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151 results for “Reference dataset”
Genomic datasets of Laminaria digitata: Paired-end reads from dd-RADseq, reference genome assembly and filtered VCF
Open the record for dataset details and reuse information.
Updated functional annotation of the Mycobacterium bovis AF2122/97 reference genome - datasets
<p>This is an archive of the repository held under https://github.com/dmnfarrell/gordon-group/tree/master/mbovis_annotation</p> <p>It contains a notebook and required input files for updating the MTB/Mbovis AF2122/97 genomes with new protein product annotations from literature.</p> <p>D Farrell UCD February 2020</p> <p>Updates to M.bovis genome (Aug 2019):<br> added 611 product annotations<br> added 689 gene names<br> added 5 manually edited entries from pdb hits<br> removed locus_tags for repeat_region features<br> added prefix to tRNA and mobile_element features for consistency</p> <p><br> References<br> Updated functional annotation of the Mycobacterium bovis AF2122/97 reference genome (https://www.biorxiv.org/content/10.1101/757823v1)</p>
Dataset for publication "Setup and Characterisation of Reference Current-to-Voltage Transformers for Wideband Current Transformers Calibration up to 2 kA"
<p>This is dataset related to paper published in AMPS 2019:</p> <p>Y. Chen, E. Mohns, H. Badura, P. Räther and M. Luiso: Setup and Characterisation of Reference Current-to-Voltage Transformers for Wideband Current Transformers Calibration up to 2 kA, <a href="https://doi.org/10.7795/EMPIR.17NRM01.CA.20190411">https://doi.org/10.7795/EMPIR.17NRM01.CA.20190411</a></p> <p>Excel file contains the data for Figures 7, 8, 9 and 10.</p>
英語版Wikipediaにおける学術文献の参照記述の初出時点データセット (Dataset of First Appears of Scholarly References on English Wikipedia)
<p>概要</p> <p>筑波大学大学院図書館情報メディア研究科に提出準備中の博士論文「Wikipediaにおける学術文献の参照記述に関する研究」の付録とする予定のデータセットです。作者は<a href="https://researchmap.jp/jir_o">吉川次郎</a>です。</p> <p>ごく簡単な説明</p> <ul> <li>博論本体の付録としてドキュメントを書きました (そのドラフトは<a href="https://www.dropbox.com/s/c8h0hkkbu2wb4jm/PhDThesis_Appendix.pdf?dl=0">こちら</a>)</li> <li>英語版Wikipedia上の学術文献の参照記述に関するデータセットです <ul> <li>dataset_doi_links はDOIリンクを対象として特定・抽出し、研究分野の紐付けを行ったものです (詳細は関連論文の1番)</li> <li>dataset_refs はdataset_doi_linksの参照記述群を対象に、誰が、いつ追加したのか? の特定を行ったものです (詳細は関連論文の2番)</li> </ul> </li> <li>いまのところ、ドキュメントは日本語のみです。英語への対応等は今後の課題です;</li> </ul> <p>関連論文</p> <ol> <li>吉川次郎; 高久雅生; 芳鐘冬樹: 「DOIリンクに基づくWikipedia上の参照記述における編集者の分析」, 情報知識学会誌, Vol. 30, No. 1, pp. 21--41, 2020. <a href="https://doi.org/10.2964/jsik_2020_004">https://doi.org/10.2964/jsik_2020_004</a></li> <li>吉川次郎; 高久雅生; 芳鐘冬樹: 「Wikipediaに学術文献の参照記述を追加する編集の特定手法」, 情報知識学会誌, Vol. 30, No. 3,2020 (全20ページ,採録決定).<a href="https://doi.org/10.2964/jsik_2020_033">https://doi.org/10.2964/jsik_2020_033</a></li> <li>吉川次郎; 高久雅生; 芳鐘冬樹: 「Wikipedia上の学術文献の参照記述の追加に関する時系列分析」(投稿中)</li> </ol>
Dataset for "Study on Reference Frames and Geographic Scale"
<p>The analysis code was written in RStudio.</p> <p>The user data folder contains six .csv flies:</p> <ul> <li>Separate_pointing_errors.csv: participants' performance in the onsite pointing task</li> <li>Pre-Questionnaire.csv: participants' responses to the pre-questionnaire</li> <li>Post_Questionnaire.csv: participants' responses to the post-questionnaire</li> <li>Reference_frame_preference.csv: categorizing participants based on their reference frame proclivities</li> <li>LookingDirection_seperate.csv: participants' looking directions during maze learning</li> <li>Separate_time_data.csv: participants' time spent at each location in the virtual maze</li> </ul> <p> </p>
Gathered datasets of cardiorespiratory activity by using the inductive coupling with single coil together with reference waveforms of ECG and spirometer
<p>Hereby the datasets, that were gathered during the experiments of monitoring the cardiorespiratory activity by using the inductive coupling together with the reference waveforms of ECG and spirometer, are published.:<br> Waveforms of inductive monitoring.zip<br> Reference waveforms of ECG and spirometer.zip</p> <p>A single volunteer participated in the experiments: healthy male, 33 years old, height of 183 cm and weight of 70 kg.</p> <p>The results are gathered from 12 positions lying around the thorax in imaginary horizontal level, approximately 10 mm below xiphisternal joint. The central position on front side of thorax is designated according to xiphisternum. The central position on front side of thorax is designated according to backbone.</p> <p>The datasets are available in .txt format, and are in separate .zip packages for inductive monitoring and reference waveforms. The correcponding dataset for every position can de identified by position number in both datasets. The signals are raw signals, not processed in any way.</p> <p>In the case of inductive monitoring the change variation of equivalent parallel resonance impedance (Rp) is shown. Concerning the reference waveforms, in the case of ECG, the result is shown in voltages (V). The reference data of respiration (gathered by using spirometer) is available in the form of two sets of digital pulses (0-s and 1-s). These pulses are representing the inhalation and exhalation i.e. either the rotor of the spirometer turns in one way or another. According to the frequency of the pulses, depending on the air flowing speed, the respiratory waveform can be constructed.</p>
Experimental dataset referring to: Non-intrusive temperature measurements for transient freezing in laminar internal flow using laser induced fluorescence
<p>This data set corresponds to the paper 'Non-intrusive temperature measurements for transient freezing in laminar internal flow using laser induced fluorescence. Please cite this paper when using this data. </p><p>The following conditions are included (for both the inlet and the centre of the channel):</p><p>Re = 474, T_in,set = 0.5</p><p>Re = 474, T_in,set = 5.0</p><p>Re = 474, T_in,set = 10.0</p><p>Re = 474, T_in,set = 15.0</p><p>Each folder includes the original two-color LIF ratio-metric data and the temperature recordings of the cold-plate as well as the inlet, outlet temperatures and the flow rate (TData). The header for the recordings is included in the main dataset which may be used to navigate the columns and select the relevant data.</p><p> </p>
LsRTDv1: A reference transcript dataset for accurate transcript-specific expression analysis in lettuce
<p>Accurate quantification of gene and transcript-specific expression, with the underlying knowledge of precise transcript isoforms, is crucial to understanding many biological processes. Analysis of RNA sequencing data has benefited from the development of alignment-free algorithms which enhance the precision and speed of expression analysis. However, such algorithms require a reference transcriptome. Here we present a reference transcript dataset (LsRTDv1) for lettuce, combining long- and short-read sequencing with publicly available transcriptome annotations, and filtering to keep only transcripts with high-confidence splice junctions and transcriptional start and end sites. LsRTDv1 is a valuable resource for the investigation of transcriptional and alternative splicing regulation in lettuce.</p>
Onload Tap Changer reference voltage Dataset at different loading condition
<p>The dataset contain the reference voltage of onload tap changer (OLTC) at different loading condition for a month. In addition, the hourly voltages of the feeders regulated by the OLTC are present in the dataset at the corresponding loading conditions</p>
Supplemental datasets for 'A near telomere-to-telomere phased reference assembly for the male mountain gorilla (Gorilla beringei beringei)'
<p>The endangered mountain gorilla <em>Gorilla beringei beringei</em> faces numerous threats to its survival, highlighting the urgent need for genomic resources to aid conservation efforts. Here, we present a near telomere-to-telomere, haplotype-phased reference genome assembly for a male mountain gorilla generated using Pacbio HiFi and Oxford Nanopore Ultralong data. The resulting assembly exhibits exceptional contiguity, with contig N50 of ~ 95 Mbps for the combined pseudohaplotype (3,540,458,497 bps, and 56.5 Mbps (3.1 Gbps) and 51.0 Mbps (3.2 Gbps) for the maternal and paternal haplotypes and an average QV of 65.15 (error rate = 3.1 x 10-7). These represent substantial improvements over most other available primate genomes. This high-quality reference genome provides an invaluable resource for future studies on gorilla evolution, adaptation, and conservation, ultimately contributing to the long-term survival of this iconic species.</p> <p>A preprint for this work is available at bioRXiv, doi: https://doi.org/10.1101/2024.10.28.620258</p>
Dataset: Towards a representative reference for MRI-based human axon radius assessment using light microscopy
<p>Dataset for: Towards a representative reference for MRI-based human axon radius assessment using light microscopy</p>
KPMP Datasets for a Reference Tissue Atlas for the Human Kidney
<p>This is the dataset for the publication of A Reference Tissue Atlas for the Human Kidney from the Kidney Precision Medicine Project (KPMP). KPMP is building a spatially-specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 subjects. We use single-cell/nucleus transcriptomics, subsegmental laser-microdissection transcriptomics and proteomics, near-single-cell proteomics, 3-D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites and pathways. They further show that mRNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.</p>
Reference Datasets for: SHAFTS (v2022.3): a deep-learning-based Python package for Simultaneous extraction of building Height And FootprinT from Sentinel Imagery
<p>These are reference building height and footprint datasets which consist of 46 cities worldwide and support the development of SHAFTS (https://github.com/LllC-mmd/3DBuildingInfoMap).</p> <p>The snapshot of original reference datasets from 46 cities and related GitHub repository has been created as a zipped file named <strong><em>SHAFTS_220527_snapshot.zip</em></strong>.</p> <p>On 2022.5.27, we added 8 additional cities from ArcGIS Hub when compared with the previous version (https://doi.org/10.5281/zenodo.6370003).</p>
Dataset for publication: "Empirical Characterization of Cable Effects on a Reference Lightning Impulse Voltage Divider"
<p>Dataset for publication: "Empirical Characterization of Cable Effects on a Reference Lightning Impulse Voltage Divider". Excel file contains the data for Figures 5 and 6.</p>
LMAS Test Dataset - NIBSC Gut DNA Reference
<p>The twenty bacterial replicons of the National Institute for <a href="https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-020-00856-3">Biological Standards and Control (NIBSC) Gut Community Standards</a> were used as reference. It includes the following strains:</p> <table> <thead> <tr> <th>Species</th> <th>Culture collection number</th> <th>Accession numbers</th> <th>Status</th> <th>Gut-Mix-RR Coverage (x)</th> <th>Gut-HiLo-RR Coverage (x)</th> </tr> </thead> <tbody> <tr> <td>Akkermansia muciniphila</td> <td>DSM 22959</td> <td>NC_010655</td> <td>Complete genome</td> <td>33.33</td> <td>0.94</td> </tr> <tr> <td>Alistipes finegoldii</td> <td>DSM 17242</td> <td>NC_018011</td> <td>Complete genome</td> <td>16.94</td> <td>4.85</td> </tr> <tr> <td>Anaerostipes hadrus</td> <td>DSM 3319</td> <td>NZ_KB290627</td> <td>Complete genome</td> <td>24.04</td> <td>6.89</td> </tr> <tr> <td>Bacteroides thetaiotaomicron</td> <td>DSM 2079</td> <td>NC_004663</td> <td>Complete genome</td> <td>5.99</td> <td>17.19</td> </tr> <tr> <td>Bacteroides uniformis</td> <td>DSM 6597</td> <td>GCF_000154205</td> <td>Scaffold</td> <td>10.81</td> <td>3.10</td> </tr> <tr> <td>Bifidobacterium longum subsp. infantis</td> <td>DSM 20088</td> <td>NC_011593</td> <td>Complete genome</td> <td>29.52</td> <td>84.63</td> </tr> <tr> <td>Bifidobacterium longum subsp. longum</td> <td>DSM 20219</td> <td>GCF_900104835</td> <td>Contig</td> <td>40.19</td> <td>115.10</td> </tr> <tr> <td>Blautia wexlerae</td> <td>DSM 19850</td> <td>GCF_000484655</td> <td>Scaffold</td> <td>11.68</td> <td>0.34</td> </tr> <tr> <td>Clostridium butyricum</td> <td>DSM 10702</td> <td>GCF_000409755</td> <td>Contig</td> <td>11.21</td> <td>32.09</td> </tr> <tr> <td>Collinsella aerofaciens</td> <td>DSM 13712</td> <td>GCF_902501475</td> <td>Contig</td> <td>44.03</td> <td>12.61</td> </tr> <tr> <td>Escherichia coli</td> <td>DSM 1103</td> <td>CP009072</td> <td>Complete genome</td> <td>8.86</td> <td>25.34</td> </tr> <tr> <td>Eubacterium hallii</td> <td>DSM 3353</td> <td>GCF_000173975</td> <td>Contig</td> <td>21.79</td> <td>6.25</td> </tr> <tr> <td>Faecalibacterium prausnitzii</td> <td>DSM 17677</td> <td>NZ_CP048437</td> <td>Complete genome</td> <td>24.66</td> <td>0.72</td> </tr> <tr> <td>Lactobacillus gasseri</td> <td>DSM 20077</td> <td>NC_008530</td> <td>Complete genome</td> <td>66.00</td> <td>1.91</td> </tr> <tr> <td>Parabacteroides distasonis</td> <td>DSM 20701</td> <td>NC_009615</td> <td>Complete genome</td> <td>10.20</td> <td>29.26</td> </tr> <tr> <td>Prevotella copri</td> <td>DSM 18205</td> <td>GCF_000157935</td> <td>Scaffold</td> <td>19.23</td> <td>55.11</td> </tr> <tr> <td>Prevotella melaninogenica</td> <td>DSM 7089</td> <td>NC_014370,1 and NC_014371,1</td> <td>2 Chromosomes</td> <td>23.49</td> <td>6.73</td> </tr> <tr> <td>Roseburia hominis</td> <td>DSM 16839</td> <td>NC_015977</td> <td>Complete genome</td> <td>18.31</td> <td>5.24</td> </tr> <tr> <td>Roseburia intestinalis</td> <td>DSM 14610</td> <td>NZ_LR027880</td> <td>Complete genome</td> <td>12.04</td> <td>0.34</td> </tr> <tr> <td>Ruminococcus gauvreauii</td> <td>DSM 19829</td> <td>GCF_000425525</td> <td>Scaffold</td> <td>14.04</td> <td>0.41</td> </tr> </tbody> </table> <p>The raw sequence data of the mock communities, with an even and staggered distribution of species, is available at:</p> <ul> <li><a href="https://www.ebi.ac.uk/ena/browser/view/SRR11487941?show=reads">SRR11487941</a> - Gut-Mix-RR Illumina MiSeq sample</li> <li><a href="https://www.ebi.ac.uk/ena/browser/view/SRR11487935?show=reads">SRR11487935</a> - Gut-Mix-HiLo Illumina MiSeq sample</li> </ul>
CO1 reference sequences dataset of the sedDNA Chironomidae study by Blattner et al. 2024
<p>This dataset contains the CO1 reference sequences used to identify the sedDNA metabarcoding reads in the study entitled "Sediment core DNA-Metabarcoding and chitinous remain identification: Integrating complementary methods to characterise Chironomidae biodiversity in lake sediment archives", which was submitted to Molecular Ecology Resources.</p>
Dataset for FEM modelling of emission reference material (ERM) developed in the EMPIR project 20NRM04 MetrIAQ
<p>This dataset contains the numerical data used in the project 20NRM04 MetrIAQ for the FEM model simulations on the emission reference material (ERM) with the software COMSOL 6.2. The data published here are freely accessible with a text editor.<br>The accompanying document '<em>FEM data and file name explanations.pdf'</em> contains additional information for each data file.<br>A detailed description of the development of the FEM model can be found in the '<em>Guideline with recommendations on the customised design and dimensioning of the ERM, including the uncertainty of the numerical model developed for the prediction of the emission rate. The numerical model will (i) support the customised generation of the ERM (ii) simulate the transport processes inside the ERM and the compound release into test chamber air and (iii) enable the prediction of the emissions for each of the selected target VOC</em>' (<a href="https://doi.org/10.5281/zenodo.12749007" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12749007</a>).</p>
ddhN biomarkers NMR reference datasets
<p>To visualize the data please click on the following link: <a title="https://zenodo.nmrium.org/zenodo/v1/record/12806559" href="https://zenodo.nmrium.org/zenodo/v1/record/12806559" target="_blank" rel="noopener">https://zenodo.nmrium.org/zenodo/v1/record/12806559</a></p> <p>Reference 1D and 2D spectra from publication: <a href="https://doi.org/10.1021/acs.jproteome.3c00654" target="_blank" rel="noopener">10.1021/acs.jproteome.3c00654</a></p> <p>For further data, please also see zenodo entry: <a href="../doi/10.5281/zenodo.7906157">10.5281/zenodo.7906157</a></p>
YA Domain Dataset: Dataset of scholarly bibliographic references on YouTube videos
<p><strong>Abstract</strong></p> <p>Scholarly communication through YouTube videos has been increasing. Although Altmetric (<a href="https://altmetric.com/">https://altmetric.com/</a>) provides the dataset on such references, its coverage is unclear, and it does not contain the original external links in each video. Considering this background, we built and published a dataset of scholarly bibliographic references on YouTube videos by using YouTube Data API v3, targeting six types of domain names: "doi.org," "ncbi.nlm.nih.gov," ieeexplore.ieee.org," "link.springer.com," "onlinelibrary.wiley.com," and "sciencedirect.com." As a result, we identified approximately 480,000 references associated with Crossref DOIs among 230,000 videos published by December 31, 2023, posted on 55,000 channels. Notably, over half of these references were not covered by the Altmetric dataset, resulting in a 150% increase in the number of references when combining the dataset constructed by the proposed method with the Altmetric dataset, compared to the Altmetric dataset alone. Regarding external links, PubMed and DOI links were prominent; however, a substantial number of direct links to publisher platforms were observed. Most channels and videos contained external links to a single platform, scattered across each platform. This dataset is helpful for identifying and analyzing scholarly references on YouTube.<br>As for the original paper related to this dataset, please refer to the references section.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. The data is split into files by DOI Registration Agencies. A sample of the record is as follows:</p> <table> <tbody> <tr> <td>{<br> "channel_id": "UCEfEi-IMiB87UsxY3765P6w",<br> "video_id": "e7YmyVd4uOE",<br> "is_covered_by_altmetric_com": false,<br> "youtube_data_api_search": [<br> {<br> "query": "doi.org",<br> "uri": "http://dx.doi.org/10.1145/2807442.2814654"<br> }<br> ],<br> "doi": "10.1145/2807442.2814654",<br> "doiRA": "Crossref"<br>}</td> </tr> </tbody> </table> <ul> <li>channel_id (String) -- Channel ID of the YouTube channel that uploaded the video.</li> <li>video_id (String) -- Video ID.</li> <li>is_covered_by_altmetric_com (Boolean) -- Whether this reference is covered by altmetric.com or not.</li> <li>youtube_data_api_search (Array) <ul> <li> query (String) -- The query used in the search:list of YouTube Data API v3. (<a href="https://developers.google.com/youtube/v3/docs/search/list?hl=en">https://developers.google.com/youtube/v3/docs/search/list?hl=en</a>)</li> <li> uri (String)-- The original external links written in the description text or video title in each video.</li> </ul> </li> <li>doi (String) -- DOI corresponding to the bibliographic reference in the video.</li> <li>doiRA (String) -- DOI registration agency for the DOI. We obtained this data using the WhichRA? API (<a href="https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra">https://www.doi.org/the-identifier/resources/factsheets/doi-resolution-documentation#4-which-ra</a>).</li> </ul> <p>We note that the altmetric dataset obtained from Altmetric Explorer in this study is not included in this dataset.</p> <p><strong>References</strong></p> <ul> <li>Kikkawa, Jiro; Takaku, Masao; Yoshikane, Fuyuki: "Enhancing Identification of Scholarly Reference on YouTube: Method Development and Analysis of External Link Characteristics", <em>Proceedings of the 28th International Conference on Theory and Practice of Digital Libraries (<a href="https://tpdl2024.nuk.si/">TPDL 2024</a>)</em>, Ljubljana, Slovenia, Lecture Notes in Computer Science (LNCS), Vol.15178, 2024.09. (in press).</li> </ul> <p><strong>Fundings</strong></p> <p>JSPS KAKENHI Grant Numbers <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K18147/">JP22K18147</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-23K11761">JP23K11761</a>, and <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-24K15652">JP24K15652</a>.</p>
Dataset records and references extracted from GBIF used for wild boar distribution modelling
<p>These are the datasets containing wild boar records downloaded from Global Biodiversity Information Facility (GBIF) used to fit presence-only and presence-background models. It indicates the data key, publishing organization key, licenses and references.</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.