1,721,050 research outputs found
The role of combining national official statistics with global monitoring to close the data gaps in the environmental SDGs
Full text linkThe Sustainable Development Goals (SDGs) have elevated the profile of the environmental dimension of development – and how we monitor this dimension. However, they have also challenged national statistical systems and the global statistical community to put in place both the methodologies and mechanisms for data collection and reporting on environmental indicators. According to a recent analysis, there is too little data to formally assess the status of 68% of the environment-related SDGs [1]. Many environment-related indicators were not part of the purview of national statistical systems and did not have a methodology or data collection system in place prior to the adoption of the SDG indicator framework [2]. Moderate improvements have been made, as evidenced by the reduced proportion of environment-related SDG indicators classified as Tier III between the original classification in 2016 and May 2019 – dropping from 50% to 28% [3]. As of March 2020, there are currently no Tier III indicators; however, as many of the SDG indicators have been recently reclassified the data availability and experience in compiling these indicators is severely limited. Socioeconomic indicators have far outpaced environmental indicators in this shift, with only 7% of non-environmental indicators classified as Tier III in May 2019 [1,4,5]. As the custodian agency for 26 of the environment-related SDG indicators, UN Environment is establishing methodologies and mechanisms to collect country-level data. However, many countries currently do not have national systems in place for monitoring these environmental indicators and thus there is a risk that much of the environmental dimension of development cannot be captured by using reporting mechanisms which only include traditionally collected national official statistics. For many of these indicators, UN Environment is exploring new data sources, such as data from citizen science. Citizen science has the potential to contribute to global and local level SDG monitoring. Realizing its full potential however, would require building key partnerships around citizen science data and creating an enabling environment. Global modelling is another approach to fill data gaps. These new types of data could not only improve global estimations but could be incorporated in national official statistics in order to improve nationally relevant data and analysis [6]. The Global Material Flow database, which estimates Domestic Material Consumption (covering SDG indicators 8.4.2 and 12.2.2), and the Global Surface Water Explorer application (covering SDG indicator 6.6.1) are a couple of examples of where UN Environment is complementing national data with global data products in the official SDG reporting process. In these cases the use of globally-derived data has been agreed by the Inter-Agency and Expert Group on SDG Indicators (IAEG-SDGs) [7]. Expanding globally-estimated or -modelled data to cover environment-related SDG indicators could build the foundation for a digital ecosystem for the planet, which would provide a basis for developing integrated analysis and insights. A Sustainability Gap Index could be one mechanism to bring together the environmental dimension of development into a single metric, which could inform the achievement of the SDGs, environmental assessments and national policy. This paper presents a summary of how the world is faring in terms of measuring the environmental dimension of the SDGs
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Citizen science and Earth Observation Data for “Rescuing” the SDGs
No full textThe UN Sustainable Development Goals (SDGs), adopted by the UN General Assembly in 2015, represent a global call to action to tackle the world’s most pressing challenges, from poverty to environmental degradation. Achieving these goals requires a data-driven approach, grounded in accurate, timely, and comprehensive data to guide policy and decision-making. Despite improvements in data availability over the past decade, with less than five years remaining to achieve the SDGs, substantial data gaps remain, limiting the ability to effectively monitor progress and guide policies and actions. Traditional data sources, such as censuses and household surveys, are insufficient to address these data gaps. New data sources, including Earth Observation (EO) data and citizen science, defined as public participation in scientific research and knowledge production, offer innovative and complementary solutions. Scientific literature has demonstrated the potential of these alternative data sources to fill critical gaps. For example, Fraisl et al. (2020) conducted a systematic review of SDG indicators and citizen science initiatives, showing that citizen science data are already contributing or could potentially contribute to monitoring 33% of SDG indicators. Their analysis also revealed a significant overlap with EO data contributions. According to GEO (2017), EO data are relevant to 29 SDG indicators, and Fraisl et al. found that citizen science could support 24 of these, demonstrating the complementarity between the two. Since publishing the aforementioned study in 2020, Fraisl et al. have been working with National Statistical Offices (NSOs) and UN agencies to demonstrate how this potential can be realized. A notable example is their collaboration with the Ghana Statistical Service, the Environmental Protection Agency in Ghana, and UNEP (as the custodian agency), which resulted in existing citizen science data on marine plastic litter being integrated into Ghana’s official statistics, as well as into the monitoring and reporting of SDG indicator 14.1.1b, Plastic Debris Density, under the leadership of the Ghana Statistical Service (GSS). This initiative bridged local data collection efforts with national and global monitoring processes and policy agendas through the SDG framework. The results were included in Ghana’s 2022 Voluntary National Review of the SDGs, reported on the UN SDG Global Database, and are informing national policies in Ghana. This makes Ghana the first country to use citizen science data for monitoring and reporting an SDG indicator. Their findings, published in Fraisl et al. (2023), provide valuable lessons for the EO community, not only from a technical perspective but also in building effective partnerships with NSOs, UN agencies, civil society organizations, academia, and other stakeholders to leverage EO data for SDG monitoring and reporting and sustainable development. This example highlights one of the ways citizen science is being utilized to support the SDGs. The oral presentation will showcase additional examples that emphasize the synergy between citizen science and EO in bridging SDG data gaps, informing or reshaping policies, and mobilizing action. It will also feature initiatives such as the Citizen Science Global Partnership (CSGP), hosted by the International Institute for Applied Systems Analysis (IIASA), which seeks to advance citizen science for a sustainable world and foster collaboration with the EO community to leverage the combined potential of citizen science and EO data for achieving sustainability. References: Fraisl D, Campbell J, See L et al (2020) Mapping citizen science contributions to the UN sustainable development goals. Sustain Sci 15:1735–1751. https://doi.org/10.1007/s11625-020-00833-7 GEO (2017) Earth Observations 2030 Agenda for Sustainable Development, V1.1. Japan Aerospace Exploration Agency (JAXA) on behalf of GEO under the EO4SDG Initiative. Available at: https://www.earthobservations.org/documents/publications/201703_geo_eo_for_2030_agenda.pdf Fraisl, D., See, L., Bowers, R. et al. The contributions of citizen science to SDG monitoring and reporting on marine plastics. Sustain Sci 18, 2629–2647 (2023). https://doi.org/10.1007/s11625-023-01402-
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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