, socio-economic recovery, improvement of the system of interconnections. All this is possible only by using latest effective digital technologies, increasing the role and weight of electronic communications, virtualization of the process of information support for scientific research and development, attracting network-digital resources to the practical scope of the economy. Analysis of recent researches and publications. Problems and certain issues of digitalization are studied in the scientific works of O. Baranov, O. Dannikov, K. Sichkarenko, A. Zahorodnii, N. Kraus, O. Holoborodko, S. Hlibko, O. Kryvoruchko, V. Kokhan, V. Milash, Ye. Naiman, N. Norits, Yu. Nosenko, V. Panasiuk, Yu. Pasmor, D. Namiot, V. Fishchuk, V. Matiushko Target of research. The purpose of the article is to identify the main world trends of digestion in the COVID-19 pandemic. Article’s main body. The article presents the analysis results of the state of digitalization in the conditions of the COVID-19 pandemic. The main world trends of this process, which takes place with the involvement of the virtual environment and smart technologies to stimulate the innovative economy, are revealed. It is stated that quarantine restrictions, which affected all spheres of human life on all continents and negatively affected the economies of even developed countries, contributed to the intensification of digital transformation, making it a popular trend (the mainstream) in the economic sphere of development of the modern globalized world. It is concluded that a balanced policy of digitalization, quick adaptation of electronic communications to the tasks of stimulating the economy will significantly affect innovation, business, society. Conclusions and prospects for the development. The researches confirmed that the effect of digitalization will find its manifestation in the digital transformation, diversification of the economy, in formation of a modern business environment, innovative industries, in supporting of open science to overcome the structural weakness of the economy and the development of smart specializations, the development of startups, business incubators, science and technology centers, industrial parks, which are acting as drivers of interactive renewal of socio-economic processes, ensuring the welfare of the population.

LAGOS‐US LOCUS v1.0: Data module of location, identifiers, and physical characteristics of lakes and their watersheds in the conterminous U.S.

AbstractMacroscale studies of aquatic ecosystems are needed to address many contemporary broad‐scale problems related to global change, particularly to inform national‐scale environmental policies. In this data paper, we fill two important gaps in data availability for lakes at the scale of the conterminous U.S., the lack of: (1) high‐resolution geographic representations of lakes and their watersheds and (2) an open data platform to connect disparate U.S. national‐scale lake datasets. We describe the LAGOS‐US LOCUS v1.0 data module that includes a detailed User Guide, which is part of the LAGOS‐US extensible research‐ready open data platform. This platform can be used to study the 479,950 lakes and reservoirs larger than or equal to 1 ha across the entire conterminous U.S. at multiple scales of space and time. The LOCUS module includes spatially‐explicit information on lakes and their watersheds, including location information, physical characteristics, and identifiers of other important national‐scale datasets.

Lots of movement, little progress: a review of reptile home range literature.

Reptiles are the most species-rich terrestrial vertebrate group with a broad diversity of life history traits. Biotelemetry is an essential methodology for studying reptiles as it compensates for several limitations when studying their natural history. We evaluated trends in terrestrial reptile spatial ecology studies focusing upon quantifying home ranges for the past twenty years. We assessed 290 English-language reptile home range studies published from 2000–2019 via a structured literature review investigating publications’ study location, taxonomic group, methodology, reporting, and analytical techniques. Substantial biases remain in both location and taxonomic groups in the literature, with nearly half of all studies (45%) originating from the USA. Snakes were most often studied, and crocodiles were least often studied, while testudines tended to have the greatest within study sample sizes. More than half of all studies lacked critical methodological details, limiting the number of studies for inclusion in future meta-analyses (55% of studies lacked information on individual tracking durations, and 51% lacked sufficient information on the number of times researchers recorded positions). Studies continue to rely on outdated methods to quantify space-use (including Minimum Convex Polygons and Kernel Density Estimators), often failing to report subtleties regarding decisions that have substantial impact on home range area estimates. Moving forward researchers can select a suite of appropriate analytical techniques tailored to their research question (dynamic Brownian Bridge Movement Models for within sample interpolation, and autocorrelated Kernel Density Estimators for beyond sample extrapolation). Only 1.4% of all evaluated studies linked to available and usable telemetry data, further hindering scientific consensus. We ultimately implore herpetologists to adopt transparent reporting practices and make liberal use of open data platforms to maximize progress in the field of reptile spatial ecology.

From Available to Actionable Data: An Exploration of Expert and Re-users Views of Open Data

ABSTRACT The level of open data re-utilization was still low 10 years after the launch of the Basque Government’s open data platform, when, unexpectedly, the COVID-19 pandemic struck. Suddenly, charts and infographics—fed on open data—were lingua franca; not only experts but also ordinary citizens were demanding understandable data to make decisions. The motto during the confinement was “let’s flatten the curve.” This article relies on participant observation of a three-day workshop, interviews with experts and open data re-users (N=15), and an analysis of urban projects that rely on open data (N=78). Data collection was conducted before the first wave and after the third wave of the COVID-19 pandemic, from November 27, 2019, to February 17, 2021, allowing researchers to make comparisons. We find that citizens are pushing for actionable open data; that is, data embedding the attributes that make them useful and usable. This includes integrating data literacy and citizens’ inputs and forming interdisciplinary teams of people inside and outside the government. This article proposes a definition for actionable data, which can be scalable to other realities.

Logistics tracking system based on decentralized IoT and blockchain platform

<p class="western" align="justify">Most of supply chain networks have encountered difficulties when trying to integrate all components and stakeholders (Customers, warehouse, transportation, and raw material production. Etc...), which make supply chain management system suffering from a lack of efficiency and transparency that make a steady increase in good’s falsification and consumer’s disappointment. All information about good’s production, storage and transportation should flow clearly during all stages of the supply chain by tracking and authenticating to avoid product’s contamination or fraud in the network. Current tracking IoT-based systems are built on top of centralized architecture and this leaves a gap for tampering and false information especially in urban area, but also the exsiting solutions cannot allow the information to be shared with authority or consumers. To effectively assess and assure traceability and transparency this paper propose an approach using a distributed ledger (blockchain) besides a configuable IoT-based system to take into account all needed data including specific case of urban area, with an opendata platform at the disposal of stockholders, authority and consumers.</p>

Deep learning based real-time tourist spots detection and recognition mechanism.

More and more information on tourist spots is being represented as pictures rather than text. Consequently, tourists who are interested in a specific attraction shown in pictures may have no idea how to perform a text search to get more information about the interesting tourist spots. In the view of this problem and to enhance the competitiveness of the tourism market, this research proposes an innovative tourist spot identification mechanism, which is based on deep learning-based object detection technology, for real-time detection and identification of tourist spots by taking pictures on location or retrieving images from the Internet. This research establishes a tourist spot recognition system, which is a You Only Look Once version 3 model built in Tensorflow AI framework, and is used to identify tourist attractions by taking pictures with a smartphone's camera. To verify the possibility, a set of tourist spots in Hsinchu City, Taiwan is taken as an example. Currently, the tourist spot recognition system of this research can identify 28 tourist spots in Hsinchu. In addition to the attraction recognition feature, tourists can further use this tourist spot recognition system to obtain more information about 77 tourist spots from the Hsinchu City Government Information Open Data Platform, and then make dynamic travel itinerary planning and Google MAP navigation. Compared with other deep learning models using Faster region-convolutional neural networks or Single-Shot Multibox Detector algorithms for the same data set, the recognition time by the models using You Only Look Once version 3, Faster region-convolutional neural networks, and Single-Shot Multibox Detector algorithms are respectively 4.5, 5, and 9 s, and the mean average precision for each when IoU = 0.6 is 88.63%, 85%, and 43.19%, respectively. The performance experimental results of this research show the model using the You Only Look Once version 3 algorithm is more efficient and precise than the models using the Faster region-convolutional neural networks or the Single-Shot Multibox Detector algorithms, where You Only Look Once version 3 and Single-Shot Multibox Detector are one-stage learning architectures with efficient features, and Faster region-convolutional neural networks is a two-stage learning architecture with precise features.

An effective and elastic blockchain-based provenance preserving solution for the open data

PurposeIn the open data context, the shared data could come through many transformation processes, originating from many sources, which exposes the risk of non-authentic data. Moreover, each data set has different properties, shared under various licenses, which means the updated data could change its characteristics and related policies. This paper aims to introduce an effective and elastic solution to keep track of data changes and manage their characteristics within the open data platform. These changes have to be immutable to avoid violated modification and could be used as the certified provenance to improve the quality of data.Design/methodology/approachThis paper will propose a pragmatic solution that focuses on the combination of comprehensive knowledge archive network – the broadest used open data platform and hyperledger fabric blockchain to ensure all the changes are immutable and transparent. As using smart contracts plus a standard provenance data format, all processes are running automatically and could be extended to integrate with other provenance systems and so the introduced solution is quite flexible to be used in different open data ecosystems and real-world application domains.FindingsThe research involves some related studies about the provenance system. This study finds out that most of the studies are focused on the commercial sector or applicable to a specific domain and not relevant for the open-data section. To show that the proposed solution is a logical and feasible direction, this paper conducts an experimental sample to validate the result. The testing model is running successfully with an elastic system architect and promising overall performance.Originality/valueOpen data is the future of many businesses but still does not receive enough attention from the research community. The paper contributes a novel approach to protect the provenance of open data.

OpenEHR Mapper - A Tool to Fuse Clinical and Genomic Data Using the openEHR Standard.

Precision medicine is an emerging and important field for health care. Molecular tumor boards use a combination of clinical and molecular data, such as somatic tumor mutations to decide on personalized therapies for patients who have run out of standard treatment options. Personalized treatment decisions require clinical data from the hospital information system and mutation data to be accessible in a structured way. Here we introduce an open data platform to meet these requirements. We use the openEHR standard to create an expert-curated data model that is stored in a vendor-neutral format. Clinical and molecular patient data is integrated into cBioPortal, a warehousing solution for cancer genomic studies that is extended for use in clinical routine for molecular tumor boards. For data integration, we developed openEHR Mapper, a tool that allows to (i) process input data, (ii) communicate with the openEHR repository, and (iii) export the data to cBioPortal. We benchmarked the mapper performance using XML and JSON as serialization format and added caching capabilities as well as multi-threading to the openEHR Mapper.

Ranking hospitals’ burn care capacity using cluster analysis on open government data

Background and objectiveTo deal with burn mass casualty incidents (BMCIs), various countries have established national or regional BMCI emergency response plans (ERPs). A burn care capacity ranking model for hospitals can play an integral role in ERPs by providing essential information to emergency medical services for distributing and handling mass burn patients. Ranking models vary across countries and contexts. However, Taiwan has had no such model. The study aims to develop a ranking model for classifying hospitals’ burn care capacity in preparation for the development of a national BMCI ERP. MethodsMultiple methods were adopted. An expert panel provided consultations on data selection and clustering validation. Data on 116 variables from 535 hospitals were collected via open data platforms under the Ministry of Health and Welfare. Data selection and streamlining was conducted to determine 42 variables for cluster analysis. SAS 9.4 was used to analyze the data set -via a hierarchical cluster analysis using Ward's method, followed by a tree-based model analysis to identify the criteria for each cluster. Both internal and external cluster validation were performed. ResultsFour clusters of burn care capacity were determined to be a suitable number of clusters. All hospitals were arranged into capacity levels accordingly. Results of the Kruskal-Wallis test showed that the difference between clusters were significant. Tree-based model analysis revealed four determining variables, among which the refined level of emergency care responsibility hospital was found to be most influential on the clustering process. Responses from the questionnaire were used as an external validation tool to corroborate with the cluster analysis results. ConclusionThe use of open government data and cluster analysis was suitable for developing a ranking model to determine hospitals’ burn care capacity levels in Taiwan. The proposed ranking model can be used to develop a BMCI emergency response plan and can also serve as a reference for using cluster analysis with open government data to rank care capacity or quality in other domains.

Development Research at High Geographic Resolution: An Analysis of Night-Lights, Firms, and Poverty in India Using the SHRUG Open Data Platform

Abstract The SHRUG is an open data platform describing multidimensional socioeconomic development across 600,000 villages and towns in India. This paper presents three illustrative analyses only possible with high-resolution data. First, it confirms that nighttime lights are highly significant proxies for population, employment, per capita consumption, and electrification at very local levels. However, elasticities between night-lights and these variables are far lower in time series than in cross section, and vary widely across context and level of aggregation. Next, this study shows that the distribution of manufacturing employment across villages follows a power law: the majority of rural Indians have considerably less access to manufacturing employment than is suggested by aggregate data. Third, a poverty mapping exercise explores local heterogeneity in living standards and estimates the potential targeting improvement from allocating programs at the village—rather than at the district—level. The SHRUG can serve as a model for open high-resolution data in developing countries.

Discipline layout and prospects of geological sciences in the National Natural Science Foundation of China

One of the major tasks in the reform of the National Natural Science Foundation of China during the new period is to sort out the knowledge system and optimize the discipline layout after finishing the adjustment of application codes. Based on the 14th Five-year Geosciences Development Plan of the National Natural Science Foundation of China, we propose a new discipline layout for the geological sciences (GS) in China. GS should focus on the prior fields and core scientific questions of “Earth system sciences and deep earth, deep sea, and deep space”, and in their respective sub-disciplines. While preserving essential traditional codes within these disciplines, we suggested to obsolete or merge outdated codes and raise special attention to new-rising subjects or interdisciplinary. According to the sub-disciplines’ characteristics, the geological sciences could be divided into four categories, i.e., basic, applied, cutting-edge and interdisciplinary subjects, and technology supporting subjects. Under the Earth system sciences framework, the authors suggest focusing on the core of basic disciplines, enrich applied fields, expand the cutting-edge and interdisciplinary disciplines, strengthen technological support, and promote a balanced development of sub-disciplines. Based on the investigation results of the discipline strategy, important sub-fields and headings of the GS are proposed. The research done at the GS should broaden its international scope and actively answer the major national requirements, with particular attention to crucial scientific problems in social and economic development. In short, the DGS should centralize efforts on the basic scientific research and national strategic needs. Following the development strategy of the Earth system sciences and deep earth, deep sea, and deep space, the authors suggest that improvements of the research ways and methods in the field of geological sciences, interdisciplinary cooperation and integration, promotions of new fields and sub-disciplines are all necessary. It is suggested to focus on the Earth system sciences, and continue to modify and merge the subjects following the guide of “Grand Geology”. More important, the authors suggest utilizing the rich geological resources in China to promote the development of geological sciences, enhance the international cooperation, and accelerate constructing an open geological big data platform.

Managing and activating open data through the websites of Saudi public universities

The study aims to know the current status of open data platforms in Saudigovernment’s universities and to analyze them according to standardized criteria.The comparison between them have done to know the extent of activation ofopen data and the similarities and differences among them. Therefore, thecontribution of this study is to develop them and provide the best model ofplatform open data among Saudi universities. The survey methodology was usedto survey 29 public universities in Saudi Arabia to know their effectiveness inopen data management.Furthermore, the analytical descriptive approach was used to describeand analyze open data platforms in Saudi government’s universities and thecomparative approach was used to compare them through the checklist anddirect observation. One of the most important results of this study is theavailability of open data platforms across 21 Saudi government’s universities.However, most of them achieve less than the average of the standard criteria ofevaluating the open data platforms.Six government’s universities in Saudi Arabia achieved intermediatepercentages in their open data platforms. These universities are the SaudiElectronic University, Tabuk University, King Abdulaziz University, King FaisalUniversity, Majma'a University, and Imam Abdul Rahman Bin Faisal University.The open data platform at Prince Sattam bin Abdul Aziz University has the firstrank where it achieved the highest percentage of 66.7%. The open data platforms at King Saud University and Najran Universityachieved 60.6%. However, al Baha open data platform received the lowestpercentage of 18.2%. In conclusion, open data portals in all Saudi publicuniversities have not achieved the completeness percentage and they shouldhave development. Consequently, the most important recommendations of thestudy are open data should be available in all sites of the Saudi governmentuniversities. In addition, all portals and open data platforms should havedevelopment to achieve the high percentage of completeness. Finally, increasethe awareness of open data and its importance through the open data platformsin the Saudi public universities

SuDaMa: Sustainable Open Government Data Management Framework for Long-Term Publishing and Consumption

Sustainable open data management systems are key elements to overcome long-term publishing and consumption challenges on open data platforms. However, most existing solutions have not been envisaged as sustainable approaches to cope with key data attributes such as timeliness, accessibility and usability. In this paper, we present a framework that addresses these challenges and ensures the efficient publication of dynamic data on open data platforms, as well as improving the consumption experience of end-users. The framework describes the architecture of a sustainable system that can operate continuously in an automated way. It involves: 1) introducing an evolvable and scalable ecosystem to guarantee the access to dynamic data when they are made available; 2) tackling governance using an autonomous agent to provide the capacity of dynamically publishing/unpublishing data resources on open data platforms. It ensures easier accessibility and greater efficiency for data developers, facilitating the development of data-enabled products, thus helping developers to seize the opportunities of the data economy; and 3) improving the consumption experience by introducing conversational bots, which enhances the usability of open data. We demonstrate the results and validity of the solution in practice through its implementation, evaluation and exploitation as a practical system in the Parliament of the Canary Islands, Spain. The framework developed can be considered a generic solution to manage open data publishing and consumption challenges in other domains.

Comparative Analysis of Metadata Models on e-Government Open Data Platforms

E-government open data platforms use metadata to provide an adequate environment for the consumption of data released on these platforms. Metadata enable the machine processing of information in a structured manner. Given that it reveals details and context about data, metadata allow interpretation in unique way and offer semantics for open government data. This paper presents an overview and analysis of four metadata models used in contemporary e-government open data platforms. We illustrate the strengths and limitations of these models through aspects and principles of open data defined in the paper by proposing metadata quality assessment model. Moreover, we examine how metadata structure is oriented toward the provision of valuable information assets needed for the utilization of open government data. Finally, we give an overview of available API support on the selected open data platforms for the provision and reuse of open data. The research results indicate that more attention should be paid to metadata to uncover relationships between datasets.

PatentsView: An Open Data Platform to Advance Science and Technology Policy

What are the connections between science and improvements in economic and social outcomes that drive the “value” of science? Most of the time, these connections are circuitous, varied, diffuse, and opaque. Patent data offer an opportunity to expose new connections between science and technology as well as exposing links to downstream economic and social outcomes. The PatentsView open data platform performs data preparation, visualization, and adds helpful features to USPTO’s administrative data. PatentsView is fundamentally a free “intermediate good” that provides the needed materials for researchers, policymakers, and students to conduct their own analyses, make their own linkages, and derive their own insights. This paper provides a quick tour of the PatentsView platform.

플랫폼 정부 관점에서 조명한 국내 COVID-19 대응 정보화 사례: ICT와 데이터 활용을 중심으로

본 연구는 COVID-19에 대한 한국의 대응 정보화 사례를 플랫폼 정부(Government as a Platform, GaaP)의 관점에서 분석하고자 한다. 플랫폼 정부는 애플, 구글, 페이스북 등 민간기업의 플랫폼 전략 성공에 따라 공공부문도 플랫폼 전략을 차용해야 한다고 주장한 O"Reilly로부터 비롯되었다. O"Reilly는 정부가 제공하는 플랫폼에서 공공과 민간이 협업하여 새로운 공공서비스를 창출 할 수 있음을 제시하였다. 본 연구는 플랫폼 정부 관련 문헌을 기반으로 네가지 요인을 도출하고 분석틀로 활용하여, 국내 COVID-19 대응사례를 분석하였다. 첫째 지능정보기술 활용 측면에서는 스마트 검역시스템, 코로나 19 역학조사 지원 시스템과 같이 각 기관별 산재되어 있는 정보시스템의 통합이 두드러졌다. 둘째, 데이터 개방 측면에서는 질병관리청 홈페이지, 코로나 19 마이크로 페이지와 같은 웹사이트는 투명성 증진을 위한 목적이 있었다. 공공데이터 포털과 같은 정보공개 플랫폼은 데이터 개방은 서비스 개발을 통한 부가가치 창출이 목적이었다. 셋째, 이해관계자의 참여 측면에서는 광화문 1번가, 공공데이터 포털, 도전 한국 등의 사례를 확인하였다. 사례분석 결과 참여의 질적 수준을 제고하고, 수요자가 원하는 데이터를 제공하며, 참여를 위한 인센티브의 도입이 필요하였다. 넷째, 혁신적 산출물 측면에서는 확진자 동선앱, 마스크앱 등의 서비스 개발과 광화문 1번가의 공공데이터 개방 관련 정책제안이 확인되었다. 현재까지 확인된 산출물의 소수이고, 앱 개발사례는 특정한 주제에 편중된 것으로 나타났는데, 이는 감염병이라는 정책이슈의 특수성과 수요자 맞춤형 데이터 제공의 부족에 기인한 것으로 보인다. In this study, it aims to analyze informatization case against to COVID-19 in Korean Government with perspective of Government as a Platform(GaaP). GaaP stems from O"Reilly who have argued that governments should adopt platform strategy due to success of platform enterprises like Apple, Google, Facebook and so on. O"Reilly suggested that public-private partnership can create innovative public service on the public platform. It analyzes case against to COVID-19 with framework that including four factors from GaaP literature. First, in the aspect of intelligent information technology, the role of integrated information systems that integrate separated information systems from various organizations are remarkable like smart quarantine system and COVID-19 epidemiological investigation system. Second, in the aspect of open data, public data release through website like homepage of Korea Centers for Disease Control and Prevention and COVID-19 micro page has a purpose on increasing transparency. public data release through open data platform like Data portal has a purpose creating added value with developing services. Third, in the aspect of stakeholder engagement, we find the cases including Gwanghwamun 1bunga, Data portal, and Challenge Korea. As a result of the case analysis, there are needs to increase quality of engagement, provide user demanded data, and introdue incentive. Fourth, in the aspect of innovative output, we find the cases app dvelopment including confirmed person route app and public mask app and policy suggestion through Gwanghwamun 1bunga. There are small number of outputs identified and app development cases are biased towards particluar subject. This result stems from distinctiveness of policy issue like infection disease and lack of user demanded open data.

Model-based Generation of Web Application Programming Interfaces to Access Open Data

In order to facilitate the reusing of open data from open data platforms’ catalogs, Web Application Programming Interfaces (APIs) are an important mechanism for reusers. However, there is a lack of suitable Web APIs to access data from open data platforms. Moreover, in most cases, the currently available APIs only allow to access catalog’s metadata or to download entire data resources (i.e. coarse-grain access to data), hampering the reuse of data. Therefore, we propose a model-based approach to automatically generate Web APIs from open data. Our generated Web APIs facilitate the access and reuse of specific data (i.e., providing fine-grain or query-level access to data), which will result in many societal and economic benefits such as transparency and innovation. With this approach we address open data publishers which will be able to include a Web API within their data, but also open data reusers in case of missing APIs. This APIfication process, which means the creation of APIs for every available dataset, is based on automatic, generic and standardised generation mechanisms. The performance and functioning of this approach is validated with different datasets, which successfully generates Web APIs that facilitate the reuse of data.

Expanded dataset of mechanical properties and observed phases of multi-principal element alloys

This data article presents a compilation of mechanical properties of 630 multi-principal element alloys (MPEAs). Built upon recently published MPEA databases, this article includes updated records from previous reviews (with minor error corrections) along with new data from articles that were published since 2019. The extracted properties include reported composition, processing method, microstructure, density, hardness, yield strength, ultimate tensile strength (or maximum compression strength), elongation (or maximum compression strain), and Young’s modulus. Additionally, descriptors (e.g. grain size) not included in previous reviews were also extracted for articles that reported them. The database is hosted and continually updated on an open data platform, Citrination. To promote interpretation, some data are graphically presented.

An Open Data and Citizen Science Approach to Building Resilience to Natural Hazards in a Data-Scarce Remote Mountainous Part of Nepal

The citizen science approach has gained momentum in recent years. It can enable both experts and citizen scientists to co-create new knowledge. Better understanding of local environmental, social, and geographical contexts can help in designing appropriate plans for sustainable development. However, a lack of geospatial data, especially in the context of developing countries, often precludes context-specific development planning. This study therefore tests an innovative approach of volunteer citizen science and an open mapping platform to build resilience to natural hazards in the remote mountainous parts of western Nepal. In this study, citizen scientists and mapping experts jointly mapped two districts of Nepal (Bajhang and Bajura) using the OpenStreetMap (OSM) platform. Remote mapping based on satellite imagery, capacity building, and mobilization of citizen scientists was performed to collect the data. These data were then uploaded to OSM and later retrieved in ArcGIS to produce a usable map that could be exploited as a reference resource for evidence-based decision-making. The collected data are freely accessible to community members as well as government and humanitarian actors, and can be used for development planning and risk reduction. By piloting in two communities of western Nepal, we found that using open data platforms for collecting and analyzing location-based data has a mutual benefit for researchers and communities. Such data could be vital in understanding the local landscape, environmental risk, and distribution of resources. Furthermore, they enable both researchers and local people to transfer technical knowledge, collect location-specific data, and use them for better decision-making.

Open Data Services in the Library

The opening and sharing of data are gaining momentum in the era of big data. Libraries have been actively involved in the research and practice of open data. At present, the related research in Chinese libraries is still in progress, which mainly focuses on the introduction of the abroad practices, or on the construction of open data platforms. This paper introduces and analyzes the Shanghai library open data service and provides a useful reference for the open data service of Libraries in China. For the future development of the Shanghai library open data services, this paper puts forward some measures and suggestions that include metadata work, website construction, legal protection, and developer community training.