Find research datasets worth reusing

opencc-by-4.0Jan 2024View details →

The state of the lower Volta Delta Beaches in Ghana from field observations.

The continuous collection of data is important, particularly for developing models for local applications. It is also important to collect in situ data in this observation scarce region to improve our understanding of coastal evolution. The data presented here were planned and collected along a 90 km coastline. The experiment holds significant scientific value as it marks the first comprehensive data-gathering effort along the Volta Delta within the Bight of Benin, West Africa. Several projects, decision-makers, and scientists will make use of this data to calibrate their models. The study was executed in 2023, and it provides a comprehensive description of the morphodynamical characteristics of the study area and identifies the factors influencing the changes. The data include nearshore and riverine bathymetry, topography, sediment grain size distribution, and waves in the study area. The research experiment provides a foundation for future extensive and long-term investigations along the LVD and promotes localized investigations.

opencc-by-4.0Jan 2022View details →

Prevalence and Characterization of Asymptomatic Thyroid Nodules in Assin North District, Ghana

Study design and sampling The study was cross-sectional involving six (6) communities in the Assin North District of the Central Region of Ghana. Ethical approval was obtained from the Institutional Review Board of the University of Cape Coast, Ghana, with this reference number: UCCIRB/EXT/2017/18. All community entry protocols with local authorities were observed before the study commenced.  Further, protocol involving informed consent was also duly observed during and after this study. Both verbal and written consent were obtained from each participant prior to participation. Participation in the study was strictly on voluntary basis. Each of these six (6) communities was considered a stratum in which all households were listed to constitute a sampling frame from which the respective number of households in each community were sampled using systematic random sampling technique. The sampling interval (Kth) in each community was determined by dividing the total number of listed households (N) by the number of households respectively require (n) (based on proportion-to-population size) as shown in Table 1. The simple random sampling technique was then employed to select the first household (i<K) in each community, from which every Kth household was selected until the expected number of households in each community was met. One eligible consenting participant in each selected household was then randomly selected for the study.  In a few instances where there were no consenting or eligible participant, the next household on the roll was considered. Exclusion criteria included participants with anterior neck swelling or clinical evidence of thyroid disease, smokers, persons on lithium, phenytoin, oral contraceptive drugs, and women during menstruation, pregnant women or women who had delivered within the last 12 months and persons with any systemic disorder Data collection Data collection was conducted in July, 2019 in two phases. The first phase consisted of face-to-face interviews with participants using a structured interview guide to elicit socio-demographic information such as age, sex, marital status, and highest level of education; history dietary salt intake (often intake- at least 5g or one teaspoon of iodized salt per day and not often intake- less than 5g or one teaspoon of iodized salt per day or not at all); and history of alcohol intake (yes- consumed alcohol regardless of quantity and no- had not consume alcohol before). Anthropometric measurements of body weight (kg) and height (cm) were measured using standard anthropometric techniques and further computed to generate measures of body surface area (BSA) and body mass index (BMI). Data collection in the phase was conducted by six (6) trained research assistants from UCCSMS and duly supervised by key investigators (listed authors) of the study. The second phase of the data collection mainly focused on diagnostic imaging of the thyroid gland by a specialist radiologist with over five (5) working experience in thyroid examination using various imaging technologies. A screening center was staged in each of the study communities on different days while ensuring that such days did not conflict with market days or other important community events. Participants who were interviewed at the household level were given an identification chit to present with to the screening stage for easy synchronization of their interview data with thyroid data. Given that ultrasound has been recognized as the initial imaging modality of choice for the early detection of thyroid nodules [2,9,22,23], a real-time ultrasound scanner (MEDISON SA8000SE-MAI, 1003 Dachi-Dong, Gangnam-Gu, Seoul Korea) with a 7.5 MHz, 50 mm linear transducer was used in examining the thyroid gland of study participants. Participants were examined while in a supine position with hyperextended cervical spine. Ultrasound gel was applied over the thyroid area with the transducer directly placed on the skin over the thyroid gland. Longitudinal and transverse scans were performed, to obtain length and width in centimeters, of each thyroid nodule. If there were multiple nodules in a single thyroid lobe only the dimensions of the largest were recorded. Documented characteristics of thyroid nodules included the location of nodules in the thyroid lobe; number of nodules (solitary or multiple), nodule composition (cyst, solid or mixed), calcifications, and nodule size (length and width in centimeters).  Out of the 343 participants interviewed in the initial phase of the data collection, 23 participants failed showed up for the thyroid screening in the second phase despite countless attempts to contact them. Hence the current study is based on 320 participants who were successfully interviewed and screened. Statistical analysis           The data was captured using SPSS and later exported to STATA 11.0 for further management and analysis. A protocol was designed from the outset for imputing, ensuring data quality and preserving of data for reuse. Descriptive statistics including frequencies, percentages, means and standard deviation were used to summarize participants’ socio-demographic and thyroid characteristics. Bivariate and multivariate logistic regression analyses were conducted to determine factors associated with ATN. Odds ratios and corresponding confidence intervals were reported with statistical significance at p < 0.05

opencc-by-4.0Jun 2022View details →

Location, biophysical and agronomic parameters for croplands in Northern Ghana

We present a dataset describing (i) crop locations, (ii) biophysical parameters and (iii) crop yield and biomass was collected in 2020 and 2021 in Ghana, mostly focusing on maize in northern Ghana. The dataset contains repeated multiple measurements of leaf area index (LAI), leaf chlorophyll concentration over a large number of maize fields, as well as associated grain yield, biomass and polygons that delineate the fields.

opencc-by-4.0Mar 2019View details →

Production and trade data on cocoa value chain in Ghana

This dataset  covers upstream, midstream and downstream behavioural patterns that were used to identify precursors of vulnerabilities in Ghana’s cocoa value chain. Data were obtained via focus group discussions and individual interviews with cocoa farmers in Ghana and extracted from annual reports of the global cocoa industry published by the International Cocoa Organisation. Behavioural patterns were established from transcripts and reports using NVivo as the computer-assisted qualitative data analysis software.

opencc-zeroAug 2024View details →

National Checklists 2017: Ghana Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Ghana collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

National Checklists 2019: Ghana Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from Ghana collected using effechecka and geonames polygons

opencc-by-4.0Sep 2024View details →

Usability Testing Data for Web Application Prototype: Enhancing Efficiency and Transparency in Ghana's Rental Housing Market

The dataset includes both quantitative and qualitative responses from participants who tested the web application prototype designed to enhance decision-making in Ghana's rental housing market. The testing focused on evaluating the user interface, ease of use, satisfaction levels, and the effectiveness of key functionalities.

opencc-by-4.0May 2023View details →

Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study

This section highlights the various methods used for this study. It covered study setting, study design, study approach, study population, sampling techniques, sample size calculation, inclusion and exclusion criteria, ethical consideration, data collection, data management and data analysis.   Study Setting The study was carried out at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi between February to August, 2022.  The study covered all the six (6) Colleges in the University.   Study Design This was a cross sectional study to ascertain health check practices among university lecturers.   Study Approach The study employed quantitative approach in which data was collected using questionnaires with both closed- and open-ended questions. Study Population The study population involved 838 Lecturers across the six Colleges at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi. A study of the lecturer population per college revealed that Colleges of Health Sciences (highest) and Agric /Natural resources (lowest) were the outliers (Quality Assurance and Planning Office, 2020).   Sampling Technique   Simple probability technique was used to select the name of a college and the day/date to visit. Two sets of papers were folded with names of colleges (set 1) and day/date of visit (set 2). A picker picked one folded paper from each set and the name of the college and the day/date to visit was matched. In this case, the ordering of date and visit gave 1st College of Humanities & Social Sciences, 2nd College of Agric and Natural Resources, 3rd College of Art & Built Environment, 4th College of Engineering, 5th College of Science and 6th College of Health Sciences. We then used the ‘walk in’’ system to select the study participants. Within the days to visit a college, any lecturer we meet in his/ her office was a potential study participant.       Sample Size Calculation The sample size was obtained using Yamane, 1967 formulae as shown below:     Where n= is the population of Lecturers in at KNUST E= is the level of precision Therefore:             n= 838                                       1+838 (0.0025) n =    838 1+ 2.098   838 3.095                                                                                                                 n=270                                                                       However, due to logistical constrains, 205 participants were contacted across the 6 Colleges at Kwame Nkrumah University of Science and Technology. We then applied simple proportions to get the number of lecturers to be consulted in each college.   Inclusion and Exclusions Criteria Inclusion criteria was made up of all Lecturers on KNUST campus who are in active service and consented to participate. All other staff not within this category were excluded from this research.   Ethical Considerations Ethical approval was sought from the CHRPE, KNUST with approval reference no: CHRPE/AP/581/21. The aim of the research was explained to participants. Those who consented to participate in the research were given consent forms to sign and date. Again, participants were assured of confidentiality. Participants were told that, they were free to withdraw from the study in the cause of time. In other words, study participants were not coerced into the study.   Data Collection Tool Data was collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues on physical inactivity and tobacco use. Aside these four main risk factors of NCDs, the questionnaire also captured frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-up, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,   Data Management Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.   Data Analysis Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ2) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).   Variables BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank and lecturer’s colleges (categorized into binary variable; COHS /COS/COE and CABE/CANR/COHSS), family history of NCDs and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).

opencc-by-4.0May 2023View details →

Assessment of non-communicable diseases screening practices among university lecturers in Ghana – a cross sectional single centre study

Data Collection Tool Data were collected using structured questionnaires. The questionnaires covered dietary intake, alcohol intake, issues with physical inactivity, and tobacco use. Aside from these four main risk factors of NCDs, the questionnaire also captured the frequency of blood pressure checks, blood pressure outcome anytime it is checked (systolic and diastolic), frequency of general body check-ups, frequency of anthropometric measurement checks (weight and height), an assessment of impressions about the outcome of weight and height checks, an assessment of intended measures to be taken depending on the outcomes of weight and health checked. Again, the general observation of the nature of the job as a lecturer and health status especially the outcome of blood pressure monitoring were also assessed. The questionnaire also captured the socio-demographic status of Lecturers,   Data Management Only the Research Team had access to data. Data was kept confidential. The researchers had planned of disposing data from the storage 5 years after the publication of this research. Collected data was entered and cleaned using Microsoft Excel spread sheet, and then imported into STATA version 14.0 (Stata Corp LP, College Station, Texas, USA) for statistical analysis and results.   Data Analysis Descriptive statistics were used to summarize the characteristics of the study population by employing frequencies and percentages for categorical data. In addition, the degree of relatedness (association) was evaluated using Chi-square (χ2) or Fisher’s exact tests where appropriate with a p ≤0.05 assumed to be statistically significant. Both bivariate and multivariate logistic regression analyses were performed and adjusted for colleges' effect to identify associations among the variables of interest. Variables having significant association in the logistic regression models were set at p≤0.05 with 95% confidence interval (95% CI) for both unadjusted and adjusted odds ratios (OR, AOR).   Variables BP was selected as the dependent variable, and in turn define as Normal: ≤ 120/80 mmHg; Elevated: Systolic between 120-129 and diastolic ≤ 80; Hypertension: Systolic ≥ 130 or diastolic ≥ 80. Then dichotomized into Normal blood pressure: ≤ 120/80 mmHg and high blood pressure (Hypertension): ≥ 130/90 mmHg for logistic regression analyses. Independent variables were socio-demographics; gender, age, marital status, staff rank, and lecturer’s colleges (categorized into binary variables; Colleges, family history of NCDs, and health check status. In this study, the variable “very often” denotes (doing the activity in question more than 4 times a month), “often” denotes (doing the activity in question at least twice a month), and “not often” denotes (doing the activity in question once a month).

Fig. 4 in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana

Fig. 4. Distribution map. Gaertnera luteocarpa sp. nov. subsp. luteocarpa (stars) and G. luteocarpa subsp. sinoensis subsp. nov. (dots).

Fig. 3. A. Gaertnera spicata fruits. B. Gaertnera cooperi fruits. A from Lachenaud & Walters 1163. Photo O.L.S. Lachenaud. B in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana

Fig. 3. A. Gaertnera spicata fruits. B. Gaertnera cooperi fruits. A from Lachenaud & Walters 1163. Photo O.L.S. Lachenaud. B from Jongkind, de Wet & Sambolah 12104. Photo C.C.H. Jongkind.

Fig. 2 in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana

Fig. 2. Gaertnera luteocarpa sp. nov. subsp. sinoensis subsp. nov. A. Fruits and leaves. B. Close up of fruits. From Jongkind, Bilivogui & Daniels 9832. Photos C.C.H. Jongkind.

Fig. 1 in Description of Gaertnera luteocarpa (Gentianales: Rubiaceae), with two subspecies, a new forest shrub species from Liberia, Ivory Coast and Ghana

Fig. 1. Gaertnera luteocarpa sp. nov. subsp. luteocarpa. A. Habit in fruit. B. Fruits. C. Stipules. D. Twig showing ridges around base of petiole. A, B & D from Hawthorne & Gyakari 205a063; C from Hawthorne & Gyakari 201a223. Photos W.D. Hawthorne.

opencc-by-4.0Jan 2021View details →

qdgc Ghana

QDGC tables delivered in geopackage file - - - - - - - - - - - - - - - - - - - - - - QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth. Within each geopackage file you will find a number of tables with these names: -tbl_qdgc_01 -tbl_qdgc_02 -tbl_qdgc_03 -tbl_qdgc_04 -tbl_qdgc_05 -etc The attributes for each table are: qdgc Unique Quarter Degree Grid Cell reference string area_reference Country level_qdgc QDGC level cellsize degrees decimal degree for the longitudal and latitudal length of the cell lon_center Longitude center of the cell lat_center Latitudal center of the cell area_km2 Calculated area for the cell geom Geometry Metadata -------- Geodata GCS_WGS_1984 Datum: D_WGS_1984 Prime Meridian: 0 Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this: - st_area(st_transform(geom, 102022))/1000000) Licensing --------- Creative Commons Attribution 4.0 International Conditions ---------- Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Thankyou -------- The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them: - Tanzania Wildlife Research Institute - Dept of Biology, NTNU, Norway - Norwegian Environment Agency - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey References ---------- * http://en.wikipedia.org/wiki/QDGC * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/ * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection * http://www.safe.com Ragnvald Larsen Trondheim 20th of January, 2021 ragnvald@mindland.com www.mindland.com

opencc-by-4.0Dec 2022View details →

Land use maps Bono East, Ghana (2020 and 2050)

Land use GIS files of the Bono East region in Ghana at a resolution of 30x30 meters. This publication contains two geo-tif files and an accompanying QGIS style. The '2020' GIS file represents the current land use. The '2050' file represents a possible future as created through the application of the iCLUE land use projection model.For more information, see Duku et al (2023) - <a href="https://doi.org/10.18174/584095">https://doi.org/10.18174/584095</a> or <a href="https://edepot.wur.nl/584095">https://edepot.wur.nl/584095</a>

opencc-by-4.0Dec 2023View details →

Transport Starter Data Kit: Historical socio-transport data for Ghana

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.

opencc-by-sa-4.0Dec 2023View details →

Infrastructure Climate Resilience Assessment Data Starter Kit for Ghana

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. 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. 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. Hazards: <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> Exposure: <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> Contextual information: <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> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. 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. These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: <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> 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. 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). References <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 (2023) 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>

opencc-zeroAug 2024View details →

National Checklists: Ghana 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>