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72 results for “Developing Countries”
Beyond the Digital Divide: Sharing Research Data across Developing and Developed Countries
<p>The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government. </p> <p>Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized. </p> <p>Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers. </p> <p>Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.</p> <p>Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis. </p>
Open Access in developing countries – attitudes and experiences of researchers Dataset
<p>A survey was conducted of 507 researchers from the developing world and connected to INASP’s AuthorAID project to ascertain experiences and attitudes to Open Access publishing. This file is the raw output from the survey, with names and email addresses removed to preserve anonymity. </p>
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
<p><span>As part of the EYE-CLIMA project, GAINS emission data for CH<sub>4</sub>, N<sub>2</sub>O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF<sub>6</sub></span>) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH<sub>4</sub> emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (<a href="http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf">http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf</a>), and they are publicly available at the Zenodo repository under <a href="https://doi.org/10.5281/zenodo.11032177">https://doi.org/10.5281/zenodo.11032177</a>. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.</p> <p>The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.</p> <p>Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity).</p> <p>The updated dataset covers files as follows (internally, all files now carry version number V05):</p> <p>ALL_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.csv</p> <p>BC_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>BC_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>CH4_FLUX_AWB_EUR_MOD_MONTH_20000101_20221231_GAINS_IIASA_V05.nc</p> <p>HFC_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>N2O_FLUX_ALL_EUR_MOD_MONTH_19900101_20231231_GAINS_IIASA_V05.nc</p> <p>SF6_FLUX_ALL_EUR_MOD_YEAR_20050101_20231231_GAINS_IIASA_V05.nc</p> <p>This is version 2.0 of the dataset. It extends from version 1.0 by covering into the year 2023, but also benefits from a number of additional GAINS improvements. Emissions of emitted compounds are provided as kg/m²/s. File names follow the notation developed for the H-Europe project EYE-CLIMA, i.e. species _ variable-type _ sector _ region _ method (MOD=model) _ timestep _ fromTime _ toTime _ model _ institute _ version . filetype.</p> <p>This version is available at <a href="https://doi.org/10.5281/zenodo.15536170">https://doi.org/10.5281/zenodo.15536170</a>. The generic address of the dataset is <a href="https://doi.org/10.5281/zenodo.10886780">https://doi.org/10.5281/zenodo.10886780</a>, resolving to the latest update available at Zenodo. No further updates are planned in EYE-CLIMA, so this version is expected to also reflect the final update within the project.</p> <p>Compared to version 1.0, GAINS benefitted from a number of new developments such as the following:</p> <p>*) Previously, GAINS has been available in five-year timesteps only (with the aim of allowing for scenarios at that resolution). For data version 1.0, a makeshift solution was found to convert into annual data. A recent update now allows, for historic data, to store and retrieve information on an annual basis (from 1990).</p> <p>*) The energy data were obtained from IEA’s world energy balances 2024 (July version, https://www.iea.org/data-and-statistics/data-product/world-energy-balances#documentation), extending into 2022 and extrapolated towards 2025, downscaled from IEA to GAINS sectors and sub-sectors. Additionally, the annual activity of industrial production is estimated using a linear approach, based on five-year timestep data.</p> <p>*) Agricultural statistics were retrieved from Eurostat (and from FAO globally) and extended to 2022, extrapolated towards 2025.</p> <p>*) Interpretation of GAINS data was reconfirmed and updated in consultations with national experts of multiple EU countries. While the process resulted in revised emission projections to be used in the Clean Air Outlook 4 (see <a title="Protected by Check Point: https://environment.ec.europa.eu/topics/air/clean-air-outlook_en" href="https://protect.checkpoint.com/v2/r02/___https:/environment.ec.europa.eu/topics/air/clean-air-outlook_en___.YzJlOmlpYXNhOmM6bzoyYzdiNDRhNDI4Njc3ZjI5MGFjMTU1N2I2OWVmNzM2ZTo3OjE5OTM6ZTFiY2IzMDMxZGViNGE0MjI0ODRmNWQ4NzA3ZDY3Njc4M2U2NzUxNmEwNzQ0ODViNDBhODc1NmNhZmMzY2FlMjpoOkY6Tg"><span lang="EN-GB">https://environment.ec.europa.eu/topics/air/clean-air-outlook_en</span></a><span lang="EN-GB">). While the details of improvements on the individual aspects cannot be disclosed, they are useful to describe historic data most adequately, and have been integrated also in this assessment. That not only leads to changes in absolute emissions for a given year, but also affects trends that now are more plausible and confirmed through the exchange with the national experts.</span></p> <p><span lang="EN-GB">*) Technical adjustments have improved the precision of temporal allocation of emissions and the conversion of grid sizes to actual area.</span></p>
Data from: Public human microbiome data are dominated by highly developed countries
<p>Supplementary tables and datasets associated with the <em>PLOS Biology </em>publication "Public human microbiome data are dominated by highly developed countries." See README.txt for a description of the files and the data fields they contain.</p> <p>Update, 8 Apr 2022: The "figures.md" file described in the readme was inadvertently left out of the initial upload. It has been added here.</p>
Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 3: Pathological details
<p>The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; a scene for surgeons or a chance for medicines</p> <p>Supplementary Table 3: Pathological details</p>
The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; Supplementary Table 2: Metastatic sites
<p>The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; a scene for surgeons or a chance for medicines.</p> <p>Supplementary Table 2: Metastatic sites</p>
Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 1: Patients' characteristics and endocrinal manifestations
<p>Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 1: Patients’ characteristics and endocrinal manifestations</p>
Fig. 3 in Development of in-country live food production for amphibian conservation: The Mountain Chicken Frog (Leptodactylus fallax) on Dominica, West Indies
Fig. 3. (A) Two rows of cricket breeding containers and cockroach breeding bins below. (B) Inside of a cricket breeding container, including refugia, food items, and several egg laying containers, transplanted into an empty container to allow eggs to hatch. (C) Inside view of a cockroach breeding bin, including substrate, refugia, and several food items. Photos: D. Nicholson.
Fig. 2 in Development of in-country live food production for amphibian conservation: The Mountain Chicken Frog (Leptodactylus fallax) on Dominica, West Indies
Fig. 2. Cultured species at the CBP in Dominica. (A) Gryllodes sigillatus. (B) Gryllus assimilis. (C) Caribacusta dominica. (D) Blaberus discoidalis. (E) Zophobas atratus. (F) Veronicella sloanii. (G) Pleurodonte dentiens. (H) Leptogoniulus sp. Photos: D. Nicholson.
A cost effectiveness analysis on interventions for childhood anemia in developing countries: A health technology assessment
<p>This is a data sheet of the "A cost-effectiveness analysis on interventions for childhood anemia in developing countries: A health technology assessment" used in the study. </p>
Data to test IDP for agriculture in developing countries
<p>The data has variables capturing net outward foreign direct investment per capita, gross domestic product per capita and trade openness for agriculture in developing countries. The other variables are the official exchange rate, gross secondary school enrolment in per cent and inflation measured as per cent of CPI growth. These are for the total economy. </p> <p>Net outward foreign direct investment per capita (NOFDIPC) was constructed as outward foreign direct investment less inward foreign direct investment for agriculture, forestry and fishing. The sum is divided by the population of both sexes. The foreign direct investment and population data were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/FDI; https://www.fao.org/faostat/en/#data/OA). The gross domestic product per capita (GDPPC) was computed as agricultural value added divided by the population. Agricultural value added was also obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/MK). Trade openness (AGTO) was computed as agricultural exports plus imports divided by agricultural value added. The exports and imports were obtained from FAOSTAT (https://www.fao.org/faostat/en/#data/TCL). Others; official exchange rate (EXRATE), gross secondary school enrolment in per cent (HC) and inflation measured as per cent of CPI growth (INFLA) were drawn from the world development indicators database of the World Bank (https://databank.worldbank.org/source/world-development-indicators#).</p>
Data from: Emerging risks of non-native species escapes from aquaculture: call for policy improvements in China and other developing countries
<p><span>1. Global aquaculture relies heavily on the farming of non-native aquatic species (hereafter, NAS). NAS escapes from aquaculture facilities can result in serious aquatic bio-invasions, which</span> has been <span>an important issue in the FAO <i>Blue Growth Initiative</i>. A r</span>egulatory quagmire regarding NAS farming and escapes, however, exists in most developing countries.</p> <p><span>2. We discuss aquaculture expansion and NAS escapes, illustrate emerging risks, and propose recommendations for improved aquaculture management</span> across developing countries and particularly for<span> China. </span></p> <p>3. <span>In </span>China<span>,</span> 68 NAS are known to have successfully established feral populations in natural habitats due to recurrent leakages or escapes; among the 68 NAS, 52<span> represent risks to native aquatic ecosystems. In addition to affecting a country's own biodiversity and ecosystem functions, NAS escapees can also threaten the </span>biosecurity<span> of shared waters in neighboring countries.</span></p> <p>4. <i>Policy implications</i>. <span>Non-native aquatic species (NAS) </span>escapes have already had adverse ecological effects in China and other developing countries. The importance of this problem, however, is not adequately recognized by current conservation policies in developing countries. To conserve biodiversity and to support the<span> goal of FAO's</span> sustainable aquaculture, developing countries <span>should now take responsible actions</span> to address NAS escapes <span>through policy and management improvements. Specifically, these</span> countries should pass comprehensive legislation, establish effective agencies and national standards and planning, and enhance integrated research and education to deal with risk assessment, prevention, monitoring, and control of <span>NAS</span> escapes. Given that China is the world's largest aquacultural producer, China can create a model for other developing countries that will increase the biosecurity and sustainability of global aquaculture.</p>
Open Data Intermediaries in Developing Countries Dataset
<p>These three datasets support the findings of the paper "Open Data Intermediaries in Developing Countries" published in the <em>Journal of Community Informatics</em>. The paper explores the concept of open data intermediaries using the theoretical framework of Bourdieu’s social model, particularly his species of capital. Secondary data on intermediaries from Emerging Impacts of Open Data in Developing Countries research was analysed according to a working definition of an open data intermediary presented in this paper, and with a focus on how intermediaries are able to link agents in an open data supply chain, including to grassroots communities. The study found that open data supply chains may comprise multiple intermediaries and that multiple forms of capital may be required to connect the supply and use of open data. The effectiveness of intermediaries can be attributed to their proximity to data suppliers or users, and proximity can be expressed as a function of the type of capital that an intermediary possesses. However, because no single intermediary necessarily has all the capital available to link effectively to all sources of power in a field, multiple intermediaries with complementary configurations of capital are more likely to connect between power nexuses. This study concludes that consideration needs to be given to the presence of multiple intermediaries in an open data ecosystem, each of whom may possess different forms of capital to enable the use of open data.</p> <p><strong>Data:</strong></p> <ol> <li>Data for 27 Asian cases extracted from the 17 Emerging Impacts of Open Data in Developing Countries case study reports.</li> <li>Data for 4 African cases extracted from the 17 Emerging Impacts of Open Data in Developing Countries case study reports.</li> <li>Tabulated data of findings for types of capital, organisational type and primary source of revenue for each of the 32 open data intermediaries included in the study. </li> </ol>
Data from: Building functional and sustainable pharmacovigilance systems - an analysis of pharmacovigilance development across high-, middle- and low-income countries
<p>Background</p> <p>Pharmacovigilance (PV) is an essential component of health systems. Functional PV systems protect and promote public health by supporting the safe and effective use of medicinal products through the prevention and mitigation of harm. With increased simultaneous introduction of innovative products in high-, middle- and low-income countries, e.g., COVID-19 vaccines, or solely in low- and middle-income countries (LMIC), e.g., malaria vaccines, PV systems in LMIC must be able to detect safety signals and ensure adequate safety surveillance. This research aims to analyse the development of PV systems across high-, middle- and low-income countries and to carve out essential elements for implementing functional and sustainable PV systems in LMIC.</p> <p>Methods</p> <p>A convergent parallel mixed-methods design, consisting of qualitative and quantitative methods was used. Qualitative research consisted of semi-structured interviews. To expand the breadth and range of the study, a quantitative survey was conducted, focusing on the same thematic questions as the semi-structured interviews.</p> <p>Results</p> <p>Twelve key informants from nine countries were interviewed and 52 respondents from 36 countries completed an online questionnaire. Four major themes consisting of 12 categories emerged from the data. Based on these, the following elements essential for building functional and sustainable pharmacovigilance systems in LMIC were identified: understanding the drivers of PV development; adequately resolving core system challenges; implementing an efficient organisational structure and procedures for PV; investing in activities beyond reporting of adverse drug reactions; identifying alternate sources of financing; having a national strategy with a vision and mission for PV; adequately leveraging the health system; and effectively integrating the pharmaceutical sector in the national PV system.</p> <p>Conclusions</p> <p>Findings from this research revealed progress in PV systems in LMIC in the last decade, though significant efforts are still needed to develop these systems to meet global standards. Developing the different areas emerging from this research, which necessitates implementing functional PV structures and processes, adequately leveraging the health system and effectively engaging the pharmaceutical sector, through the mechanisms proposed, would enable a comprehensive progression from basic to stable, functional and thus sustainable PV systems in LMIC.</p>
Dataset: Digital Transformation in Developing Countries (A Case Study)
<p>This data represents the availability of data to support the publication of researchers' papers on digital transformation in developing countries (case study: Indonesia).</p>
Research on Universities Profile upon Entrepreneurship and Innovation Orientation: Case of Developing Countries
<p>This Data set is a part of a research project of "Research on Universities Profile upon Entrepreneurship and Innovation Orientation: Case of Developing Countries" </p>
Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset
<p>Dataset of the project Modeling Decarbonization Pathways in the Power Sector in Developing Countries: The case of Colombia (EMP-LAC 2023) - Dataset</p>
Lactose-free Milk in Infants With Acute Diarrhea in a Developing Country
ClinicalTrials.gov study NCT02246010. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Trial of the Use of Antenatal Corticosteroids in Developing Countries
ClinicalTrials.gov study NCT01084096. IPD Sharing: Not stated. Countries: 6. Publications: 9.
Building functional and sustainable pharmacovigilance systems - an analysis of pharmacovigilance development across high-, middle- and low-income countries
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.