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20,299 results for “Systematic”
FIGURE 18. A in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURE 18. A Maximum-Likelihood tree (GTR + G + I model) based on the COI gene and rooted with Polyxenus lagurus. All data—except from H. bokori — were obtained from Genbank. Numbers refer to bootstrap values (1000 replicates). Scale bar = 0.02 substitutions / site. For origin of the H. bokori material, see Table 1.
FIGURES 12 – 14. Hungarosoma bokori Verhoeff, 1928 in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURES 12 – 14. Hungarosoma bokori Verhoeff, 1928, male (Abaliget Cave). 12: Antenna. 13: Gonopod complex, anterior view. The right side of pair structures is slightly turned laterally. 14: Gonopods in right lateral view. Abbreviations: Letters a – h signal equivalent structures in both views. Anterior gonopods (legs 8): a = cheirite, b = brush-like arm, c = additive divided arm, d = hyaline process. Posterior gonopods (legs 9): e = gonopod, f = ventral hyaline prominence, g = claw shape process with long seta. Not scaled. Photos: Andrej Mock.
FIGURES 6 – 9. Hungarosoma bokori Verhoeff, 1928 in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURES 6 – 9. Hungarosoma bokori Verhoeff, 1928, specimens from the Abaliget Cave, preserved in alcohol (not scaled). 6: Habitus of adult male in lateral view; the cheirites of anterior gonopods are visible. 7: Details of the dorsal part of the male trunk. 8: Ventral side of mid-body segments in detail. 9: Dorsal side of a juvenile of stadium III with the shape of the pleurotergites typical for the genus (all material from the Abaliget Cave, Hungary). Photos: Andrej Mock.
FIGURES 10 – 11. Hungarosoma bokori Verhoeff, 1928 in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURES 10 – 11. Hungarosoma bokori Verhoeff, 1928, female from the Driny Cave, scanning electronic microscopy of details of the shape and surface of mid-body segments. 10: Dorsolateral view (left side). 11: A pleurotergite, dorsolateral view in detail. Photos: Andrej Mock & Karel Tajovský.
FIGURE 16. Hungarosoma bokori Verhoeff, 1928 in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURE 16. Hungarosoma bokori Verhoeff, 1928, male, gonopods (Abaliget Cave). Right lateral view. Letters a – h signal equivalent structures in both views. Abbreviations: Letters a – h signal equivalent structures in both views. Anterior gonopods (legs 8): a = cheirite, b = brush-like arm, c = additive divided arm, d = hyaline process. Posterior gonopods (legs 9): e = gonopod, f = ventral hyaline prominence, g = claw shape process with long seta.
FIGURES 2 – 5. Hungarosoma bokori Verhoeff, 1928 in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURES 2 – 5. Hungarosoma bokori Verhoeff, 1928, female, holotype (Abaliget Cave). 2: Head end of the body, right lateral view. 3: Tergite 15, dorsal view. 4: Antenna, lateral view. 5: Discernable vulvae in situ (v), right lateral view. Photos: Jörg Spelda.
FIGURE 15. Hungarosoma bokori Verhoeff, 1928 in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURE 15. Hungarosoma bokori Verhoeff, 1928, male, gonopods (Abaliget Cave). Anterior view (right side of pair structures is slightly turned laterally). Abbreviations: Letters a – h signal equivalent structures in both views. Anterior gonopods (legs 8): a = cheirite, b = brush-like arm, c = additive divided arm, d = hyaline process. Posterior gonopods (legs 9): e = gonopod, f = ventral hyaline prominence, g = claw shape process with long seta.
FIGURE 1. Hungarosoma bokori Verhoeff, 1928 in Hungarosoma bokori Verhoeff, 1928 (Diplopoda: Chordeumatida): new insights into its taxonomy, systematics, molecular genetics, biogeography and ecology
FIGURE 1. Hungarosoma bokori Verhoeff, 1928, female, sampled at the entrance of the Baradla Cave, Hungary, 21. iii. 2013. Photo: Ľubomír Kováč & Andrej Mock.
FIGURE 3 in Morphology and systematic position of Cingula tumidula G. O. Sars, 1878 (Gastropoda: Rissoidae)
FIGURE 3. Details of reproductive anatomy of Pusillina tumidula. A – D. female reproductive system of two specimens from Franz Joseph Land, 44 m, 79 º 53.4 ' N, 51 º 26.06 ' E, MMBI, no number: A, C. left side; B, D. right side; lobes and seminal receptacle are not visible on B; E. male cephalopodium with penis. BC— bursa copulatrix, L— lobes, probably function in sperm storage, LOG— Lower oviduct gland, PE— penis, SD— seminal duct, SR— seminal receptacle, T— tentacle, UOG— upper oviduct gland. Scale = 0.5 mm.
FIGURE 2 in Morphology and systematic position of Cingula tumidula G. O. Sars, 1878 (Gastropoda: Rissoidae)
FIGURE 2. Details of protoconch (A – D) and radula (E – F) of Pusillina tumidula. A – B. Eastern Barents Sea (Pechora Sea), 17.5 m, 69 ° 15.61 ' N, 57 ° 22.76 ' E, MMBI, no number; C. Franz Joseph Land, 44 m, 79 º 53.4 ' N, 51 º 26.06 ' E, MMBI, no number; D. White Sea, 85 m, 66 ° 20 ' N, 30 ° E (specimen with costae), ZIN 35667 / 2; E – F. Dalne-Zelenetskaya Bay, 59 m, 69 ° 07.92 ' N, 36 ° 05.45 ' E, MMBI, no number. Scales: A, C – D = 300 µm, В = 50 µm, E = 20 µm, F = 5 µm.
FIGURE 1 in Cytotaxonomy of the Simuliidae (Diptera): a systematic and bibliographic conspectus
FIGURE 1. Number of chromosomal works on the Simuliidae per year, 1934 – 2014, including theses and dissertations. Klaus Rothfels’s seminal paper on the taxonomic utility of simuliid polytene chromosomes appeared in 1956.
Domain-Specific Language domain analysis and evaluation: a systematic literature review
<p>In order to successfully implement Domain-Specific Languages (DSLs), it is needed to systematically define and to support its development process; namely its Evaluation and the Domain Analysis phase. For that purpose, the studies were systematically selected from the most relevant venues that focus on the implementation of DSLs, in order to get insight if and how these development phases were performed. The special focus was given to the human-machine DSLs (excluding the machine-machine languages), the involvement of its end-users in the development process and the evaluation of DSLs usability, i.e. quality in use of DSLs. </p> <p>Preliminary results give us a notion that there is increased the state of practice of performing the evaluation of the DSLs, mostly including usability concerns, at least after its implementation. Generally, the quality of the reviewed studies was high. On another hand, rarely the assessments are done during domain analysis, which in general is not reporting inclusion of end-users or consideration of different use-cases, although the majority of studies refer to target non-programmers and to contribute easy in use. </p> <p>We did collect the valuable body of primary studies that are giving us answers to our research questions, however, to raise the credibility of the conclusions we should extend the analysis to other venues. Also, as we get insights into the categorization of practices we could specify more concretely answers that would give us means to perform more detailed meta-analysis. </p>
Systematic PSI processing over Santorini volcano: the 2011-2012 unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2011-2012, based on 12 descending ASAR Envisat images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2012-2013 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation. The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the period 2012-2013, based on 25 descending TerraSAR-X images. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 1992-2000 pre-unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 1992-2000, based on 37 descending ERS-1,2 images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2015 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2015, based on 30 ascending COSMO-SkyMed</strong> <strong>images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Systematic PSI processing over Santorini volcano: the 2012-2016 post unrest period
<p>In the framework of the ESA funded project “Disaster Risk Reduction using innovative data exploitation methods and space assets”, the National Observatory of Athens together with Terradue customized a GEP implementation of the StaMPS suite of s/w modules, to automatically process stacks of SAR imagery using Persistent Scatterer Interferometry. The engine is now able to seamlessly and timely process data from Envisat, ERS-1,2, TerraSAR-X, COSMO-SkyMed and Radarsat-2 platforms, for user-defined Regions Of Interest. Hence, the concept of project’s “Trial Case VO-1” is to modify, customize, execute and test a fully automatic processing chain over the GEP, starting from raw/SLC SAR data, to ground velocities generation.</p> <p>The new capabilities have been tested for an historic volcano in Santorini island, Greece, aiming to create a unique database of diachronic InSAR measurements spanning from 1992 to 2015 (~23 years of data). The processing of the five different SAR stacks was done entirely within the GEP environment, in the context of the Trial Case VO-1. The thematic map included herein contains the estimated Line-Of-Sight ground velocities (mm/yr) for the <strong>period 2012-2016, based on 20 descending Radarsat-2 images</strong>. Kindly note that the product is raw and has not gone through any manual post-processing. Therefore, adjustments may need to be made to address reference point issues, isolated phase unwrapping errors, etc. </p>
Understanding in vivo Models of Depression: A Systematic Review - Records of Full Search
<p>This Zenodo record outlines the full list of journal articles retrieved from the search string as well as a a subset of articles that have been screened by two independent human reviews and reconciled by a third independent screener. </p> <p> </p> <p>We carried out a search of 2 online databases for studies reporting animal models of depression. This search, carried out in May 2016, identified 70,365 unique publications (File: Depression-Dataset-SLIM-AllRecords.txt )</p> <p> </p> <p>Two independent investigators have, to date, screened 5749 of these publications for inclusion or exclusion and these publications form the dataset for this study (File: Updated-training-data.txt ). </p> <p>Several text-mining approaches will be developed for this depression literature search using the results from manual screening to train the machine, where machine-learning software set rules to automatically define each publication as included or excluded without the need for human screening. In this project, we seek to identify the best performing machine learning algorithm for this depression literature search. Performance is measured on sensitivity, specificity, and precision. </p> <p> </p> <p>Column Names in Datasets: </p> <p>Depression-Dataset-SLIM-AllRecords.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, SetNumber</p> <p>Depression-Dataset-SLIM-DevelopmentTrainingSet.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p>Updated-training-data.txt - ID, Author, Year, Title, Journal, Volume, Issue, Pages, Abstract, URL, Incl(1)/Excl(0)</p> <p> </p>
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) of the research entitled "Development of Competences for the Fashion Designer: a Scope Review
<p>PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol and Check list CAPSI - Critical analysis of the articles related to the specific objective: map the current themes that permeate the competencies of fashion design professionals through a scoping review.</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.