Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
123
datasets available to search
ShareScore release 0.9.0
Dataset results
123 results for “Sierra Leone”
National Checklists 2017: Sierra Leone Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Sierra Leone collected using effechecka and geonames polygons
National Checklists 2019: Sierra Leone Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Sierra Leone collected using effechecka and geonames polygons
Figs 11-17 in Three new species of Platyprosopus from Thailand, Cambodia, and Sierra Leone, and additional records (Coleoptera, Staphylinidae, Staphylininae)
Figs 11-17: Platyprosopus maximus: (11) forebody; (12) median dorsal portion of head; (13) median portion of pronotum; (14) male tergites IX-X; (15) male sternite IX; (16-17) aedeagus in lateral and in ventral view. Scale bars: 11, 14-17: 1.0 mm; 12-13: 0.2 mm.
Figs 17-21 in Contribution to the AIrotropical Ɨchneumoninae (Hymenoptera, Ɨchneumonidae) Irom Gabon and Sierra Leone, with descriptions oI Iive new species and two new subspecies
Figs 17-21: Face of (17) Adelotropis gabonense nov.sp. ♀ and (18) Adelotropis spinosus nov.sp. ♀. Meta- soma of (19) Adelotropis gabonense nov.sp. ♀ and (20) Adelotropis spinosus nov.sp. ♀. Fig. 21: Scutellum of Adelotropis spinosus nov.sp. ♀. Fig 22: Ctenocalus maculipennis rufopetiolatus ssp.nov. ♀: Habitus from dorsal.
Figs 13-16 in Contribution to the AIrotropical Ɨchneumoninae (Hymenoptera, Ɨchneumonidae) Irom Gabon and Sierra Leone, with descriptions oI Iive new species and two new subspecies
Figs 13-16: Propodeum of: (13) Compsophorus fuscopetiolaris nov.sp. ♀; (14) Compsophorus paracorrugata nov.sp. ♀; (15) Adelotropis gabonense nov.sp. ♀; (16) Magwengiella congica HEINRICH ♂.
Figs 7-12 in Contribution to the AIrotropical Ɨchneumoninae (Hymenoptera, Ɨchneumonidae) Irom Gabon and Sierra Leone, with descriptions oI Iive new species and two new subspecies
Figs 7-12: Face of: (7) Compsophorus corrugata gabonense nov.ssp. ♀; (8) Compsophorus coxator nov.sp. ♀; (9) Compsophorus fuscopetiolaris nov.sp. ♀; (10) Compsophorus paracorrugata nov.sp. ♀; (11) Compsophorus fuscopetiolaris nov.sp. ♂; (12) Magwengiella congica HEINRICH ♂.
Infrastructure Climate Resilience Assessment Data Starter Kit for Sierra Leone
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. </p> <p> Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=12278">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> <p> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. </p> <p> For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). </p> <p><strong>References</strong></p> <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2025) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>
Transport Starter Data Kit: Historical socio-transport data for Sierra Leone
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.</p>
Socio- and Techno-Economic Dataset for Energy Modelling in Sierra Leone
<p>This repositary contains a Reference Energy Syatem (RES) and dataset containing the raw data used in the Sierra Leone energy models created by CCG and the Ministry of Energy in Sierra Leone including scenario-specific constraints used in the modelling. The models used were MAED and OSeMOSYS. Full information regarding data sources and assumptions used can be found in the corresponding Data in Brief.</p> <p>This work was supported by the Climate Compatible Growth Programme (#CCG) of the UK's Foreign Development and Commonwealth Office (FCDO). The views expressed in this paper do not necessarily reflect the UK government's official policies.</p>
National Checklists: Sierra Leone Species List
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
CCG Starter Data Kit: Sierra Leone
<p>A starter data kit for Sierra Leone</p>
Sierra Leone
[Sierra Leone](https://en.wikipedia.org/wiki/Sierra_Leone) is a country on the southwest coast of West Africa. It is bordered by Liberia to the southeast and Guinea surrounds the northern half of the nation. Sierra Leone has a tropical climate with a diverse environment ranging from savanna to rainforests, a total area of 71,740 km2 (27,699 sq mi) and a population of 7,092,113 as of the 2015 census.The capital and largest city is Freetown. The country is divided into five administrative regions which are subdivided into sixteen districts. Sierra Leone is a constitutional republic with a unicameral parliament and a directly elected president serving a five-year term with a maximum of two terms. Muslims make up about three-quarters of the population, though with an influential Christian minority. Religious tolerance in the West African nation is very high and is generally considered a norm and part of Sierra Leone's cultural identity. Source: Objaverse 1.0 / Sketchfab
2014 - 2017, Sierra Leone Ebola epidemic data and knowledge
Information about the 2014 - 2017, Sierra Leone Ebola epidemic curated from multiple publications and reports. The information is represented in machine-interpretable Apollo-XSD format. The terminology is defined by the Apollo-SV ontology and standard identifiers.
Assessment of Self-Medication Practices and Safety Profile of Medicines Utilisation among Pregnant Women attending Antenatal Clinics in Freetown, Sierra Leone: A Multicentre Cross-Sectional Descriptive Study
<p><strong><span>Background: </span></strong><span>Despite the potential fetal and maternal risks of self-medication, studies on self-medication practice and the safety profile of medicines used during pregnancy are scarce in our setting. This study determined the self-medication practice and safety profile of medicines used among pregnant women.</span></p> <p><strong><span>Methods: </span></strong><span>This cross-sectional study was conducted in face-to-face interviews among 345 pregnant women at three hospitals in Sierra Leone. Data were analyzed using descriptive statistics and binary logistic regression to <span>determine the prevalence and associated factors of self-medication. </span></span></p> <p><strong><span>Results: </span></strong><span>A total of 345 pregnant women participated in the study. The prevalence of self-medication among pregnant women with conventional and/or herbal medicine was </span><span>132 (38.3%)</span><span>. Also, 93 (75%) of the conventional medicines (CMs) were categorized as probably safe, of which paracetamol 36 (29.0%) was commonly used, followed by amoxicillin 23 (18.5%) and antimalarials 22 (17.7%) for common illnesses such as </span><span>headache 30 (25.4%), </span><span>urinary tract infection </span><span>23 (19.4%)<span> and malaria 22 (18.6%). The common reason for self-medication was previous experience with the disease 24 (27.3%). </span><em>Luffa acutangula</em> 19 (30.2%) <span>was the most used herbal medicine (HM), </span>and Oedema 30 (47.6%) was the most reported ailment. <span>Among the HM users,</span> 34 (54.0%)<span> believe they are more effective than CMs. Secondary school education (AOR = 2.128, 95%CI = 1.191 – 3.804, p = 0.011), tertiary education (AOR = 2.915, 95%CI = 1.104 – 7.693, p = 0.031), monthly income of greater than NLe 1,000 (AOR = 4.084, 95% CI = 1.269 – 13.144, p = 0.018), and perceived maternal illness (AOR = 0.367, CI = 0.213 – 0.632, p = <0.001) were predictors of self-medication.</span></span></p> <p><strong><span>Conclusion: </span></strong><span>Self-medication practice was highly prevalent and was associated with educational status, monthly income, and maternal illness during pregnancy. Therefore, intervention programs should be designed and implemented to minimize the practice and risk associated with self-medication among pregnant women.</span></p>
An example of critical thinking (CT) snippets usage at Fourah Bay College, University of Sierra Leone
<p>Recorded students & lecturer WhatsApp group conversation while going through the 'CT snippets' which are based on INASP's online course 'Questioning as we learn: An introduction to critical thinking'</p>
Health records from hospitalized adults with malaria during 2019 at Bo Government Hospital, Sierra Leone
<p class="MsoNormal"><span>We performed a chart review of adults admitted to Bo Government Hospital during 2019. Of 893 admissions,149 (59% female, mean age 58.5 years) had a laboratory diagnosis of malaria and 22 (14.8%) died. Mortality was significantly higher among patients with severe malaria compared with those who had non-severe malaria (6/20 [30%] versus 16/129 [12.4%], <em>p</em>=0.031). Our data suggest that malaria is a common cause of death in hospitalized Sierra Leonean adults. </span></p>
Burden of cardiometabolic risk factors and preclinical target organ damage among adults in Freetown, Sierra Leone: a community-based health-screening survey
<p>The study aimed to investigate the prevalence of cardiometabolic risk factors (CMRFs), target organ damage and its associated factors among adults in Freetown, Sierra Leone.</p> <p><strong>Design</strong></p> <p>This community-based cross-sectional study used a stratified multistage random sampling method to recruit adult participants. </p> <p><strong>Setting </strong></p> <p>The health screening study was conducted between October 2019 and October 2021 in Western Area Urban, Freetown, Sierra Leone. </p> <p><strong>Participants</strong></p> <p>A total of 2394 adults Sierra Leoneans aged 20 years, or more were enrolled.</p> <p><strong>Outcome measure </strong></p> <p>Anthropometric data, fasting lipid profiles, fasting plasma glucose, target organ damage, clinical profiles and demographic characteristic of participants were described. The cardiometabolic risks were further related to target organ damage.</p> <p><strong>Results</strong></p> <p>The prevalence of known CMRFs was 35.3% for hypertension, 8.3% for diabetes mellitus, 21.1% for dyslipidemia, 10.0% for obesity, 13.4% for smoking and 37.9% for alcohol. Additionally, 16.1% had left ventricular hypertrophy (LVH) by ECG, 14.2% had LVH by 2D-Echo while 11.4% had chronic kidney disease. Diabetes [OR = 1.176, 95% C.I. (0.759 - 1.823)] and dyslipidaemia [OR = 1.844, 95% C.I. (1.006 -3.380)] were independently associated with LVH. LVH by Echo shows an independent association with diabetes mellitus [OR =1.176, 95% C.I (0.759-1.823)] and dyslipidemia [OR = 1.844, 95% C.I (1.006-3.380)]. The odds of having CKD were associated with diabetes mellitus [OR =1.212, 95% CI (0.741-1.983)] and hypertension [OR =1.163, 95% CI (0.887-1.525)]. A low optimal cut-off points for ECG-LVH (male 24.5 vs female 27.5mm) was required to maximize sensitivity and specificity. </p> <p><strong>Conclusions</strong></p> <p>This study provides novel data-driven information on the burden of cardiometabolic risks and its association with preclinical target organ damage in a resource limited setting. It illustrates the need for interventions in improve cardiometabolic health screening and management among adults in Sierra Leoneans. </p>
An Assessment of Medication Errors among Pediatric Patients in Three Hospitals in Freetown Sierra Leone: Findings and Implications for a Low-Income Country
<p> </p><p><strong>Background</strong></p><p>Pediatric patients are prone to medicine-related problems like medication errors (MEs), which can potentially cause harm. Yet, this has not been studied in this population in Sierra Leone. Therefore, this study investigated the prevalence and nature of MEs, including potential drug-drug interactions (pDDIs) in pediatric patients in three hospitals in Freetown, Sierra Leone.</p><p><strong>Methods: </strong>The study was conducted in three Hospitals among pediatric patients in Freetown and consisted of two phases. Phase one was a cross-sectional retrospective review of prescriptions for completeness and accuracy against standard prescription writing guidelines. Phase two was a point prevalence inpatient chart review of MEs that were categorized into prescription, administration, and dispensing errors and pDDIs. Data was analyzed using frequency, percentages, median, and interquartile range. Kruskal-Wallis H and Mann-Whitney U tests were used to compare the prescription accuracy between the hospitals, with p<0.05 considered statistically significant.</p><p><strong>Results: </strong>In phase one<strong>, </strong>while no prescription attained the global accuracy score (GAS) gold standard of 100%, 106 (29.0%) achieved the 80-100% mark. The patient 63 (17.2%), treatment 228 (62.3%), and prescriber 33 (9.0%) identifiers achieved an overall GAS range of 80-100%. Although the total GAS was not statistically significant (p=0.065), the date (p=0.041), patient (p=<0.001), treatment (p=0.022), and prescriber (p=<0.001) identifiers were statistically significant across the different hospitals. For phase two, the prevalence of MEs was 74 (56.1%), while that for pDDIs was 54 (40.9%). There was a statistically positive correlation between the occurrence of pDDI and the number of medicines prescribed (r=0.211, P=0.015).</p><p><strong>Conclusion: </strong>Low GAS depicts poor compliance with prescription writing guidelines and high prescribing errors. Medication errors were observed at each phase of the medication use cycle, while clinically significant pDDIs were also reported. Thus, there is a need for training on prescription writing guidelines and medication errors.</p><p> </p><p><strong>Keywords:</strong> Pediatrics, Prescription, Medication errors, Drug-drug interactions, Sierra Leone</p>
STRIVE (Sierra Leone Trial to Introduce a Vaccine Against Ebola)
ClinicalTrials.gov study NCT02378753. IPD Sharing: Not stated. Countries: 1. Publications: 13.
Moderate Acute Malnutrition (MAM) and Severe Acute Malnutrition (SAM) in Sierra Leone
ClinicalTrials.gov study NCT01785680. IPD Sharing: Not stated. Countries: 1. Publications: 7.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.