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3,788 results for “indices”
On the relation between technical debt indicators and quality criteria in Stack Overflow discussions
<p>This is the replication package for the paper "On the relation between technical debt indicators and quality criteria in Stack Overflow discussions". The paper was published on the Technical Research Track of the Bralizian Symposium on Software Engineering.This is the replication package for the paper "On the relation between technical debt indicators and quality criteria in Stack Overflow discussions". The paper was published on the Technical Research Track of the Bralizian Symposium on Software Engineering.</p>
Indicative distribution map for Ecosystem Functional Group T1.2 Tropical/Subtropical dry forests and thickets
<p>This archive contains indicative distribution maps and profiles for <strong>T1.2 Tropical/Subtropical dry forests and thickets</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
Dataset for "Structural and molecular indices in common carp (Cyprinus carpio L.) fed n-3 PUFA enriched diet. Food and Chemical Toxicology, 151, 112146."
<p>Dataset for "<strong><em>Eljasik P, Panicz R, Sobczak M, Sadowski J, Tórz A, Barbosa V, Marques A, Dias J. (2021). </em></strong><em>Structural and molecular indices in common carp (Cyprinus carpio L.) fed n-3 PUFA enriched diet. </em><strong><em>Food and Chemical Toxicology, 151, 112146. </em></strong><em>DOI: <a href="https://www.sciencedirect.com/science/article/abs/pii/S0278691521001794?via%3Dihub">doi.org/10.1016/j.fct.2021.112146</a>"</em></p>
Ecoacoustic Study Design Variation: Impact on Acoustic Indices and AudioSet Fingerprints
<b>Description: </b><p>Acoustic Index and AudioSet Fingerprint data quantified derived from audio recorded between the 26th of February and the 2nd March 2019.<br><br>The original raw audio was compressed, shortened and temporally subset to replicate common inconsistencies in ecoacoustic studies. This data frame show how this experimental variation affects how soundscapes are quantified by Analytical Indices and the AudioSet Fingerprint</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="http://127.0.0.1:8000/projects/project_view/200"><b>3D Acoustics for Audio Monitoring of Rainforest Biodiversity </b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (NERC QMEE CDT Studentship, NE/P012345/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="http://127.0.0.1:8000/datasets/xml_metadata?id=5153193">here</a></p><p><b>Files: </b>This consists of 1 file: Ecoacoustic_Method_Comparison.xlsx</p><p><b>Ecoacoustic_Method_Comparison.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Analytical index values under differing experimental conditions</b> (described in worksheet Analytical_Index_Data)</p><p>Description: This dataset contains all the analytical indices derived from audio under different experimenatal conditions</p><p>Number of fields: 16</p><p>Number of data rows: 87211</p><p>Fields: </p><ul><li><b>id.no</b>: Sample ID (Field type: id)</li><li><b>file.size</b>: File size as a % of uncompressed (Field type: numeric)</li><li><b>compression</b>: Compression level (Mp3) (Field type: ordered categorical)</li><li><b>frame.size</b>: Frame size (recording length) (Field type: ordered categorical)</li><li><b>site</b>: Field Site Location (Field type: location)</li><li><b>req.freq</b>: Recording Frequency (Field type: numeric)</li><li><b>date</b>: Date (Field type: date)</li><li><b>time</b>: Time ID (including subsamples) (Field type: id)</li><li><b>max.freq</b>: Nyquist (Maximum) frequency (Field type: numeric)</li><li><b>ACI</b>: Acoustic Complexity Index (Field type: numeric)</li><li><b>ADI</b>: Acoustic Diversity Index (Field type: numeric)</li><li><b>Aeev</b>: Acoustic Eveness (Field type: numeric)</li><li><b>Bio</b>: Biodiversity Index (Field type: numeric)</li><li><b>H</b>: Acoustic Entropy (Field type: numeric)</li><li><b>M</b>: Median of Acoustic Envelope (Field type: numeric)</li><li><b>NDSI</b>: Normalised Difference Soundscape Index (Field type: numeric)</li></ul></li><li><p><b>AudioSet Fingerprint values under differing experimental conditions</b> (described in worksheet AudioSet_Fingerprint_Data)</p><p>Description: This dataset contains all the audioset fingerprint values derived from audio under different experimenatal conditions</p><p>Number of fields: 137</p><p>Number of data rows: 87329</p><p>Fields: </p><ul><li><b>id.no</b>: Sample ID (Field type: id)</li><li><b>file.size</b>: File size as a % of uncompressed (Field type: numeric)</li><li><b>frame.size</b>: Frame size (recording length) (Field type: ordered categorical)</li><li><b>compression</b>: Compression level (Mp3) (Field type: ordered categorical)</li><li><b>site</b>: Field Site Location (Field type: location)</li><li><b>req.freq</b>: Recording Frequency (Field type: numeric)</li><li><b>date</b>: Date (Field type: date)</li><li><b>time</b>: Time ID (including subsamples) (Field type: id)</li><li><b>max.freq</b>: Nyquist (Maximum) frequency (Field type: numeric)</li><li><b>feat1</b>: Feature 1 (Field type: numeric)</li><li><b>feat2</b>: Feature 2 (Field type: numeric)</li><li><b>feat3</b>: Feature 3 (Field type: numeric)</li><li><b>feat4</b>: Feature 4 (Field type: numeric)</li><li><b>feat5</b>: Feature 5 (Field type: numeric)</li><li><b>feat6</b>: Feature 6 (Field type: numeric)</li><li><b>feat7</b>: Feature 7 (Field type: numeric)</li><li><b>feat8</b>: Feature 8 (Field type: numeric)</li><li><b>feat9</b>: Feature 9 (Field type: numeric)</li><li><b>feat10</b>: Feature 10 (Field type: numeric)</li><li><b>feat11</b>: Feature 11 (Field type: numeric)</li><li><b>feat12</b>: Feature 12 (Field type: numeric)</li><li><b>feat13</b>: Feature 13 (Field type: numeric)</li><li><b>feat14</b>: Feature 14 (Field type: numeric)</li><li><b>feat15</b>: Feature 15 (Field type: numeric)</li><li><b>feat16</b>: Feature 16 (Field type: numeric)</li><li><b>feat17</b>: Feature 17 (Field type: numeric)</li><li><b>feat18</b>: Feature 18 (Field type: numeric)</li><li><b>feat19</b>: Feature 19 (Field type: numeric)</li><li><b>feat20</b>: Feature 20 (Field type: numeric)</li><li><b>feat21</b>: Feature 21 (Field type: numeric)</li><li><b>feat22</b>: Feature 22 (Field type: numeric)</li><li><b>feat23</b>: Feature 23 (Field type: numeric)</li><li><b>feat24</b>: Feature 24 (Field type: numeric)</li><li><b>feat25</b>: Feature 25 (Field type: numeric)</li><li><b>feat26</b>: Feature 26 (Field type: numeric)</li><li><b>feat27</b>: Feature 27 (Field type: numeric)</li><li><b>feat28</b>: Feature 28 (Field type: numeric)</li><li><b>feat29</b>: Feature 29 (Field type: numeric)</li><li><b>feat30</b>: Feature 30 (Field type: numeric)</li><li><b>feat31</b>: Feature 31 (Field type: numeric)</li><li><b>feat32</b>: Feature 32 (Field type: numeric)</li><li><b>feat33</b>: Feature 33 (Field type: numeric)</li><li><b>feat34</b>: Feature 34 (Field type: numeric)</li><li><b>feat35</b>: Feature 35 (Field type: numeric)</li><li><b>feat36</b>: Feature 36 (Field type: numeric)</li><li><b>feat37</b>: Feature 37 (Field type: numeric)</li><li><b>feat38</b>: Feature 38 (Field type: numeric)</li><li><b>feat39</b>: Feature 39 (Field type: numeric)</li><li><b>feat40</b>: Feature 40 (Field type: numeric)</li><li><b>feat41</b>: Feature 41 (Field type: numeric)</li><li><b>feat42</b>: Feature 42 (Field type: numeric)</li><li><b>feat43</b>: Feature 43 (Field type: numeric)</li><li><b>feat44</b>: Feature 44 (Field type: numeric)</li><li><b>feat45</b>: Feature 45 (Field type: numeric)</li><li><b>feat46</b>: Feature 46 (Field type: numeric)</li><li><b>feat47</b>: Feature 47 (Field type: numeric)</li><li><b>feat48</b>: Feature 48 (Field type: numeric)</li><li><b>feat49</b>: Feature 49 (Field type: numeric)</li><li><b>feat50</b>: Feature 50 (Field type: numeric)</li><li><b>feat51</b>: Feature 51 (Field type: numeric)</li><li><b>feat52</b>: Feature 52 (Field type: numeric)</li><li><b>feat53</b>: Feature 53 (Field type: numeric)</li><li><b>feat54</b>: Feature 54 (Field type: numeric)</li><li><b>feat55</b>: Feature 55 (Field type: numeric)</li><li><b>feat56</b>: Feature 56 (Field type: numeric)</li><li><b>feat57</b>: Feature 57 (Field type: numeric)</li><li><b>feat58</b>: Feature 58 (Field type: numeric)</li><li><b>feat59</b>: Feature 59 (Field type: numeric)</li><li><b>feat60</b>: Feature 60 (Field type: numeric)</li><li><b>feat61</b>: Feature 61 (Field type: numeric)</li><li><b>feat62</b>: Feature 62 (Field type: numeric)</li><li><b>feat63</b>: Feature 63 (Field type: numeric)</li><li><b>feat64</b>: Feature 64 (Field type: numeric)</li><li><b>feat65</b>: Feature 65 (Field type: numeric)</li><li><b>feat66</b>: Feature 66 (Field type: numeric)</li><li><b>feat67</b>: Feature 67 (Field type: numeric)</li><li><b>feat68</b>: Feature 68 (Field type: numeric)</li><li><b>feat69</b>: Feature 69 (Field type: numeric)</li><li><b>feat70</b>: Feature 70 (Field type: numeric)</li><li><b>feat71</b>: Feature 71 (Field type: numeric)</li><li><b>feat72</b>: Feature 72 (Field type: numeric)</li><li><b>feat73</b>: Feature 73 (Field type: numeric)</li><li><b>feat74</b>: Feature 74 (Field type: numeric)</li><li><b>feat75</b>: Feature 75 (Field type: numeric)</li><li><b>feat76</b>: Feature 76 (Field type: numeric)</li><li><b>feat77</b>: Feature 77 (Field type: numeric)</li><li><b>feat78</b>: Feature 78 (Field type: numeric)</li><li><b>feat79</b>: Feature 79 (Field type: numeric)</li><li><b>feat80</b>: Feature 80 (Field type: numeric)</li><li><b>feat81</b>: Feature 81 (Field type: numeric)</li><li><b>feat82</b>: Feature 82 (Field type: numeric)</li><li><b>feat83</b>: Feature 83 (Field type: numeric)</li><li><b>feat84</b>: Feature 84 (Field type: numeric)</li><li><b>feat85</b>: Feature 85 (Field type: numeric)</li><li><b>feat86</b>: Feature 86 (Field type: numeric)</li><li><b>feat87</b>: Feature 87 (Field type: numeric)</li><li><b>feat88</b>: Feature 88 (Field type: numeric)</li><li><b>feat89</b>: Feature 89 (Field type: numeric)</li><li><b>feat90</b>: Feature 90 (Field type: numeric)</li><li><b>feat91</b>: Feature 91 (Field type: numeric)</li><li><b>feat92</b>: Feature 92 (Field type: numeric)</li><li><b>feat93</b>: Feature 93 (Field type: numeric)</li><li><b>feat94</b>: Feature 94 (Field type: numeric)</li><li><b>feat95</b>: Feature 95 (Field type: numeric)</li><li><b>feat96</b>: Feature 96 (Field type: numeric)</li><li><b>feat97</b>: Feature 97 (Field type: numeric)</li><li><b>feat98</b>: Feature 98 (Field type: numeric)</li><li><b>feat99</b>: Feature 99 (Field type: numeric)</li><li><b>feat100</b>: Feature 100 (Field type: numeric)</li><li><b>feat101</b>: Feature 101 (Field type: numeric)</li><li><b>feat102</b>: Feature 102 (Field type: numeric)</li><li><b>feat103</b>: Feature 103 (Field type: numeric)</li><li><b>feat104</b>: Feature 104 (Field type: numeric)</li><li><b>feat105</b>: Feature 105 (Field type: numeric)</li><li><b>feat106</b>: Feature 106 (Field type: numeric)</li><li><b>feat107</b>: Feature 107 (Field type: numeric)</li><li><b>feat108</b>: Feature 108 (Field type: numeric)</li><li><b>feat109</b>: Feature 109 (Field type: numeric)</li><li><b>feat110</b>: Feature 110 (Field type: numeric)</li><li><b>feat111</b>: Feature 111 (Field type: numeric)</li><li><b>feat112</b>: Feature 112 (Field type: numeric)</li><li><b>feat113</b>: Feature 113 (Field type: numeric)</li><li><b>feat114</b>: Feature 114 (Field type: numeric)</li><li><b>feat115</b>: Feature 115 (Field type: numeric)</li><li><b>feat116</b>: Feature 116 (Field type: numeric)</li><li><b>feat117</b>: Feature 117 (Field type: numeric)</li><li><b>feat118</b>: Feature 118 (Field type: numeric)</li><li><b>feat119</b>: Feature 119 (Field type: numeric)</li><li><b>feat120</b>: Feature 120 (Field type: numeric)</li><li><b>feat121</b>: Feature 121 (Field type: numeric)</li><li><b>feat122</b>: Feature 122 (Field type: numeric)</li><li><b>feat123</b>: Feature 123 (Field type: numeric)</li><li><b>feat124</b>: Feature 124 (Field type: numeric)</li><li><b>feat125</b>: Feature 125 (Field type: numeric)</li><li><b>feat126</b>: Feature 126 (Field type: numeric)</li><li><b>feat127</b>: Feature 127 (Field type: numeric)</li><li><b>feat128</b>: Feature 128 (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2019-02-26 to 2019-06-02</p><p><b>Latitudinal extent: </b>4.6644 to 4.7027</p><p><b>Longitudinal extent: </b>117.5351 to 117.5914</p>
Is reputation on Stack Overflow always a good indicator for users' expertise? No!
<p>Dataset for RQ1 and RQ2. </p>
Daily calculation of the 30-day Selyaninov hydrothermal indicator (Kurgan, 1894-2020).
<p>This dataset presents the results of the "daily" calculation of the 30-day Selyaninov hydrothermal indicator for the Kurgan region, Russia, from 1894 till 2020 years.</p> <p>From Wikipedia: Selyaninov hydrothermal moisture coefficient is a characteristic of the level of moisture supply of the territory. Introduced by the Russian climatologist G. T. Selyaninov (1887-1966). It is widely used in agronomy for a general assessment of the climate and the allocation of zones of different levels of moisture availability in order to determine the feasibility of growing certain crops.</p> <p>[in Rus: https://ru.wikipedia.org/wiki/%D0%93%D0%B8%D0%B4%D1%80%D0%BE%D1%82%D0%B5%D1%80%D0%BC%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B9_%D0%BA%D0%BE%D1%8D%D1%84%D1%84%D0%B8%D1%86%D0%B8%D0%B5%D0%BD%D1%82_%D1%83%D0%B2%D0%BB%D0%B0%D0%B6%D0%BD%D0%B5%D0%BD%D0%B8%D1%8F_%D0%A1%D0%B5%D0%BB%D1%8F%D0%BD%D0%B8%D0%BD%D0%BE%D0%B2%D0%B0]</p> <p>Датасет представляет результаты "ежедневного" расчета 30-ти дневного гидротермического показателя Селянинова (г.Курган, 1894-2020).</p> <p>Из Википедии: Гидротермический коэффициент увлажнения Селянинова (ГТК) — характеристика уровня влагообеспеченности территории. Введён российским климатологом Г.Т. Селяниновым (1887—1966). Широко используется в агрономии для общей оценки климата и выделения зон различного уровня влагообеспеченности с целью определения целесообразности выращивания тех или иных сельхозкультур.</p>
Tables and Appendix indicate population density and distribution of Cheer Pheasant (Catreus wallichii) in different localities of AJ&K
<p><strong>Table 5 </strong>Population density (per sq. km.) of Cheer Pheasant <em>(Catreus wallichii) </em>in different localities of AJ&K.</p> <p><strong>Table 6 </strong>Population density (per sq. km.) of Cheer Pheasant <em>(Catreus wallichii) </em>in different sub-localities of AJ&K.</p> <p><strong>Table 7 </strong>Population density (per sq. km.) of Cheer Pheasant <em>(Catreus wallichii) </em>across the months in AJ&K.</p> <p><strong>Appendix-I </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichi</em>) in Jhelum Velley, AJ&K.</p> <p><strong>Appendix-II </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichii</em>) at Machiara National Park, Muzaffarabad AJ&K.</p> <p><strong>Appendix-III </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichii</em>) at Phalla Game Reserve, Haveli, AJ&K.</p> <p><strong>Appendix-IV </strong>Distribution and Population density of Cheer Pheasant (<em>Catreus wallichii</em>) at Nar Sher Ali Khan, Bagh, Azad Jammu & Kashmir.</p> <p> </p> <p> </p>
FIG. 6. – Phylogenetic scenario indicating possible relationships h in Contribution to the systematics and phylogeny of Bouvrain, 1982 (Mammalia, Bovidae)
FIG. 6. – Phylogenetic scenario indicating possible relationships h., houtumschindleri.
FIGURE 3 in Typhlodromalus Aripo De Leon (Acari: Phytoseiidae) Development And Reproduction On Major Cassava Pests At Different Temperatures And Humidities: An Indication Of Enhanced Mite Resilience
FIGURE 3: Mean daily fecundity of Typhlodromalu aripo female on cassava green mite Mononychellus progresivus in absence and presence of B. tabaci crawlers.
FIGURE 2 in Typhlodromalus Aripo De Leon (Acari: Phytoseiidae) Development And Reproduction On Major Cassava Pests At Different Temperatures And Humidities: An Indication Of Enhanced Mite Resilience
FIGURE 2: Mean daily consumption rate by Typhlodromalus aripo female of cassava green mite in absence and presence of Bemisia tabaci crawlers.
FIGURE 1 in Typhlodromalus Aripo De Leon (Acari: Phytoseiidae) Development And Reproduction On Major Cassava Pests At Different Temperatures And Humidities: An Indication Of Enhanced Mite Resilience
FIGURE 1: Leaf disk unit showing dimensions (0.4 cm-depth × 1.5 cm-diameter) of feed area of Typhlodromalus aripo life stage cohorts on cassava green mite (eggs and active), Bemisia tabaci and Phenacoccus manihoti.
Figure 1 in The European Tertiary Neritiliidae (Mollusca, Gastropoda, Neritopsina): indicators of tropical submarine cave environments and freshwater faunas
Figure 1. Location of the fossil localities (★).
Figure 10 in Bone surface texture as an ontogenetic indicator in long bones of the Canada goose Branta canadensis (Anseriformes: Anatidae)
Figure 10. The relationship between texture type and date of death.
Figure 8 in Bone surface texture as an ontogenetic indicator in long bones of the Canada goose Branta canadensis (Anseriformes: Anatidae)
Figure 8. Relationships between texture type and bone length and percentage adult size.
Inferred ancestry of scytonemin biosynthesis proteins in cyanobacteria indicates a response to Paleoproterozoic oxygenation
<p>Protection from radiation damage is an important adaptation for phototrophic microbes. In the case of cyanobacteria, surface, shallow water, and peritidal environments are especially exposed to long- wavelength ultraviolet (UVA) radiation. Several groups of cyanobacteria within these environments are protected from UVA damage by the production of the pigment scytonemin. Paleontological evidence of cyanobacteria in UVA-exposed environments from the Proterozoic, and possibly as early as the Archaean, suggests a long evolutionary history of radiation protection within this group. We show that phylogenetic analyses of enzymes in the scytonemin biosynthesis pathway support this hypothesis, and reveal a deep history of vertical inheritance of this pathway within extant cyanobacterial diversity. Referencing this phylogeny to cyanobacterial molecular clocks suggests that scytonemin production likely appeared during the early Proterozoic, soon after the Great Oxygenation Event. This timing is consistent with an adaptive scenario for the evolution of scytonemin production, wherein the threat of UVA-generated reactive oxygen species becomes significantly greater once molecular oxygen was more pervasive across photosynthetic environments.</p>
(re)Use Indications of High Energy Physics related Research Data and Software in Zenodo
<p>This dataset contains High Energy Physics related research data and software (re)use indications (formal citations, informal mentions) in scholarly works. All research data and software resources were identified and extracted from Zenodo. The (re)use indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.</p> <p>The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Quality and Trackability of Author Indicated Software Citations (NEST Case Study)
<p>This dataset contains a randomly selected minimal sample of 471 NEST software citations, which were provided by authors and published on the NEST software page as a publication list. The software citations were analyzed on their quality and trackability.</p> <p>The data was collected in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Digital publishing of Indic manuscripts and inscriptions using the READ Workbench corpus development, research and publishing framework
<p>Paper presented on Friday 11 June 2021 at the Digital Medievalist Global Symposium <em>The past, present, and future of Digital Medieval Studies</em> for the Asia & Oceania Panel, in the session Reading Indic and Japanese scripts.</p> <p> </p>
Pollinator-mediated indirect effects on plant fecundity revealed by network indices
<p>Indirect effects arise when one species influences how another species interacts with a third. Pollinator-mediated indirect effects are widespread in many plant communities and are often not restricted to plant species pairs. An analytical framework does not exist yet that allows for the evaluation of indirect effects through shared pollinators in a community context, as well as their consequences for plant fitness. We used network indices describing pollinator sharing to assess the extent to which plant species affect and are affected by others in a pollination network from a species-rich dune community. For 23 plant species, we explore how these indices relate to plant fecundity (seeds/flower) over two years. We further linked plant traits and indices to uncover functional aspects of pollinator-mediated indirect interactions. Species frequently visited by shared pollinators showed higher fecundity and exhibited traits that increase pollinator attraction and generalization. Conversely, species whose shared pollinators frequently visited other plants had lower fecundity and had more specialized traits. Thus, pollinator sharing benefited some species while others suffered reproductive disadvantages, consistent with competition. The framework developed here uses network tools to advance our understanding of how pollinator-mediated indirect interactions influence a species' relative reproductive success at the community-level.</p>
Evaluating computational approaches for comparison of protein expression across cancer indications
<p>########################################################################################################<br># CPTAC PAN cancer imputed and normalized data <br>########################################################################################################<br># R Code snippet<br>iBAQ = readRDS("/your path/iBAQ.rds")<br>table(iBAQ$normalized)<br># CN No QN QN riBAQ Smooth QN <br># 17435640 17435640 17435640 17435640 17435640 <br>library(dplyr)<br>full.qn <- iBAQ %>% filter(normalized=="QN")<br>OV <- full.qn %>% filter(Indication=="OV")%>% filter(Tissue=="Tumor")<br>OV_4genes <- OV[OV$Gene%in% c("CDH6", "FOLR1", "ERBB2", "TACSTD2"),]</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.