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7,742 results for “individuals”
Multiple mitochondrial haplotypes within individual specimens affect biodiversity estimation by DNA metabarcoding
<p>In the global action for biodiversity conservation, the most urgent need is to delimit and identify species. DNA barcoding and metabarcoding based on molecular markers have been increasingly used in species delimitation and species diversity assessment, respectively, owing to their advantages of standardization and high throughput. The molecular markers used in these methods for animals are mainly derived from mitochondrial DNA. The principle involved is that inter-specific genetic distances are greater than intra-specific genetic distances. However, if multiple mitochondrial haplotypes exist within individual specimens and the divergence between these haplotypes is greater than the assumed maximum intra-specific divergence, the species delimitation and species diversity assessment may be affected. In our recent studies, a widespread phenomenon of multiple mitochondrial haplotypes within individual specimens was discovered in fig wasps (Hymenoptera, Insecta), having negative impact on DNA barcoding and metabarcoding. In this study, we expanded the taxa to include up to 480 specimens from 82 species, 66 genera, 45 families, and 13 orders of Insecta and Arachnida. By high-throughput sequencing of mitochondrial <i>cox1</i> fragments within individual specimens, we studied the pattern of multiple mitochondrial haplotypes within individual specimens. Three DNA metabarcoding strategies, one of which was simulated, were applied. The results revealed that multiple mitochondrial haplotypes within individual specimens could lead to an overestimation of species diversity by metabarcoding. Simultaneously, we found that the infection with intracellular endosymbiotic bacteria <i>Wolbachia</i> was significantly negatively related with this effect. These results suggest that additional attention should be paid to the interference of multiple mitochondrial haplotypes within individual specimens on the results of DNA metabarcoding in species diversity assessment of animals.</p>
Figure 3 from: Šerić Jelaska L, Dumbovich V, Kucinic M (2011) Carabid beetle diversity and mean individual biomass in beech forests of various ages. ZooKeys 100: 393-405. https://doi.org/10.3897/zookeys.100.1536
Figure 3 - Proportion of species according to their hibernation strategies (larvae – black columns, adults – white columns) in relation to forest age (years).
Figure 1 from: Barkworth M, Murrell Z (2012) The US Virtual Herbarium: working with individual herbaria to build a national resource. ZooKeys 209: 55-73. https://doi.org/10.3897/zookeys.209.3205
Figure 1 - Regional networks and herbaria in the U.S.A. Network boundaries are guides; herbaria are free to join the network of their choice. Some herbaria contribute records to more than one network. No network has been established as yet for the Great Plains, Great Lakes, and Southern Rocky Mountain Regions. Data obtained June, 2011.
Figure 4 from: Šerić Jelaska L, Dumbovich V, Kucinic M (2011) Carabid beetle diversity and mean individual biomass in beech forests of various ages. ZooKeys 100: 393-405. https://doi.org/10.3897/zookeys.100.1536
Figure 4 - Dendrogram of cluster analyses among forest sites using presence/absence carabid beetle data. Two distinct clusters are formed at roughly 30% similarity. Marks 60y 1–3, 80y 1–3 and 150y. 1–3 denote investigated sites placed in the 60-, 80- and 150-year-old forests.
Figure 1 from: Šerić Jelaska L, Dumbovich V, Kucinic M (2011) Carabid beetle diversity and mean individual biomass in beech forests of various ages. ZooKeys 100: 393-405. https://doi.org/10.3897/zookeys.100.1536
Figure 1 - Position of investigated sites labelled according to the age of the forest ("60y 1–3" denote sites 1 to 3 in the 60-year-old forest, "80y 1–3" denote sites 1 to 3 in the 80-year-old forest and "150y 1–3" denote sites 1 to 3 in the 150-year-old forest). Insert: location of Papuk Nature Park in Croatia.
Daphnia magna life history data for caged and uncaged individuals (lab and mesocosm)
<p>Life history studies are often conducted in a laboratory environment where it is easy to assay individual animals. However, factors such as temperature, photoperiod, and nutrition vary greatly between laboratory and field environments, making it difficult to compare results. Consequently, there is a need to study individual life histories in the field, but this is currently difficult in systems such as Daphnia where it is not possible to mark and track individual animals. Here, we present a proof of principle study showing that field cages are a reliable method for collecting individual-level life history data in Daphnia magna. As a first step, we compared the life history of paired animals reared outside and inside cages to test the hypothesis that cages allow free-flow of algal food resources. We then used a semi-natural mesocosm setting to compare the performance of individual field cages versus glass jars re-filled with mesocosm water each day. We found that cages did not inhibit food flow, and that differences in life histories between three clones detected in the jar assays were also detectable using the much less labour-intensive field cages. We conclude that field cages are a feasible approach for collecting individual-level life history data in systems such as Daphnia where individual animals cannot be marked and tracked.</p>
Processed data with small file sizes [individual subject files]
<p>Processed data with small file sizes [individual subject files]</p>
Individual and population level costs and health-related quality of life outcomes of third-generation cephalosporin resistant bloodstream infection in Malawi
<p>This file contains the original cleaned dataset used in the analysis</p>
Data for manuscript "Individually Addressable and Spectrally Programmable Artificial Atoms in Silicon Photonics"
<p>Data in scientific manuscript published in preprint: https://arxiv.org/abs/2202.02342</p>
Biomechanical Effects of an Articulating Prosthetic Toe Joint During Stair Navigation for Individuals with Unilateral, Below-Knee Limb Loss
<p>See ReadMe.txt (Markdown Format)</p>
Annotated recordings of two captive groups of rooks, with individual identity and context
<p>This dataset was used in our paper "Vocal complexity in a socially complex corvid: gradation, diversity, and lack of common call repertoire in male rooks" (DOI will be added on publication).</p> <p>If you use this dataset in your work, please cite the article (citation to be added on publication).</p> <p>The dataset includes audio recording (stored in "audio.zip") and annotations (stored in "labels.zip" and "clean labels.zip), collected between 2020 and 2022 in two captive groups of rooks in outdoor aviaries, one in Strasbourg (France) and one in Cambridge (UK). The audio was compressed losslessly to FLAC files from the original uncompressed WAV to fit with the Zenodo 50GB limit. They can be converted back to WAV with the Python soundfile package or with FFMPEG from the command line if needed (though some conversion will probably fail due to the ~4GB file size limit on WAV).</p> <p>NOTE: To ease checking data formatting without downloading the entire dataset, the "example.zip" folder contains one audio file and its associated annotations.</p> <p>Two different versions of the annotations files are included: "clean_labels.zip" includes the TSV files used in the analysis for the paper, and "labels.zip" includes the TXT files used for annotation, which can be opened along with the audio files in the Audacity software for reviewing. Each audio file corresponds to one TXT and one TSV file, with corresponding files sharing the same filename. Filename format is 'YYYYMMDD_HHMMSS(_StartXXXX)', meaning the date and time of the beginning of the recording; optionally, "StartXXXX" means that the original recording was split into multiple files, with each file starting XXXX seconds after the start of the original recording.</p> <p>TSV annotations include, for each recorded rook vocalisations: time stamps (Start, End columns), emitter identity (Source column), context of emission (Event column; these annotations are often abbreviations, but the most important distinction is between calls and songs, denoted by the presence or absence of "sing" in the Event cell). and additional comments (Comment column). Special cases for annotations include Inc (unknown single individual vocalised but could not be identified), Pls (several individuals vocalised but overlapped too much to be separated), Comment (for events of note that were not vocalisations), and Ignore (this was used for sections that could not be checked for annotations for any reason; vocalisations may be included but were not annotated).</p> <p>TXT annotations include the same information but the Source and Event columns are merged and the corresponding Comments are additional markers between the vocalisation timestamps, to be compatible with Audacity. This is best viewed in the example file.</p> <p>Additional info regarding the individuals can be found in Table S1 of the paper.</p> <p> </p> <p>The code used for the analysis is hosted at <a href="https://gitlab.com/kimartin/cluster_rook_vocs">https://gitlab.com/kimartin/cluster_rook_vocs</a>.</p> <p>For questions on the dataset, please reach out to <a href="mailto:killian.martin@ens-lyon.fr">killian.martin@ens-lyon.fr</a></p>
Individual .ttl files for plate VGF147 samples v1.0.1
Individual .ttl files for plate VGF147 samples processed using the ENPKG framework
Individual .ttl files for plate VGF138 samples v1.0.1
Individual .ttl files for plate VGF138 samples processed using the ENPKG framework
Supplementary data to the paper: Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane
<p>Supplementary research data to the paper (rejected):</p> <blockquote> <p>Davide Fantini, Federico Avanzini, Stavros Ntalampiras and Giorgio Presti (2023) "Automatic Extraction of Anthropometric Features for the Individualization of the Pinna-Related Transfer Function in the Median Plane"</p> </blockquote> <p>The repository includes the research data generated and analyzed in the abovementioned paper describing a method for PRTF individualization. In particular, the following data are included:</p> <ul> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/README.md">README.md</a>: instructions for the data</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/pinna_range_img.mat">pinna_range_img.mat</a>: pinna range images extracted from the 3D head meshes of the <a href="https://depositonce.tu-berlin.de/items/dc2a3076-a291-417e-97f0-7697e332c960">HUTUBS dataset</a></li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/landmarks.mat">landmarks.mat</a>: landmarks coordinates both manually annotated and automatically placed with ASM</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry.mat">anthropometry.mat</a>: anthropometric parameters automatically extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/img_features.mat">img_features.mat</a>: image features pinna cavities extracted from both manually annotated and ASM-fitted landmarks</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/grnn_models.mat">grnn_models.mat</a>: Generalized Regression Neural Network (GRNN) models trained from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/predicted_dtf.mat">predicted_dtf.mat</a>: Directional Transfer Function (DTF) sets predicted from both HUTUBS anthropometry and the proposed pinna features</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/anthropometry_documentation.pdf">anthropometry_documentation.pdf</a>: documentation of the pinna anthropometric parameters</li> <li><a href="../api/files/bf20ab4d-c913-4cd0-9357-3b4039f1affb/auditory_model_complete_elevation_range.pdf">auditory_model_complete_elevation_range.pdf</a>: auditory model evaluation in the complete elevation range</li> </ul> <p>The data are provided in the Matlab file format MAT. Nevertheless, the MAT files can be read with other programming languages, such as Python (<a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">scipy.io.loadmat</a>).</p> <p>A GitHub repository to automatically extract the pinna landmarks and features as described in the paper is available <a href="https://github.com/DavideFantini/pinna-landmarks-fitting-and-anthropometry-extraction" target="_blank" rel="noopener">here</a>.</p>
individual gene knock outs in K562 cells-1
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-8
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-3
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-5
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-4
<p>This data includes (individual gene knock outs in K562 cells,1-8) bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
individual gene knock outs in K562 cells-2
<p>This data (individual gene knock outs in K562 cells,1-8) includes bulk RNA sequencing data from individual gene knockouts K562 cells for 5 selected genes (KCTD5, EIF2AK1, APAF1, CCNC, IGF1R, triplicate samples for each) in addition to control guideRNA transduced K562 cells (n=5 samples)</p>
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.