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397 results for “Study design”
SIF datasets (50 m) and demos of network structure designs on the study of the STP-SIF issue
<p>The SIF datasets (i.e., SIF2019 and SIF2020) accompany the paper "Regional-Scale Cotton Yield Forecast via Data-Driven Spatio-Temporal Prediction (STP) of Solar-Induced Chlorophyll Fluorescence (SIF)" that was published in <a href="https://www.sciencedirect.com/science/article/abs/pii/S0034425723004121">Remote Sensing of Environment</a> on October 20, 2023. They have a spatial resolution of about 50 m and a monthly temporal resolution. Each of them has seven bands, corresponding to April to October. Please refer to our previous work, "Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network", which was published in <a href="https://www.sciencedirect.com/science/article/pii/S0168169922005737">Computers and Electronics in Agriculture</a> on August 14, 2022, for the development and detailed description.</p><p>The geographic reference (ESPG: 4326 (WGS_1984)) is the same for the two dataset, conforming to that in the geotiff file.</p><p><strong>Citation</strong>:</p><p>[1] Kang, X., Huang, C., Zhang, L., Wang, H., Zhang, Z., Lv, X., 2023. Regional-scale cotton yield forecast via data-driven spatio-temporal prediction (STP) of solar-induced chlorophyll fluorescence (SIF). Remote Sensing of Environment 299, 113861. doi:10.1016/j.rse.2023.113861</p><p>[2] Kang, X., Huang, C., Zhang, L., Zhang, Z., Lv, X., 2022. Downscaling solar-induced chlorophyll fluorescence for field-scale cotton yield estimation by a two-step convolutional neural network. Computers and Electronics in Agriculture 201, 107260. doi:10.1016/j.compag.2022.107260</p><p>[3] Kang, X., Huang, C., Chen, J.M., Lv, X., Wang, J., Zhong, T., Wang, H., Fan, X., Ma, Y., Yi, X., Zhang, Z., Zhang, L., Tong, Q., 2023. The 10-m cotton maps in Xinjiang, China during 2018-2021. Sci Data 10, 688. doi:10.1038/s41597-023-02584-3</p><p>[4] Lang, P., Zhang, L., Huang, C., Chen, J., Kang, X., Zhang, Z., Tong, Q., 2023. Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province. Frontiers in Plant Science 13, 1048479. doi:10.3389/fpls.2022.1048479</p>
Study Data: Obtaining Semi-Formal Models from Qualitative Data: From Interviews into BPMN Models in User-Centered Design Processes
<p>This dataset (Data.zip) contains the raw data of a user study on the investigation of transforming think aloud interviews into BPMN models. All information on how to use the data are provide in the SPSS files and as a readme file. This transformation is executed following a manual additionally provided in Documents.zip. For the training phase, a website was used provided in Website.zip including Screenshots for simpler re-use. Further information are also included as readme file in the zip container.</p> <p>Main research question answered is in how far the manual reduces interpretation and variance in the created models. </p>
Evaluating alternative study designs for optimal sampling of species' climatic niches
Ecologists have traditionally studied intraspecific variation by sampling species across their geographic ranges. However, whether this classic approach produces samples that accurately represent species' climatic niches is largely unknown. Alternative, niche-based study designs using species' climatic niches to inform sampling locations should more efficiently and completely capture the breadth of the niche, but the magnitude of this difference and how it may vary is unclear. Here we use conifers as a model system to explore these issues and reach specific recommendations for future sampling designs. Using an independent dataset of high-quality species' occurrences, we first show that recent publications examining variation across geographic space do a poor job of capturing the full breadth of species' niches, such that on average, only 22% of species' niche space was sampled. This was also true of a large compiled database, the International Tree-Ring Data Bank (ITRDB), which yielded average niche coverage of only 45%. Finally, we simulated common sampling designs (i.e., random points, grids, and transects) in both geographic and niche-based sampling frameworks. Using two sampling metrics, niche coverage and niche undersampling, we measured how completely and evenly these simulated studies characterized the niches of 64 North American conifers. Niche-based sampling better represented species' niches than geographic sampling, with the magnitude of this difference depending on study design and sample size. Niche-based gridded study designs achieved the most complete sampling at all but the smallest sample sizes, covering ~15-25% more of a species' niche than similar designs implemented geographically. With fewer than 10 samples, however, all study designs performed poorly, and niche-based transects achieved slightly higher niche coverage. Consequently, when more than a handful of samples are collected, we recommend that studies seeking to characterize variation across a species' niche consider using a gridded study design implemented in a niche-based sampling framework.
Non-native species drive biotic homogenization, but it depends on the realm, beta diversity facet and study design: A meta-analytic systematic review
<p>While reducing the species richness of invaded communities is a well-known consequence of biological invasions, non-native species can also reduce variability between communities over time (i.e., beta diversity) in a process known as biotic homogenization. Although biotic homogenization due to non-native species is a common topic of theoretical reviews, we believe no global meta-analysis on the effect of non-native species on beta diversity has been carried out yet. Here, we systematically show that non-native species drive biotic homogenization, but it depends on the realm, beta diversity facet and study design. Biotic homogenization was more intense in marine and freshwater ecosystems than in terrestrial ecosystems. We also found that non-native species reduced both taxonomic and phylogenetic beta diversity, but not the functional beta diversity. Finally, we observed more intense effects using "before vs. after invasion" followed by "uninvaded vs. invaded sites" while the effect size of studies using "communities associated with native vs. non-native species" did not differ from zero. Our findings highlight that non-native species contribute to biotic homogenization as a prevalent pattern in communities worldwide, and that biodiversity conservation strategies should go beyond investigating the reduction in the number of species by also taking into account beta diversity in its multiple facets.</p>
Data generated for study of simultaneous design of wind turbines and cable layout in offshore wind
<p>This set of files contains the results of the models proposed in the manuscript: "Pérez-Rúa, J.-A. and Cutululis, N. A.: A Framework for Simultaneous Design of Wind Turbines and Cable Layout in Offshore Wind, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2021-47, in review, 2021."</p>
Replication package: 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study
<p>Replication package | 40 Years of Designing Code Comprehension Experiments: A Systematic Mapping Study</p>
Anticipated versus Actual Effects of Platform Design Change: A Case Study of Twitter's Character Limit
<p>The design of online platforms is both critically important and challenging, as any changes may lead to unintended consequences, and it can be hard to predict how users will react. Here we conduct a case study of a particularly important real-world platform design change: Twitter's decision to double the character limit from 140 to 280 characters to soothe users' need to "cram'' or "squeeze'' their tweets, informed by modeling of historical user behavior.<br> In our analysis, we contrast Twitter's anticipated pre-intervention predictions about user behavior with actual post-intervention user behavior: Did the platform design change lead to the intended user behavior shifts, or did a gap between anticipated and actual behavior emerge?<br> Did different user groups react differently?<br> We find that even though users do not "cram'' as much under 280 characters as they used to under 140 characters, emergent "cramming'' at the new limit seems to not have been taken into account when designing the platform change. Furthermore, investigating textual features, we find that, although post-intervention ``crammed'' tweets are longer, their syntactic and semantic characteristics remain similar and indicative of "squeezing''. Applying the same approach as Twitter policy-makers, we create updated counterfactual estimates and find that the character limit would need to be increased further to reduce cramming that re-emerged at the new limit.<br> We contribute to the rich literature studying online user behavior with an empirical study that reveals a dynamic interaction between platform design and user behavior, with immediate policy and practical implications for the design of socio-technical systems. </p>
DESIGN, SYNTHESIS AND STUDY OF BIOLOGICAL ACTIVITY OF PEPTIDES WITH NEUROPROTECTIVE PROPERTIES
<p>The main purpose of this project is to search for novel compounds that will protect neurons against oxidative stress (6-hydroxydopamine, 6-OHDA) and glucocorticoids (corticosterone, CORT) in a human neuroblastoma cell line (SH-SY5Y) and elucidate the mechanism by which attenuated the physiological changes induced by these neurotoxins. We will explore whether this protective role of tested compounds will be involved also in the regulation of apoptosis, maintaining oxidoreductive balance, and stabilization of mitochondrial membrane potential. Furthermore, the involvement of the BDNF/TrkB-ERK-CREB/mTOR signalling pathway in neuronal survival will be examined.</p>
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Reinforcement Learning
<p>Well-trained models and generation results for reinforcement learning part of <a href="https://github.com/ytl0410/Polymer-Generative-Models-Benchmark">ytl0410/Polymer-Generative-Models-Benchmark: Well-trained models and generative outcomes for the paper "Benchmarking Study of Deep Generative Models for Inverse Polymer Design" (github.com)</a></p>
R code and simulation output for Efford, M. G. & Boulanger, J. 2019. Fast evaluation of study designs for spatially explicit capture-recapture. Methods in Ecology and Evolution
<p>R code and simulation output for Efford, M. G. & Boulanger,<br> J. 2019. Fast evaluation of study designs for spatially explicit<br> capture-recapture. Methods in Ecology and Evolution In press.</p> <p>R code draws on previously published R packages 'secr' and 'secrdesign' available from CRAN:</p> <p><a href="https://CRAN.R-project.org/package=secr">https://CRAN.R-project.org/package=secr</a></p> <p><a href="https://CRAN.R-project.org/package=secrdesign">https://CRAN.R-project.org/package=secrdesign</a></p>
Which RESTful API Design Rules are Important and How Do They Improve Software Quality? A Delphi Study with Industry Experts
<p>The dataset of a Delphi study with 8 industry experts who reached consensus on the perceived importance and positive software quality impact of 82 RESTful API design rules from the catalogue by Mark Massé ("REST API Design Rulebook", O’Reilly Media, 2011). The replication package contains:</p> <ul> <li><strong>rules.csv:</strong> the final consensus results for the 82 rules in CSV format</li> <li><strong>rule-importance.xlsx</strong>: the detailed results and analysis for rule importance as an Excel spreadsheet</li> <li><strong>rule-sw-quality-impact.xlsx</strong>: the detailed results and analysis for rule impact on software quality as an Excel spreadsheet</li> </ul> <p>In this version, we updated the final numbers for the software quality mapping with the results from the synchronous meeting.</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>
Ecoacoustic Study Design Variation: Raw Files
<b>Description: </b><p>Metadata of the raw audio files used to investigate how variation in study design affects how indices are quantified. (All audio recorded at SAFE project) </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/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="https://www.safeproject.net/datasets/xml_metadata?id=5159914">here</a></p><p><b>Files: </b>This dataset consists of 4 files: Audio_Data_Info.xlsx, Matrix.7z, Logged.7z, Primary.7z</p><p><b>Audio_Data_Info.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Metadata</b> (described in worksheet Metadata)</p><p>Description: This worksheet describes the properties of all the acoustic files linked to this DOI</p><p>Number of fields: 9</p><p>Number of data rows: 640</p><p>Fields: </p><ul><li><b>root.file.name</b>: External File Name (Field type: file)</li><li><b>file.name</b>: Root Audio ID (Field type: id)</li><li><b>format</b>: Description of file type (Field type: comments)</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 (VJR is in primary forest, E is in logged forest and D is in Matrix) (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 of Recording (Field type: time)</li></ul></li></ol><p><b>Matrix.7z</b></p><p>Description: 7zip file containing 223 .flac audio recordings</p><p><b>Logged.7z</b></p><p>Description: 7zip file containing 212 .flac audio recordings</p><p><b>Primary.7z</b></p><p>Description: 7zip file containing 205 .flac audio recordings</p><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>
Data from: Multispecies site occupancy modeling and study design for spatially replicated environmental DNA metabarcoding
<p>Although environmental DNA (eDNA) metabarcoding has become widely applied to gauge ecosystems in a noninvasive and cost-efficient manner, false negatives can occur due to various factors in its inherent multistage workflow. It is therefore essential to deal with this kind of species detection errors in eDNA metabarcoding to achieve accurate assessment of species distribution and diversity. To address this issue, we proposed a variant of the multispecies site occupancy model for eDNA metabarcoding studies and applied it to an eDNA metabarcoding dataset of freshwater fish communities collected in the Kasumigaura watershed in Japan.</p> <ul> </ul>
Estimated densities of activity from: Characterising diel activity patterns to design conservation measures: Case study of European bat species
<p>Estimated densities of activity calculated in <em>"Characterising diel activity patterns to design conservation measures: case study of European bat species"</em> (see Materials and Methods for more information on these calculations).</p> <p>The files named "<em>densitiyAllYear</em>" correspond to the estimated densities based on our entire dataset.</p> <p>The files named "<em>densitiySpring</em>" correspond to the estimated densities based on a subset of our dataset comprising only monitoring carried out between 1 March and 21 June.</p> <p>The files named "<em>densitiySummer</em>" correspond to the estimated densities based on a subset of our dataset comprising only monitoring carried out between 22 June and 21 August.</p> <p>The files named "<em>densitiyAutumn</em>" correspond to the estimated densities based on a subset of our dataset comprising only monitoring carried out between 22 August and 31 October.</p> <p>The first column of each file (<em>"PercentageNight"</em>) corresponds to the percentage of the night elapsed (0 % = sunset time, 100 % = sunrise time), the second to the estimated activity density (<em>"Density"</em>).</p>
Design Of A Condition Monitoring System (CMS) For A Gas Compressor Using Shell Bonny Terminal As A Case Study
<p><strong>Subcontracted</strong> <strong>Researcher</strong> <strong>&</strong> <strong>Field</strong> <strong>Electrical</strong> <strong>and</strong> <strong>Electronics</strong> <strong>Engineer</strong>, <em>Shell</em> <em>Bonny</em> <em>Terminal, Nigeria</em>— 2013</p> <p>I served as a Hooks Electric subcontracted researcher and field electrical and electronics engineer at Shell Bonny Terminal, spearheading a groundbreaking project to design and construct a state-of-the-art software condition monitoring system (CMS) for a gas compressor. In addition to my technical responsibilities, I also took charge of integrating this innovative design into the existing SCADA (Supervisory Control and Data Acquisition) infrastructure of the plant.</p> <p>Working collaboratively with a team of four talented engineers, I successfully managed and coordinated their efforts throughout the project. Together, we leveraged our collective expertise to develop and implement a fully functional software condition monitoring system. This collaborative approach ensured that our project benefitted from diverse perspectives and skill sets, leading to a comprehensive and robust solution.</p> <p>Utilizing my proficiency in LabVIEW, C#, native C, Java and ActiveX library, I led the development of a cutting-edge data acquisition (DAQ) device for CM system. This device seamlessly captured sensor readings from the live gas compressor in real-time, while the systemic algorithm I designed skillfully tracked false alarms using fallback sensors. By monitoring critical parameters such as ambient and self-temperature, axial speed, vibration, inflow pressure, and outflow pressure, we were able to enhance the system's reliability and performance.</p> <p>Furthermore, I effectively integrated the software condition monitoring system into the plant's existing SCADA infrastructure, ensuring seamless communication and compatibility with the overall plant monitoring and control system. This integration facilitated centralized data management and provided comprehensive insights into the gas compressor's operation, enabling proactive maintenance and efficient decision-making.</p> <p>By successfully managing the team and overseeing the integration process, I ensured that our project aligned seamlessly with the plant's existing infrastructure, resulting in an optimized and harmonized system.</p> <p>Through our collective efforts, we delivered a highly sophisticated and fully integrated software condition monitoring system that significantly improved the management and performance of the gas compressor at Shell Bonny Terminal.</p> <p>I have curated all the resource files that will enable anyone replicate our work. This is shared under the CC-by-4 license.</p> <p>The LabVIEW codes are documented in Work.zip file and all manuals and equipment specifications have been uploaded. I have created a test bench to help anyone quickly test the LabVIEW software in simulation mode. </p> <p>I have also provided a slide presentation that summarizes our work, including methodologies and results.</p>
Co-design of a citizen science study: unlocking the potential of eDNA for volunteer freshwater monitoring
<ol> <li>Citizen science is increasingly being promoted as a means of gathering more data to help inform the management of ecosystems. Involving the participants in the design of data collection activities is a form of co-design often proposed by those calling for a translational ecology.</li> <li>In addition, novel monitoring approaches have the potential to improve the quality of data collected by citizen scientists. We explored the potential of environmental DNA (eDNA) for vertebrate (mainly fish) species monitoring through a co-designed catchment monitoring strategy. </li> <li>Having been introduced to the potential of eDNA, citizen scientists designed and executed an eDNA-based survey of a small chalk stream catchment to explore questions of concern.</li> <li>The eDNA monitoring approach provided data about fish and other vertebrate diversity in the catchment which would have otherwise required sampling approaches difficult for citizen scientists. These data give a preliminary answer to some of the citizen scientists' priority questions and are comparable to fish data collected through traditional electrofishing surveys.</li> <li>Recommendations are offered for co-design and the use of novel research techniques by citizen scientists.</li> </ol>
Dataset used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence"
<p>This dataset repository includes input and output spatial data of urban microclimate simulations performed through QGIS and ENVI-met software used in the study "Urban microclimate simulations based on GIS data to mitigate thermal hot-spots: Tree design scenarios in an industrial area of Florence", published in the Building and Environment Journal, <a href="https://doi.org/10.1016/j.buildenv.2023.110854">https://doi.org/10.1016/j.buildenv.2023.110854</a>.</p>
Study of an Orthotic Designed to Equalize Leg Lengths for Patients With Injuries Managed in Walking Boots
ClinicalTrials.gov study NCT03848949. IPD Sharing: NO. Countries: 1. Publications: 4.
A Phase 1/2A, Randomized Study of a T Follicular Helper (TFH)-Targeting Genetic Vaccine Strategy Designed to Induce Broad, Durable Immune Responses
ClinicalTrials.gov study NCT06810934. IPD Sharing: YES. Countries: 1. Publications: 10.
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.