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1,904 results for “Kenya”
Evaluating rose yield responses to compost treatments: Data from an 18-month field study in Kenya
<p>This dataset and these scripts supports the manuscript 'Modelling cut rose yield after compost amendment over an 18-month period using repeated sigmoidal Gompertz curve fitting' by Evy de Nijs, Roland Bol, Albert Tietema & Emiel van Loon. </p> <p>Roses are an important crop for the floricultural sector of Kenya. Roses are a perennial crop and under continuous production for six to ten years. To optimize rose production, it is essential to understand how different management practices impact yield over time. This dataset contains a detailed record of rose yield data collected in an 18-month large-scale field experiment. The aim of this experiment was to evaluate the effect of pre-planting compost amendment on the yield and quality of cut roses. It was conducted in a polythene greenhouse near lake Naivasha, Kenya. Yield data included the number of stems harvested per day per flowering bed. Data presented here offer a comprehensive view of the impacts of different compost treatments on the yield of cut roses. Combined with the offered scripts, this is the framework presented in the aforementioned manuscript. This approach allows to use repeated growth curves to analyze yields compared to a baseline General Additive Model. </p> <p> </p> <p>de Nijs, E. A., Tietema, A., Bol, R., & van Loon, E. E. (2025). Modeling Cut Rose Yield Over an 18‐Month Period After Compost Amendment Using Repeated Sigmoidal Gompertz Curve Fitting. <em>Plant‐Environment Interactions</em>, <em>6</em>(3), e70049.</p>
Herbivore dung and parasite counts, Ol Pejeta Conservancy and Mpala Research Centre, Kenya (2015-2018)
Data package contains two datasets of dung surveys, one dataset of parasite egg measurements, and two camera trap datasets collected from Mpala Research Centre and Ol Pejeta Conservancy, Laikipia County, Kenya from November 2015-September 2018. Datasets are provided as part of the publication `Watering sources aggregate parasites with increasing effects in more arid conditions`. Source data files for figures in the manuscript are also provided here.
Plant community data at water sources, Mpala Research Centre, Kenya (2015-2017)
Data package contains four datasets of plant measurements taken at Mpala Research Centre, Laikipia County, Kenya from November 2015-September 2017. Additional code for data analysis is also provided as part of the publication `The effects of herbivore aggregations at water sources on savanna plants differ across soil and climate gradients`.
(Fastq Files) Amplicon sequencing of ama1 and mdr1 to track within-host P. falciparum diversity in Kilifi, KENYA
<p>These data were generated from amplicon sequencing of <em>Plasmodium falciparum</em> <em>ama1 </em>and<em> </em><em>mdr1</em> genes in samples collected from Kilifi, at the coast of Kenya.</p> <p>The two papers that reference these data will soon be included here:</p> <ol> <li> The Journal of Infectious Diseases - https://doi.org/10.1093/infdis/jiac144</li> <li>Wellcome Open Research - https://wellcomeopenresearch.org/articles/7-95</li> </ol> <p>Two objectives were explored:</p> <ol> <li>To determine temporal changes in the genetic diversity of malaria parasites in asymptomatic and febrile infections.</li> <li>To track within-host parasite diversity, throughout treatment in a clinical drug trial.</li> </ol>
Household surveys in four informal settlements in Abidjan (Côte d'Ivoire) and Nairobi (Kenya)
<p><strong>Description:</strong> Latest release of data (anonymized) collected in informal settlements in Côte d'Ivoire and Kenya during my PhD thesis, with the respective metadata (questionnaire files). Please note that some data (geolocation, specific age of participant, and health facilities used) have been ommitted due to personal data protection concerns.</p> <p><strong>Includes:</strong> Data (CSV), questonnaires (XLS), and Jupyter notebooks summarizing the data (using Python).</p> <p><strong>Ethical clearance:</strong> We obtained ethical clearance in Switzerland from EPFL’s HREC (decision n° 068-2020), in Kenya from KEMRI (KEMRI/RES/7/3/1) and the National Commission for Science, Technology & Innovation (NACOSTI/P/21/10921), and in Côte d’Ivoire from the National Health and Life Sciences Ethics Committee (Comité National d’Éthique des Sciences de la Vie et de la Santé, ref. n° 005-22/MSHPCMU/CNESVS-km).</p> <p><strong>Citation:</strong> Pessoa Colombo V. Relating health benefits of water, sanitation, and hygiene services with the context of urban informal settlements: lessons from Côte d'Ivoire and Kenya. PhD thesis. EPFL: Lausanne. 2023. https://doi.org/10.5075/epfl-thesis-10143</p>
Dataset: Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment
<p>This dataset and these scripts supports the article 'Co-composting to close the cycle of resources during rose cultivation in Kenya: An agronomic and pesticide residue assessment' as published in Cleaner Waste Systems. https://doi.org/10.1016/j.clwas.2024.100154</p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we focused on exploring the potential of large-scale composting of rose waste in Kenyan rose cultivation. The objective of this study was to examine the potential of composting rose waste in this large-scale commercial setting with low operational costs, exploring its benefits and challenges.</p> <p>In piles of 4000 kg green waste the evolution of three mixtures was closely monitored in terms of their physico-chemical parameters. Furthermore, the pesticide residue levels of mature rose waste were assessed. </p>
Dataset: The impact of rose-waste compost on commercial cut rose cultivation in Kenya
<p>This dataset and these scripts supports the manuscript 'From waste to fertilizer: The impact of rose-waste compost on commercial cut rose cultivation in Kenya' as submitted to Cleaner Waste Systems. </p> <p>Roses are an important crop for the floricultural sector of Kenya and roses are a perennial crop and under continuous production for six to ten years. The cultivation produces large quantities of green waste, up to 50 kg per hectare per day. In this experiment we evaluated the impact of compost amendment on the yield and quality of cut roses cultivation in a large-scale commercial setting with over 7,500 rose plants. It was conducted between August 2022 and February 2024 near Lake Naivasha in Kenya. Additionally we evaluated the potential of compost to partially substitute mineral fertilizer. Yields were recorded daily and cut rose quality and physicochemical soil parameters were assessed every three to six months to comprehensively evaluate the impact of compost incorporation on cut rose production.</p>
Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain)
<h2><span lang="EN-US">Dataset name</span></h2> <p><span lang="EN-US">Small_Scale_Fishery_Data_2023_v2 </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Title</span></h2> <p><span lang="EN-US">Qualitative Data on 60 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain). </span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Description </span></h2> <p><span lang="EN-US"> This dataset was created for the Fish2Sustainability research project, which aims to evaluate how small-scale fisheries (SSF) contribute to Sustainable Development Goals (SDGs). The dataset includes 60 case studies across eight countries and was developed using a rapid appraisal framework. The framework includes a four-step process: </span></p> <p><span lang="EN-US"> 1. Identifying specific SDG targets influenced by SSF;</span></p> <p><span lang="EN-US"> 2. Extracting relevant variables from UN indicators;</span></p> <p><span lang="EN-US"> 3. Gathering expert input via a questionnaire to score these variables;</span></p> <p><span lang="EN-US"> 4. Creating composite indicators to measure SSF performance against SDGs.</span></p> <p><span lang="EN-US"> The dataset contains raw data from step 3, case study details, variable scores, and comments from data collectors (contributing authors). The dataset is valuable for researchers interested in small-scale fisheries and socio-ecological systems. By incorporating expert judgments from individuals with expertise in SSF, particularly in data-poor contexts, the dataset offers a wealth of knowledge for conducting comparative analyses across different contexts.</span><span lang="EN-US"> </span></p> <h2><span lang="EN-US">Authors </span></h2> <p><span lang="EN-US">Léopold, M.1, Bitoun, R.E.2, Beckensteiner, J.3, Chuenpagdee, R.4, Fondo, E.N.5, Akintola, S.L.6, Bach, P.7, Frangoudes, K.8, Gaibor, N.9, Gutierrez-Cala, L.10, Massey, Y.7, Randrianandrasana, R.11, Razanakoto, T.11, Saavedra-Díaz, L.M.10, Schreiber Arias, M.12,13, Salas, S.14, Devillers, R.2,4 </span></p> <h3><span lang="EN-US">Affiliations </span></h3> <p><span lang="EN-US">1 ENTROPIE (IRD, University of La Reunion, CNRS, University of New Caledonia, Ifremer), c/o IUEM, Plouzané, France </span></p> <p><span lang="EN-US">2 Espace-Dev (IRD, Univ. </span>Montpellier, Univ. Guyane, Univ. La Réunion, Univ. Antilles, Univ. Nouvelle Calédonie), Montpellier, France</p> <p>3 AMURE (Ifremer, UBO, CNRS), Plouzané, France</p> <p><span lang="EN-US">4 Department of Geography, Memorial University of Newfoundland, St. John’s, NL, Canada</span></p> <p><span lang="EN-US">5 Kenya Marine and Fisheries Research Institute, Mombasa, Kenya</span></p> <p><span lang="EN-US">6 Department of Fisheries, Faculty of Science, Lagos State University, Nigeria</span></p> <p><span lang="EN-US">7 MARBEC, University of Montpellier, CNRS, Ifremer, IRD, Sète, France</span></p> <p>8 Université de Bretagne Occidentale: Brest, France</p> <p>9 Instituto Público de Investigación de Acuicultura y Pesca (IPIAP), Universidad del Pacifico (UPAC), Guayaquil, Ecuador</p> <p>10 Grupo de Investigación en Sistemas Socioecológicos para el Bienestar Humano (GISSBH), Programa de Biología, Universidad del Magdalena, Colombia</p> <p>11 Centre d’Etudes et de Recherches Economiques pour le Développement (CERED), Université d’Antananarivo, Madagascar</p> <p><span lang="EN-US">12 EqualSea Lab, Universidad Santiago de Compostela, A Coruña, Spain</span></p> <p><span lang="EN-US">13 School of Global Studies, University of Gothenburg, Gothenburg, Sweden</span></p> <p>14 Centro de Investigación y de Estudios Avanzados (CINVESTAV), IPN, Unidad Mérida, Mexico </p> <h2><span lang="EN-US">Method </span></h2> <p><span lang="EN-US">Case studies were selected in eight countries by national SSF experts, based on specific criteria and research priorities. Case studies were not selected to represent the full diversity of SSF globally or even nationally. Instead, they were chosen to capture a range of fisheries that could showcase different contributions to SDGs. SSF were defined based on various characteristics, such as resources harvested, gear used, and location of the fishery. </span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Geographical Coverage </span></h3> <p><span lang="EN-US">60 small-scale fisheries located in seven countries are documented in the data:</span></p> <ul> <li><span lang="EN-US">Colombia (4 case studies) – Pacifico: La Guajira, San Andrés y Providencia; Caribe: Chocó, Cauca, Valle del Cauca, Nariño.</span></li> <li><span lang="EN-US">Ecuador (3) – Region: Esmeraldas, Manabi, Guayas, El Oro.</span></li> <li><span lang="EN-US">France (2) – Region: Bretagne, Occitanie.</span></li> <li><span lang="EN-US">Kenya (22) – County: Kilifi, Kwale, Lamu, Mombasa, Tana River.</span></li> <li><span lang="EN-US">Madagascar (20) – Region: Analanjirofo, Anosy, Atsimo Andrefana, Boeny, Diana, Menabe, Vatovavy Fitovinany.</span></li> <li><span lang="EN-US">Mexico (2) – State: Baja California Sur, Campeche, Yucatan.</span></li> <li><span lang="EN-US">Nigeria (6) – State: Bayelsa, Cross River, Lagos, Ondo, Ogun. </span></li> <li><span lang="EN-US">Spain (1) – State: Galicia.</span> </li> </ul> <h3><span lang="EN-US">Data Collection </span></h3> <p><span lang="EN-US">Data collection took place from November 30, 2022, to July 3, 2023, spanning approximately seven months. The data presented serve as a snapshot of the conditions within a specific small-scale fishery during the assessment period. To consider the evolution of trends such as exports, economic growth, and income, we considered any relevant variables over the past decade. </span></p> <p><span lang="EN-US">Data collection approaches varied depending on the context, and data collectors received training to ensure survey consistency. We used primary data sources such as interviews, observations, and measurements whenever possible. In cases where resources were limited, we preferred secondary sources such as existing datasets and literature. Our methods were standardized, but data collectors could adjust them based on their resources. We primarily used direct observation, focus groups, and interviews to collect data. Scoring in interviews and focus groups was done directly or through group analysis by interviewers. Disagreements were resolved through additional interviews or group discussions, with secondary data used if needed. Please refer to the methods in : </span></p> <p><strong><span lang="EN-US">Bitoun et al., (2024). A methodological framework for capturing marine small-scale fisheries’ contributions to the sustainable development goals. Sustainability Science, 19(4), 1119–1137. https://doi.org/10.1007/s11625-024-01470-0. </span></strong><span lang="EN-US"><strong> </strong> </span></p> <h3><span lang="EN-US">Ethics </span></h3> <p><span lang="EN-US">Participants had the option to join of their own accord, were fully briefed on the research goals, and were given the opportunity to review interview guidelines before proceeding. Depending on the circumstances, interviews could last 45 minutes to 4.5 hours. Participants were guaranteed confidentiality and anonymity in the handling and reporting of their data.</span><span lang="EN-US"> </span></p> <h3><span lang="EN-US">Suggested citation</span></h3> <p><span lang="EN-US">Léopold, M., Bitoun, R., & Devillers, R. (2023). Qualitative Data on 61 Small-Scale Fisheries: A socio-ecological rapid appraisal applied to cases from America (Colombia, Ecuador, and Mexico), Africa (Kenya, Madagascar, and Nigeria), and Europe (France and Spain) (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.16077739</span></p> <h2><span lang="EN-US">Data Files</span></h2> <p><span lang="EN-US">The dataset includes the following:</span></p> <ul> <li><span lang="EN-US">The raw dataset (.xls format).</span></li> <li><span lang="EN-US">A data dictionary describing and defining each dataset column (.xls format).</span></li> </ul>
Data for B-vitamin content in maternal diet and infant morbidity among breastfeeding mother-infant dyads from northern Kenya
<p>Data for a manuscript entitled, "B-vitamin content in maternal diet and infant morbidity among breastfeeding mother-infant dyads from northern Kenya" by Amelia N. Odo et al. The original version was published in April 13, 2020.</p> <p>Version 2 includes data from more participants and additional variables relevant for characterizing maternal and infant dietary characteristics.</p> <p>Version 3 includes the variable "other" for other foods as part of the dietary diversity data. The codebook for the variable "inflam" has been updated (1 = CRP > 3 mg/l; 0 = CRP < 3 mg/l) instead of 10 mg/l stated in Version 1/2. The information on grant support has been added.</p> <p> </p>
A dataset of human and Plasmodium falciparum genotypes in severe malaria cases from The Gambia and Kenya
<p>This data release contains human and <em>Plasmodium falciparum</em> malaria genotypes from the article:</p> <p><strong>Malaria protection due to sickle haemoglobin depends on parasite genotype</strong></p> <p>Gavin Band, Ellen M. Leffler, Muminatou Jallow, Fatoumatta Sisay-Joof, Carolyne<br> M. Ndila, Alexander W. Macharia, Christina Hubbart, Anna E. Jeffreys, Kate Rowlands, Thuy<br> Nguyen, Sónia Gonçalves, Cristina V. Ariani, Jim Stalker, Richard D. Pearson, Roberto<br> Amato, Eleanor Drury, Giorgio Sirugo, Umberto d'Alessandro, Kalifa A. Bojang, Kevin<br> Marsh, Norbert Peshu, Joseph W. Saelens, Mahamadou Diakité, Steve M. Taylor10, David J.<br> Conway, Thomas N. Williams, Kirk A. Rockett, Dominic P. Kwiatkowski</p> <p>Nature (2021) doi: <a href="https://doi.org/10.1038/s41586-021-04288-3">10.1038/s41586-021-04288-3</a> <strong>bioRxiv link</strong>: <a href="http://doi.org/10.1101/2021.03.30.437659">doi.org/10.1101/2021.03.30.437659</a>.</p> <p>The release contains genotypes from human and <em>Plasmodium falciparum</em> genetic variants, genotyped using blood samples from 4,171 children ascertained with severe symptoms of malaria at the Royal Victoria Teaching Hospital (now the Edward Francis Small Teaching Hospital), The Gambia, and from the Kilifi District Hospital (now Kilifi County Hospital), Kenya in the period 1995-2009.</p> <p>An accompanying set of association test summary statistics has also been released on Zenodo (doi: <a href="https://doi.org/10.5281/zenodo.5722497">10.5281/zenodo.5722497</a>). Please see <a href="http://www.malariagen.net/resource/32">www.malariagen.net/resource/32</a> for full details of other resources associated with the above manuscript.</p> <p> </p>
Survey of open data and information seeking in Kenya's Urban Slums and Rural Settlements
<p>This dataset provides survey responses from 240 people surveyed as part of the "Investigating the Impact of Kenya’s Open Data Initiative on Marginalized Communities: Case Study of Urban Slums and Rural Settlements" project.</p> <p>The data, collected in mid-2013 looks at issues of where citizens look for data, and how successful they have been in getting government information from different sources, as well as their awareness of the Kenya open data portal, and their interest in getting information through different digital channels in future.</p> <p>Descriptive statistics have been analysed in the publication "Open Government Data for Effective Public Participation: Findings of a Case Study Research Investigating The Kenya's Open Data Initiative in Urban Slums and Rural Settlements", but no further analysis has yet been carried out.</p> <p><strong>Data descriptions</strong></p> <p>The Codebook.csv file lists variable names and the questions asked to elicit each response.</p> <p>JHC-Data.csv contains the results from the questionnaires collected through structured in-person interview in the three locations. The questionnaires were administered at chiefs centres, community resource centres, constituency development fund office and religious centres). The questionnaires were filled in by every 2nd these centres.</p> <p><strong>More information</strong></p> <p>More information on the research project can be found at http://opendataresearch.org/project/2013/jhc</p>
A collection of annotated soundscape recordings from western Kenya
<p>This collection contains 35 soundscape recordings of 32 hours total duration, which have been annotated with 10,294 labels for 176 different bird species from western Kenya. The data were recorded in 2021 and 2022 west and southwest of Lake Baringo in Baringo County, Kenya. This collection has partially been featured as test data in the 2023 BirdCLEF competition and can primarily be used for training and evaluation of machine learning algorithms.</p> <p><strong>Data collection</strong></p> <p>For this collection, AudioMoths and SWIFT recording units were deployed at multiple locations west and southwest of Lake Baringo, Baringo County, Kenya between Dezember 2021 and February 2022. Recording locations cover a variety of habitats from open grasslands to semi-arid scrubland and mountain forests. Recordings were originally sampled at 48 kHz and converted to MP3 for faster file transfer. For publication, all files were resampled to 32 kHz and converted to FLAC.</p> <p><strong>Sampling and annotation protocol</strong></p> <p>A total of 32 hours of audio from various sites west and southwest of Lake Baringo were selected for annotation. Annotators were tasked with identifying and labeling each bird call they could discern, excluding any calls that were too weak or indiscernible. The annotation process was carried out using Audacity. Provided labels mark the center of each bird call. In this collection, we use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list). Parts of this dataset have previously been used in the 2023 BirdCLEF competition. </p> <p><strong>Files in this collection</strong></p> <p>Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date and timestamp in EAT (UTC+3). As an example, the file “KEN_001_20211207_153852.flac” has sequential ID 001 and was recorded on December 7th 2021 at 15:38:52 EAT. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.</p> <p><strong>Acknowledgements</strong></p> <p>Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection. In particular, our thanks go to Francis Cherutich for setting up recording units, collecting and annotating data, and to Alain Jacot for assisting in programming the units and transporting the recorders to Kenya.</p>
Dataset: Spatial Data Starter Kit for OnSSET Energy Planning in Kitui County, Kenya
<p>This is a set of openly-available data pre-processed to facilitate county-level energy planning using the Open Source Spatial Electrification Tool (OnSSET) in Kitui County, Kenya. It provides a ready-to-use starter kit of data inputs for county-level OnSSET analysis. The work to identify these data is submitted for publication - publication details will be added here as soon as possible upon release. These data are contained in spatial data files used to create the input for OnSSET in Kitui, and a prepared CSV data input for OnSSET in Kitui (<em>kitui_OnSSET_data</em>). The following spatial data files are included in the dataset:</p> <ul> <li>kitui_admin: A vector (.geojson) file containing the administrative boundaries of Kitui county. </li> <li>kitui_clusters: A vector (.geojson) file locating population clusters generated in data processing for OnSSET.</li> <li>kitui_demand: A raster (.tif) file containing merged health, agriculture, commercial, and residential demands for Kitui county in kWh.</li> <li>kitui_elevation: A raster (.tif) containing elevation information.</li> <li>kitui_GHI: A raster (.tif) file containing global horizontal irradiance data for Kitui county.</li> <li>kitui_hydro: A vector (.geojson) file containing the locations of hydropower stations in Kitui county. Note that there are none, and that this is expected.</li> <li>kitui_night_lights: A raster (.tif) file capturing the light emitted from Kitui county at night.</li> <li>kitui_power_stations: A vector (.geojson) file showing the locations of power stations in Kitui county.</li> <li>kitui_roads: A vector (.geojson) file showing the main roadways in Kitui county.</li> <li>kitui_transformers: A vector (.geojson) file showing transformer locations in Kitui county.</li> <li>kitui_transmission_lines: A vector (.geojson) file locating transmission lines in Kitui county. </li> <li>kitui_travel_hours: A raster (.tif) file showing travel time to the nearest market center in Kitui county.</li> <li>kitui_wind_100: A raster (.tif) file of wind speeds at 100 m in Kitui county.</li> </ul> <p>This dataset has been produced through work undertaken in the Climate Compatible Growth Programme.</p>
Ariaal mother and milk sIgA data from 2006 drought in northern Kenya
<p>Data utilized for the manuscript, "Human milk secretory immunity in relation to maternal nutrition and infant vulnerability in northern Kenya."</p>
National Checklists 2017: Kenya 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 Kenya collected using effechecka and geonames polygons
National Checklists 2019: Kenya 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 Kenya collected using effechecka and geonames polygons
Geospatial Analysis of Economic Development in kenya by Province
<p>This dataset presents both vector and raster data combinations for pm2.5, elevation, nightlight data, population density, area, and population that can be used to estimate the economic development of Kenya using distribution of banks as a proxy.</p>
Data from: Genomic analysis reveals limited hybridization among three giraffe species in Kenya
<p>The data deposited here was generated by and reported in Coimbra <em>et al.</em> (2023).</p> <p><em>SNP calling and linkage pruning</em></p> <ul> <li><strong>snp_calling_per_species.tar.gz:</strong> includes a genotype likelihoods (GL) file estimated with ANGSD for each giraffe species.</li> <li><strong>sampled_ld.tar.gz:</strong> contains a random sample of estimated pairwise <em>r<sup>2</sup></em> values for each species used to fit linkage disequilibrium (LD) decay curves.</li> <li><strong>ld_pruned_snps.tar.gz:</strong> contains an LD-pruned ANGSD GL file per species.</li> <li><strong>snp_calling_combined.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all sampled individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Relatedness</em></p> <ul> <li><strong>relatedness.tar.gz:</strong> contains the input and output files used with NGSremix to estimate relatedness among giraffe in the dataset.</li> <li><strong>snp_calling_combined_unrelated.tar.gz:</strong> includes a single LD-pruned ANGSD GL file comprising all unrelated individuals of the three giraffe species analyzed in this study.</li> </ul> <p><em>Population structure and admixture</em></p> <ul> <li><strong>pcangsd.tar.gz:</strong> contains the covariance matrix generated by PCAngsd.</li> <li><strong>ngsadmix.tar.gz:</strong> includes run likelihood lists for each K value ranging from 1 to 11, as well as the admixture proportions (stored in '.qopt' files) inferred from the run with the highest log-likelihood for each K in NGSadmix.</li> <li><strong>evaladmix.tar.gz:</strong> contains the pairwise correlation of residuals between individuals estimated with evalAdmix for the NGSadmix runs with the highest log-likelihood run for each K.</li> </ul> <p><em>SNP-based phylogenomic inference</em></p> <ul> <li><strong>snp_phylogeny.tar.gz:</strong> contains the input PHYLIP file and the IQ-TREE output tree and log files.</li> </ul> <p><em>Phylogeny of mitochondrial genomes</em></p> <ul> <li><strong>mtdna_phylogeny.tar.gz:</strong> includes the 13 mitochondrial protein-coding gene alignments, the partitions file, and the IQ-TREE output tree and log files.</li> </ul> <p><em>Inference of migration events</em></p> <ul> <li><strong>admixture_graphs.tar.gz:</strong> contains the TreeMix / OrientAGraph input file ('treemix.frq.strat.gz'), the output files for all TreeMix and OrientAGraph runs, and the OptM summary table of TreeMix runs ('optm.tsv').</li> </ul> <p><em>Test for introgression</em></p> <ul> <li><strong>dsuite_introgression.tar.gz:</strong> includes the input VCF, the admixture graph topology reconstructed by OrientAGraph, and the Dsuite output files for the estimation of Patterson's D, f4-ratio, and f-branch statistics.</li> </ul> <p><em>Contemporary migration rates</em></p> <ul> <li><strong>ba3-snps.tar.gz:</strong> contains the input and output files for the BA3-SNPs-autotune and BA3-SNPs runs.</li> </ul> <p><em>Demographic reconstruction</em></p> <ul> <li><strong>demographic_inference.tar.gz:</strong> includes the SFS files generated with ANGSD and realSFS and the StairwayPlot2 blueprint and output files.</li> </ul> <p>Other:</p> <ul> <li><strong>metadata.csv:</strong> a companion file containing sample information used in conjunction with R scripts to plot the figures in the paper.</li> </ul>
Fig. 10 in Additions to the taxonomy of the Afrotropical Tetramorium weitzeckeri species complex (Hymenoptera, Formicidae, Myrmicinae), with the description of a new species from Kenya
Fig. 10. Body in lateral view. A. Tetramorium weitzeckeri Emery (CASENT0103295, AntWeb, A. Nobile 2006). B. T. boltoni Hita Garcia, Fischer & Peters (ZFMKHYM20096155).
Fig. 11 in Additions to the taxonomy of the Afrotropical Tetramorium weitzeckeri species complex (Hymenoptera, Formicidae, Myrmicinae), with the description of a new species from Kenya
Fig. 11. Tetramorium mpala sp. nov. (CASENT0316967). A. Body in lateral view. B. Body in dorsal view. C. Head in frontal view.
ScienceDex guides
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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.