Open Data Policy Research Articles

Users of open Big Earth data – An analysis of the current state

The broad dissemination of open data policies for Big Earth data leads to a diversification of users. The literacy in data and data handling differs for each user group, resulting in different needs and requirements. In addition, the development of cloud-based data systems challenges traditional workflows of all users. In order to tailor new cloud-based data systems for Earth data users, it is of utmost importance to obtain a better understanding of users in terms of the type of data they explore, the applications they need the data for, the way they access and process data, and the challenges they face. This is an indispensable prerequisite to ensure an increased uptake of Big Earth data in the future.In order to get a better insight in the requirements and challenges of users coping with Big Earth data, we run a comprehensive web-based user survey. Our results, inclined to users of Big Earth data in Europe and the North American continent, reveal that a majority of survey respondents still download copies of data onto their local machine and handle and process data locally with a combination of programming and desktop-based software. However, survey respondents are facing severe problems related to the growing data volumes, the data heterogeneity and the limited processing capacities for their demanding applications. Thus, they show a specific interest in using cloud-based data services in the near future but express the need for an easier data discovery and the interoperability of data systems. Based on the survey findings, we draw a set of recommendations to make Big Earth data more FAIR (findable, accessible, interoperable and re-useable).

On researcher bias in Software Engineering experiments

Researcher bias occurs when researchers influence the results of an empirical study based on their expectations, either consciously or unconsciously. Researcher bias might be due to the use of Questionable Research Practices (QRPs). In research fields like medicine, blinding techniques have been applied to counteract researcher bias. In this paper, we present two studies to increase our body of knowledge on researcher bias in Software Engineering (SE) experiments, including: (i) QRPs potentially leading to researcher bias; (ii) causes behind researcher bias; and (iii) possible actions to counteract researcher bias with a focus on, but not limited to, blinding techniques. The former is an interview study, intended as an exploratory study, with nine experts of the empirical SE community. The latter is a quantitative survey with 51 respondents, who were experts of the above-mentioned community. The findings from the exploratory study represented the starting point to design the survey. In particular, we defined the questionnaire of this survey to support the findings from the exploratory study. From the interview study, it emerged that some QRPs (e.g., post-hoc outlier criteria) are acceptable in certain cases. Also, it appears that researcher bias is perceived in SE and, to counteract researcher bias, a number of solutions have been highlighted. For example, duplicating the data analysis in SE experiments or fostering open data policies in SE conferences/journals. The findings from the interview study are mostly confirmed by those from the survey, and allowed us to delineate recommendations to counteract researcher bias in SE experiments. Some recommendations are intended for SE researchers, while others are purposeful for the boards of SE research venues.

Application-Based COVID-19 Micro-Mobility Solution for Safe and Smart Navigation in Pandemics

Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity.

Open government data portals: Predictors of site engagement among early users of Health Data NY

A key premise of open government data (OGD) policies is enhanced engagement between government and the public. However, it is not well understood who the users of OGD are, how to tailor OGD content, and which communities to target for outreach. We examined users' engagement with Health Data NY, New York's health oriented OGD portal, to understand user characteristics associated with increased site engagement. We used Google Analytics data to classify four site engagement metrics into high versus low engagement and used logistic regression to test associations between higher site engagement and gender, age group, device type, and consumer interest. We found that being in a younger age bracket, male, a desktop user, and a Technophile are associated with higher engagement. The findings contribute to further understanding OGD initiatives and consumer health information behavior. More broadly, we demonstrate how OGD managers can leverage their web analytics data to understand which users are most engaged, thereby enabling them to better target their content.

A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses.

Computational reproducibility is a corner stone for sound and credible research. Especially in complex statistical analyses-such as the analysis of longitudinal data-reproducing results is far from simple, especially if no source code is available. In this work we aimed to reproduce analyses of longitudinal data of 11 articles published in PLOS ONE. Inclusion criteria were the availability of data and author consent. We investigated the types of methods and software used and whether we were able to reproduce the data analysis using open source software. Most articles provided overview tables and simple visualisations. Generalised Estimating Equations (GEEs) were the most popular statistical models among the selected articles. Only one article used open source software and only one published part of the analysis code. Replication was difficult in most cases and required reverse engineering of results or contacting the authors. For three articles we were not able to reproduce the results, for another two only parts of them. For all but two articles we had to contact the authors to be able to reproduce the results. Our main learning is that reproducing papers is difficult if no code is supplied and leads to a high burden for those conducting the reproductions. Open data policies in journals are good, but to truly boost reproducibility we suggest adding open code policies.

Open government data licensing in the Gulf Cooperation Council

The Cooperation Council of the Arab States of the Gulf (‘GCC’) is a political and economic bloc made up of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia (‘Saudi’) and the United Arab Emirates (UAE). As these countries start publishing more of their government-produced copyright works through open data portals, it has become important to analyse the legal terms under which these works are licensed and the extent to which members of the public are allowed to use and utilize them. This article examines the different open licensing approaches of open government data in Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the UAE by analysing each of their licensing provisions and identifying the key differences between them. Open government data are a trending topic among the GCC Member States with Saudi issuing a brand new open data policy in September 2020,1 Oman issuing a revised open data policy in June 2020,2 and the UAE issuing a Smart Data Framework in February 2019 that included open data licensing provisions.3 This section provides an introduction to open government data, an overview of copyright restrictions on government works within the GCC, and an outline of the different approaches to licensing that are available to overcome these restrictions.

EVALUATING THE IMPACT OF LASRC AND SEN2COR ATMOSPHERIC CORRECTION ALGORITHMS ON LANDSAT-8/OLI AND SENTINEL-2/MSI DATA OVER AERONET STATIONS IN BRAZILIAN TERRITORY

Abstract. Accurate and consistent Surface Reflectance estimation from optical remote sensor observations is directly dependant on the used atmospheric correction processor and the differences caused by it may have implications on further processes, e.g. classification. Brazil is a continental scale country with different biomes. Recently, new initiatives, as the Brazil Data Cube Project, are emerging and using free and open data policy data, more specifically medium spatial resolution sensor images, to create image data cubes and classify the Brazilian territory crops. For this reason, the purpose of this study is to verify, on Landsat-8 and Sentinel-2 images for the Brazilian territory, the suitability of the atmospheric correction processors maintained by their image providers, LaSRC from USGS and Sen2cor from ESA, respectively. To achieve this, we tested the surface reflectance products from Landsat-8 processed through LaSRC and Sentinel-2 processed through LaSRC and Sen2cor comparing to a reference dataset computed by ARCSI and AERONET. The obtained results point that Landsat-8/OLI images atmospherically corrected using the LaSRC corrector are consistent to the surface reflectance reference and other atmospheric correction processors studies, while for Sentinel-2/MSI images, Sen2cor performed best. Although corrections over Sentinel-2/MSI data weren’t as consistent as in Landsat-8/OLI corrections, in comparison to the surface reflectance references, most of the spectral bands achieved acceptable APU results.

Open Data Policies among Library and Information Science Journals

Journal publishers play an important role in the open research data ecosystem. Through open data policies that include public data archiving mandates and data availability statements, journal publishers help promote transparency in research and wider access to a growing scholarly record. The library and information science (LIS) discipline has a unique relationship with both open data initiatives and academic publishing and may be well-positioned to adopt rigorous open data policies. This study examines the information provided on public-facing websites of LIS journals in order to describe the extent, and nature, of open data guidance provided to prospective authors. Open access journals in the discipline have disproportionately adopted detailed, strict open data policies. Commercial publishers, which account for the largest share of publishing in the discipline, have largely adopted weaker policies. Rigorous policies, adopted by a minority of journals, describe the rationale, application, and expectations for open research data, while most journals that provide guidance on the matter use hesitant and vague language. Recommendations are provided for strengthening journal open data policies.

The Colorado East River Community Observatory Data Collection

AbstractThe U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow‐dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long‐term and spatially‐extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross‐disciplinary process‐based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value‐added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.

Open data innovation: Visualizations and process redesign as a way to bridge the transparency-accountability gap

Open government data has led to public policy innovation in pursuit of various expected benefits. One of the intended goals of open data innovation to improve transparency and accountability. However, our current understanding of open data innovation and its ability to generate transparency and accountability is limited, particularly with regard to empirical evidence. In this paper, we describe how a state agency redesigned its organizational processes around visualization tools and how such efforts helped bridge the transparency-accountability gap by enhancing the understandability and usability of open government data. We conclude that open data innovation does not stop with the adoption of an open data policy, but rather involves an ongoing cycle of improvements through which the organization responds to its various stakeholders' use of open data, thereby increasing usefulness of those datasets and, subsequently, improving overall accountability.

An integrated assessment of the impacts of PM2.5 and black carbon particles on the air quality of a large Brazilian city

Data on airborne fine particle (PM2.5) emissions and concentrations in cities are valuable for traffic and air quality managers, urban planners, health practitioners, researchers, and ultimately for legislators and decision makers. Emissions and ambient concentrations of PM2.5 and black carbon (BC) were assessed in the city of Curitiba, southern Brazil. The methodology combined a month-long monitoring campaign with both fixed and mobile instruments, development of emission inventories, and dispersion model simulations on different scales. The mean urban background PM2.5 concentrations during the campaign were 7.3 μg m−3 in Curitiba city center, but three- to fourfold higher (25.3 μg m-3) in a residential area on the city’s outskirts, indicating the presence of local sources, possibly linked to biomass combustion. BC concentrations seemed to be more uniformly distributed over the city, with mean urban background concentrations around 2 μg m−3, half of which due to local traffic emissions. Higher mean BC concentrations (3–5 μg m-3) were found along busy roads. The dispersion modeling also showed high PM2.5 and BC concentrations along the heavily transited ring road. However, the lack of in situ data over these peripheral areas prevented the verification of the model output. The vehicular emission factors for PM2.5 and BC from the literature were found not to be suitable for Curitiba’s fleet and needed to be adjusted. The integrated approach of this study can be implemented in other cities, as long as an open data policy and a close cooperation among regional, municipal authorities and academia can be achieved.

Fengyun Meteorological Satellite Products for Earth System Science Applications

Following the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

The National Ecological Observatory Network’s soil metagenomes: assembly and basic analysis

The National Ecological Observatory Network (NEON) annually performs shotgun metagenomic sequencing to sample genes within soils at 47 sites across the United States. NEON serves as a valuable educational resource, thanks to its open data policies and programming tutorials, but there is currently no introductory tutorial for performing analyses with the soil shotgun metagenomic dataset. Here, we describe a workflow for processing raw soil metagenome sequencing reads using the Sunbeam bioinformatics pipeline. The workflow includes cleaning and processing raw reads, taxonomic classification, assembly into contigs, annotation of predicted genes using custom protein databases, and exporting assemblies to the KBase platform for downstream analysis. This workflow is designed to be robust to annual data releases from NEON, and the underlying Snakemake framework can manage complex software dependencies. The workflow presented here aims to increase the accessibility of NEON's shotgun metagenome data, which can provide important clues about soil microbial communities and their ecological roles.

Política de Dados Abertos na Agricultura Familiar: tecnologia inovadora para subsidiar políticas públicas na área agrária

<span class="fontstyle0">The Open Data policy is a struggle in the institutionality of national transparency for the construction of the Democratic Rule of Law. Research on the inclusion of open data in the field, in particular, aimed at discussion around family farming, is important to promote access to information necessary for the monitoring and evaluation of public policies. This article is limited as a way of contributing to the understanding of this dynamic of action in family farming on the policies that affect it. Through bibliographic and documentary research and content portals. We found that several data on family farming do not exist, either because they are not produced or even because they have not been cataloged, generating a need for access to coherent and relevant information, especially in the spheres of state and municipal governments. Only with due open access to this information will we be able to analyze it, refute data and produce knowledge about the repercussions of various public policies aimed at family farming</span> <br /><br />

COVID-19 research and publishing: Advance ambitiously, but cautiously, in 2021

Objectives: This paper retrospectively reflects on opportunities and risks encountered in 2020 related to publishing COVID-19-related research, and offers perspectives on how researchers should proceed cautiously in 2021. Methods: Based on select literature, the author’s perspective, and case studies sourced primarily from PubMed, challenges in publishing COVID-19-related research observed in 2020 and early 2021 are described. Background problems and suggestions for possible solutions are provided. Findings: 2020 was highly transformative, not only for biomedical research as a direct result of a focus on COVID-19, but because many aspects of publishing were challenged. Some concerns that had already been emphasized recently, such as reproducibility, misinformation, or predatory publishing, were amplified in 2020, and carried over into 2021. Open data policies were more encouraged, but in select cases, they were weakly implemented. Applications: Both young and established researchers and academics alike, in their passionate desire to contribute to humanity’s advance in understanding COVID-19, need to appreciate several risks and perils that lie within academic publishing. Keywords: COVID-19; correction of literature; ethics; pandemic; public health; retractions

Transparencia y Gobernanza en la Gestión de la Crisis de COVID-19

The state of emergency at COVID-19 tests the capabilities of the world's governments, in contexts of increasing political conflict, where social needs and aspirations for justice and equity grow exponentially, all which impact on the structuring of a post-coronavirus order with uncertain characteristics. In this sense, through a documentary methodology and the analysis of official government management data, the objective of the article is to describe the meaning of transparency and governance policies implemented within the framework of the health crisis of the pandemic of the new coronavirus, from the perspective of the open government with special emphasis on the cases of Chile and Colombia. The main findings of the research emphasize that open data policy is a necessary condition for qualitatively increasing the levels of governance and transparency of public management in crisis management, all of which makes it possible to conclude that the open government model makes public information available to the citizen that positions him as a watchdog for government activities but, at the same time, the data, being timely and comprehensive, promote the creation of initiatives for collective benefit.

Wind Energy flips to Open Access

The Wind Energy journal has seen significant changes in the last couple of years. We now have Transparent Peer Review which we believe is a fairer and more open way of reviewing the manuscripts which we receive, and furthermore, the journal moved to Open Access as of the beginning of 2021. To further enhance the impact of our authors and to make their work more accessible, we also have an Open Data policy. These are exciting times for the journal with several new editors joining the team in the last couple of years. We now have ten editors covering the very large range of disciplines relevant to the field of wind energy. Every one of them is keen to help authors bring their excellent research to the wider wind energy community. We have also been refreshing our Editorial Board to ensure that we have a very broad range of experts who can help advise and guide the journal in the coming years. With global installed capacity of around 700 GW and growing, wind energy is set to make a large contribution to decarbonising our energy systems. That figure will only continue to grow, and Europe in particular has seen a significant growth in offshore wind energy, which is starting to be repeated around the world, particularly in Asia. These developments present new scientific challenges as turbines become larger and greater demands are placed on them by network operators. The challenges are diverse including having a better understanding of the wind conditions, understanding how wind farms affect their environment, developing new materials, devising smart methods for wind farm control, innovating in turbine and foundation design, and finding ways to integrate ever larger amounts of wind power into networks. Disseminating wind energy research is important not only to the scientific community but also the industrial community if it is to have true societal impact. Open Access provides an ideal vehicle to do this and reach as wide an audience as possible. The Wind Energy journal is proud to be part of the Open Access community to facilitate wind energy's contribution to the energy transition. Wind Energy is the oldest journal dedicated to wind energy research and has established a reputation for publishing high quality research in its field. With the move to Open Access, now is an excellent time to get exposure for your research through this journal so that it has the best possible impact on the broader wind energy community. The Wind Energy journal is keen to engage with the scientific community. With this in mind, we are excited to announce that we have teamed up with the North American Wind Energy Association/WindTech 2021 Conference and will be publishing selected articles from the conference in a special volume. We hope to have more special topic volumes in the future and would welcome your feedback on suitable topics. On behalf of the Editorial Board of Wind Energy, I look forward to seeing your latest research appearing in the journal. We will do all we can to facilitate that, making sure it has the impact it deserves. Simon Watson Editor-in-Chief, Wind Energy

Evaluation of Crop Type Classification with Different High Resolution Satellite Data Sources

Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned.