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136 results for “Analytical data”
Big Data Analytics: Data-driven optimization of manufacturing companies
<p>This dataset contains the result of a survey conducted to identify the transformation of manufacturing companies by big data analytics. It contains 154 participations from employees of manufacturing companies of which 133 have completed all questions. The dataset is UTF8 encoded and available in German (original) as well as English (translated).</p>
[VERSION 2] Data set and analytic codes supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?"
<p>This is <strong>VERSION 2</strong> of data set and analytic codes (with a meta data [see the meta data from VERSION 1]) supporting "How do management decisions impact butterfly assemblages in smallholding oil palm plantations in Peninsular Malaysia?". We investigated the impacts of replanting and alternative replanting decisions (replanting with monoculture versus polyculture oil palm plantations) on within-plantation environmental conditions and butterfly assemblages (diversity, density, and composition). We also assessed the effects of habitat structure and complexity within plantations on butterfly assemblages. Apart from "BantingButterflies_ButterflyData", other data are the same as in VERSION 1.</p><p><strong>## List of changes:</strong></p><p># 1. <i>Tirumala septentrionis </i>was not included in the analyses because it should have been <i>Ideopsis vulgaris</i> (had been corrected),</p><p># 2. PC5 and PC6 (from PCA) were considered as predictors for the GLMs,</p><p># 3. The Mantel test was added.</p><p><strong>## Other notes:</strong></p><p># 1. Older version of ggiNEXT could work with facet.var = "site", now it needs to be "Assemblage"</p><p># 2. Older version of ggiNEXT could work with facet.var = "order", now it needs to be "Order.q"</p><p># 3. "set.seed(42)" function was used before running "iNEXT", ANOSIM, and the Mantel test to get reproducible outputs (exactly the same outputs every time each function is run).</p><p><strong>Funding and research permission:</strong> Jardine Foundation, the Cambridge Trust, and Tim Whitmore Fund provided funding for MFH, the Biotechnology and Biological Sciences Research Council (BBSRC) funded JS (USN: 304338625), and BBSRC (BB/T012366/1) provided funding for the establishment of the plots and surveys of environmental parameters. Research permission was provided by the Economic Planning Unit (EPU) of Malaysia's Prime Minister's Department for MFH (Ref: EPU 40/200/19/3727) and JS (Ref: MEA 40/200/19/3705).</p>
data for article "Theory informed, experiment based, constraint on the rate of autoxidation chemistry – An analytical approach"
<p>The files contain the data from simulations shown in plots for main manuscript figures and SI figures. Further, a preliminary autoCONSTRAINT verion to reproduce the results shown is attached and the PyCHAM model used to create CIMS-like inputs to autoCONSTRAINT referenced (model inputs are attached). For any questions on the data contact: office@pi-numerics.com</p> <p>Detailed information on the file content is provided in file "SI_data_description"</p>
Big data analytics in support of the under-rail maintenance management at Vitória – Minas Railway
<p>This video describes an ongoing study using data collected by an instrumented ore car on Vitória–Minas Railway, operated by Vale in Brazil. The research uses big data analysis methods over collected data by the instrumented car during its voyages. Railway geometry issues can cause undesirable movements on the wagons that can cause discomfort for passengers or instability for the cargo. In the worst scenario, derailments can occur. Each second, several sensors installed on the instrumented car collect data about velocity, acceleration, and movements on the wagon. The volume of collected data is impressive since the railway has about 2,000 km of extension. That volume compels us to use big data analytics methods. As the result of the research, the team aims to establish some levels of operational conditions, named as severity indexes, which can indicate to the maintenance teams the necessity of intervention on the railway.</p>
Analytics, Visualisation and Machine Learning of General Practitioner Prescribing using Open Health Data
<p>Open Prescription data used in Postgraduate project into Northern Ireland General Practice prescribing.</p>
Data set and analytic codes supporting "Length-biomass equations to allow rapid assessment of semi-aquatic bug biomass in tropical streams"
<p>Body size and biomass data of semi-aquatic bugs (Gerromorpha, Hemiptera) collected in a range of habitat types (oil palm, oil palm with buffer, logged forest, and old-growth forest) in the SAFE Project (Stability of Altered Forest Ecosystems) experimental area, Maliau Basin, and Danum Valley. The sites are located in Sabah, Malaysia. In this study, we developed length-biomass equations to estimate the biomass of semi-aquatic bugs.</p>
Cloud-based User Entity Behavior Analytics Log Data Set
<p>This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1].</p><p>The logs are provided in JSON format with the following attributes in the first level:</p><ul><li><strong>id</strong>: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1.</li><li><strong>time</strong>: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z.</li><li><strong>uid</strong>: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer.</li><li><strong>uidType</strong>: Specifier for uid, which is either the user name or IP address for logged out users.</li><li><strong>type</strong>: The action carried out by the user, e.g., file_accessed.</li><li><strong>params</strong>: Additional event parameters (e.g., paths, groups) stored in a nested dictionary.</li><li><strong>isLocalIP</strong>: Optional flag for event origin, which is either internal (true) or external (false).</li><li><strong>role</strong>: Optional user role: consulting, administration, management, sales, technical, or external.</li><li><strong>location</strong>: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc.</li></ul><p>In the following data sample, the first object depicts a successful user login (see <i>type: login_successful</i>) and the second object depicts a file access (see <i>type: file_accessed</i>) from a remote location:</p><blockquote><p>{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}</p><p> {"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"}</p></blockquote><p>The data set was generated at the premises of <a href="https://www.huemer-group.com/">Huemer Group</a>, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called <a href="https://www.huemer-group.com/hbox/">hBOX</a> enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project <a href="https://nextcloud.com">Nextcloud</a> with interfaces and functionalities tailored to the requirements of customers.</p><p>The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our <a href="https://github.com/ait-aecid/clue-lds">github repo</a>.</p><p>Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set.</p><p><strong>If you use the dataset, please cite the following publication:</strong></p><p>[1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. <a href="https://doi.org/10.1109/BigData55660.2022.10020672">"A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing"</a>. <a href="http://bigdataieee.org/BigData2022/"><i>2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022)</i></a>, December 17-20, 2022, Osaka, Japan. IEEE. [<a href="https://www.skopik.at/ait/2022_bigdata.pdf">PDF</a>]</p>
Co-curricular Quantitative Data Analytics Training Program (CQDATP) for Undergraduate Natural Science Students
<p>While data analytics has increasingly become an essential skill for various professionals in natural science fields, college curricula often cannot be updated frequently to meet the emerging changes of the workforce’s needs. A drastic curricular change is particularly difficult at small colleges with limited availability of faculty. This study proposes a co-curricular training program that enables natural science students to master skills of applying quantitative data analytics on real datasets without requiring a significant curricular change. The training program was developed and implemented as a sequence of in-person one-hour training sessions at a small four-year college. The results show that the approach is effective in improving students’ data analytics skills. This program can be adapted by other small institutions to achieve similar goals.</p> <p> </p> <p>This dataset include the training materials and deidentified student performance and survey responses. </p>
Metagenomics education in a modular CURE format positively affects students' scientific discovery perception and data analytical skills
<p>The targeted metagenomics study performed by Pollock et al. <em>(Pollock 2018)</em> was part of the Global Coral Microbiome Project <em>(Vega Thurber Lab 2014)</em>. Since the original publication of Pollock et al. <em>(Pollock 2018) </em>was written for a highly specialized academic public, we provided Bsc. students with a short introduction of the paper and guided them through the metadata table stored in the <em>gcmp16S_map_r25.txt </em>file under the <em>GCMP Australia sequence data, OTU tables, and metadata</em> folder (<a href="https://doi.org/10.6084/m9.figshare.c.3855466.v2">https://doi.org/10.6084/m9.figshare.c.3855466.v2</a>).</p> <p>In order to process the fastq-files and obtain OTU tables, a custom-made pipeline on a Galaxy instance <em>(Galaxy Community)</em> was used. This pipeline contained data analytical tools obtained from the Naturalis Biodiversity Center GitHub environment (<a href="https://github.com/naturalis">https://github.com/naturalis</a>). In order to align with the newest Galaxy best practices, we updated the tools and links to the original as well as the updated GitHub and toolshed versions are included in the references list.</p> <p>The names of the original raw fastq-files contain many dots which can hamper file-type recognition during downstream analyses in Galaxy. Therefore, prior to distributing the fastq-files to the students, these names were renamed using the ManageZIP tool <em>(The BLFS Development Team 1999-2023)</em>. Dots were replaced with underscores, with an exception made for the last 2 file type extension dots. For example, the file name <em>E1.2.Tur.pelt.1.20140814.M_S71_L001_R1_001.fastq.gz</em> was changed into <em>E1_2_Tur_pelt_1_20140814_M_S71_L001_R1_001_1.fastq.gz</em>. Similarly, the filenames in the metadata table were adjusted accordingly. The total collection of renamed file names is available through this zenodo repository as wel as the accompanying metadata file.</p>
Analytical Electron Microscopy of Grain Boundary Segregation: Application to Al-Zn-Mg-Cu (7xxx) Alloys - Supporting Data
<p>Data to reproduce the figures reported in the manuscript Analytical Electron Microscopy of Grain Boundary Segregation: Application to Al-Zn-Mg-Cu (7xxx) Alloys. Published open access in Materials Characterization <a href="https://doi.org/10.1016/j.matchar.2019.06.016">https://doi.org/10.1016/j.matchar.2019.06.016</a></p>
Table 5. Analytical data for miscellaneously attributed samples from Broken Hill, including the Broken Hill Consols Mine. a in Compositions of silver halides from the Broken Hill district, New South Wales
<p><b>Table</b> 5. Analytical data for miscellaneously attributed samples from Broken Hill, including the Broken Hill Consols Mine.a</p><table><tbody><tr><th>No.</th><th><b>Analyses</b></th></tr></tbody><tbody><tr><th>D46338b</th><td>Cl</td><td>60</td><td>57</td><td>57</td><td>53</td><td>56</td><td>57</td><td>58</td><td>68</td><td>56</td><td>66</td><td></td></tr><tr><th></th><td>Br</td><td>32</td><td>35</td><td>34</td><td>37</td><td>35</td><td>34</td><td>32</td><td>31</td><td>35</td><td>33</td><td></td></tr><tr><th></th><td>I</td><td>8</td><td>8</td><td>9</td><td>10</td><td>9</td><td>9</td><td>10</td><td>1</td><td>9</td><td>1</td><td></td></tr><tr><th>D26168c</th><td>Cl</td><td>72</td><td>76</td><td>63</td><td>65</td><td>60</td><td>77</td><td>77</td><td>72</td><td>66</td><td>65</td><td></td></tr><tr><th></th><td>Br</td><td>28</td><td>24</td><td>37</td><td>35</td><td>40</td><td>22</td><td>23</td><td>28</td><td>34</td><td>35</td><td></td></tr><tr><th>D28184d</th><td>I Cl</td><td>75</td><td>68</td><td>86</td><td>70</td><td>70</td><td>1 70</td><td>78</td><td>70</td><td>84</td><td></td><td></td></tr><tr><th></th><td>Br</td><td>25</td><td>30</td><td>14</td><td>28</td><td>30</td><td>29</td><td>21</td><td>30</td><td>16</td><td></td><td></td></tr><tr><th>D28185d</th><td>I Cl</td><td>55</td><td>2 56</td><td>55</td><td>2 56</td><td>55</td><td>155</td><td>1 60</td><td>50</td><td>45</td><td>50</td><td>56</td></tr><tr><th></th><td>Br</td><td>43</td><td>43</td><td>44</td><td>43</td><td>44</td><td>43</td><td>40</td><td>46</td><td>45</td><td>45</td><td>41</td></tr><tr><th></th><td>I</td><td>2</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td></td><td>4</td><td>10</td><td>5</td><td>3</td></tr></tbody></table><p>a Analyses reported as in Table I. b Trace S detected. C Traces Pb, Cu, S, As detected. d Consols Mine; traces Pb, As, S, Fe detected.</p>
Repository Analytics and Metrics Portal (RAMP) 2018 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2018. For a description of the data collection, processing, and output methods, please see the "methods" section below. Note that the RAMP data model changed in August, 2018 and two sets of documentation are provided to describe data collection and processing before and after the change.</p>
Repository Analytics and Metrics Portal (RAMP) 2017 data
<p>The Repository Analytics and Metrics Portal (RAMP) is a web service that aggregates use and performance use data of institutional repositories. The data are a subset of data from RAMP, the Repository Analytics and Metrics Portal (<a href="http://ramp.montana.edu/">http://rampanalytics.org</a>), consisting of data from all participating repositories for the calendar year 2017. For a description of the data collection, processing, and output methods, please see the "methods" section below.</p>
Data supplement for: Agreement of analytical and simulation-based estimates of the required land depth in climate models
<p>Many current-generation climate models have land components that are too shallow. Under climate change conditions, the long-term warming trend at the surface propagates deeper into the ground than the commonly used 3-10m. Shallow models alter the terrestrial heat storage and distribution of temperatures in the subsurface, influencing the simulated land-atmosphere interactions. Previous studies focusing on annual timescales suggest that deeper models are required to match subsurface-temperature observations and the classic analytical heat conduction solution. However, for a systematic investigation of land-model deepening in the frame of anthropogenic climate change, the classic analytical solution is inaccurate because it does not mimic the timescale and amplitude of the simulated warming trend. This study intends to bridge the gap between analytical and simulation-based estimates of the subsurface thermodynamic state by adapting the classic analytical framework to mimic long-term anthropogenic warming. The analysis shows that a land-model depth of at least 170m is recommended for a proper simulation of the post-1850 ground climate, which differs up to 30% from the estimate of the classic approach. Compared to previous studies, this provides an accurate estimate of the required land model depth for long-term climate-change simulations and indicates the relative bias in insufficiently deep land models.</p>
Data for "Light Curves of Type IIP Supernovae from Neutrino-driven Explosions of Red Supergiants Obtained by a Semi-analytic Approach"
<p>Data in the form of Table 1 in the accompanying paper "Light Curves of Type IIP Supernovae from Neutrino-driven Explosions of Red Supergiants Obtained by a Semi-analytic Approach" at arXiv.org</p> <p>See the README file on the usage</p>
Data set and analytic codes supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia"
<p>This deposit contains data set and analytic codes** (accompanied with a meta data) supporting "The effects of land-use change on semi-aquatic bugs (Gerromorpha, Hemiptera) in rainforest streams in Sabah, Malaysia". We investigated the impacts of land-use change on semi-aquatic bug (Gerromorpha, Hemiptera) communities in Sabah, Malaysia.</p> <p>Semi-aquatic bugs were collected from streams in old-growth forest, logged forest, and oil palm with and without riparian buffer strips. A range of environmental parameters were also collected to represent environmental conditions (associated with land-use change). Environmental data were collected at catchment, riparian, and stream scales, and were used separately for the assessments of their effects on the bugs. We looked at the effects on the abundance, biomass, species richness, and community composition of semi-aquatic bugs. We also assessed the effects on the proportion of juveniles, winged individuals, and female <em>Ptilomera</em> sp. (a morphospecies with clear sexual dimorphism in this study).</p> <p>This research was funded by the Jardine Foundation, the Cambridge Trust, the Natural Environment Research Council (NERC) (studentship 1122589), Proforest, the Varley Gradwell Travelling Fellowship, the Tim Whitmore Fund, the Panton Trust, the Cambridge University Commonwealth Fund, the Hanne and Torkel Weis-Fogh Fund, and the S.T. Lee Fund.</p> <p> </p> <p>** For reproducibility of outputs of the Canonical Correspondence Analysis (CCA), do insert the following function in the R Markdown before the line of "anova.cca(CCAEnvInsect, by = 'terms', first = TRUE)":</p> <p>set.seed(42) # About set.seed: <a href="https://stackoverflow.com/questions/13605271/reasons-for-using-the-set-seed-function">r - Reasons for using the set.seed function - Stack Overflow</a></p>
Models and data for "Guaranteed safe controller synthesis for switched systems using analytical solutions"
<p>Models and data for the paper "Guaranteed safe controller synthesis for switched systems using analytical solutions" presented at the 7th IEEE Conference on Control Technology and Applications, Barbados, August 16-18, 2023.</p> <p> </p>
BIG DATA ANALYTICS IN DIGITAL HUMAN RESOURCES MANAGEMENT: IMPACT ON THE RECRUITMENT PROCESS
<p>This study investigates how HR employees experience the big data phenomenon in the recruitment function of HRM and how their perceptions of the phenomenon have evolved. This study also examines how BD will affect organizational and HRM and how it can be improved in other functions of HR. In this exploratory study, which comprehensively addresses the BD phenomenon in HRM, the phenomenological design approach, one of the qualitative research methods, was applied to test the research questions and a semi-structured interview form was used for research data. Using the snowball sampling method, in-depth interviews were conducted with 10 HR employees working in large and semi-structured organizations in Turkey and the interviews were analyzed with MAXQDA 20. The findings show that HRM employees are aware of BD. On the other hand, it is understood that BD technologies provide easy accessibility in recruitment, offer a strategic competitive advantage, and enable more effective management of information management, which saves HR employees' work in a facilitating way. They benefit from technology as a decision support assistant. Finally, the research results provide theoretical and practical implications for future researchers and practitioners for the development and effective use of BD technology in the field of HRM.</p>
Data from: Assigning harvested waterfowl to geographic origin using feather δ2H isoscapes: What is the best analytical approach?
<p class="MsoListParagraph">Establishing links between breeding, stopover, and wintering sites for migratory species is important for their effective conservation and management. Isotopic assignment methods used to create these connections rely on the use of predictable, established relationships between the isotopic composition of environmental hydrogen and that of the non-exchangeable hydrogen in animal tissues, often in the form of a calibration equation relating feather (<em>δ</em><sup>2</sup>H<sub>f</sub>) values derived from known-origin individuals and amount-weighted long-term precipitation (<em>δ</em><sup>2</sup>H<sub>p</sub>) data. The efficacy of assigning waterfowl to moult origin using stable isotopes depends on the accuracy of these relationships and their statistical uncertainty. Most current calibrations for terrestrial species in North America are done using amount-weighted mean growing-season <em>δ</em><sup>2</sup>H<sub>p</sub> values, but the calibration relationship is less clear for aquatic and semi-aquatic species. Our objective was to critically evaluate current methods used to calibrate <em>δ</em><sup>2</sup>H<sub>p</sub> isoscapes to predicted <em>δ</em><sup>2</sup>H<sub>f</sub> values for waterfowl. Specifically, we evaluated the strength of the relationships between <em>δ</em><sup>2</sup>H<sub>p</sub> values from three commonly used isoscapes and known-origin <em>δ</em><sup>2</sup>H<sub>f</sub> values from three published and one collected as part of this study, also grouping these data into foraging guilds (dabbling vs diving ducks). </p>
Workflow-Based Spatio-Temporal Data Analytics
<p>In the biodiversity domain, researchers often have to combine a large variety of heterogeneous spatio-temporal data sources. For example, the loss of biodiversity can be quantified by analyzing occurrence observations of various species across time. To find the root cause of that loss, occurrence data may need to be combined with satellite images to find possible correlations with climate variables. To facilitate an exploratory approach for this combination of data sources, it is essential to provide researchers with workflow-based tools such that each step during the formulation of a research hypothesis can be tracked. In this presentation, we will discuss Geo Engine, a workflow-based analysis platform for spatio-temporal data analytics, and its place within FAIR data spaces.</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.