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18,621 results for “development;”
Data used for developing a parameterization for spatial distribution of solar irradiance over rugged terrain
<p>This dataset includes data and results produced while developing a parameterization for the spatial distribution of solar radiation over mountainous terrain. The method is designed for applications in Earth System Models.</p> <p>The four items included in this dataset are the following:</p> <p><strong>GFDL_preproc_dems</strong> includes digital elevations maps derived from the Shuttle Radar Topography Mission (SRTM) for three sample domains.</p> <p><strong>single-time-step </strong>Includes atmospheric optical properties used as input for Monte Carlo simulations.</p> <p><strong>gmd_2021_grids_light_*</strong> Include maps of terrain parameters derived from SRTM elevation data, and the partition of the domains in homogeneous tiles (sub-grid units) for different number and types of of land units.</p> <p><strong>rmc_simul_res</strong> results of the Monte Carlo simulations, consisting of maps of simulated solar radiation for different domains and solar angles.</p>
Autonomy Is An Acquired Taste: Exploring Developer Preferences for GitHub Bots
<p>Software bots are an important part of software development, and the rise of AI-based code tools will make them even more important. Software bots can do many tasks, and can exhibit a range of interaction behaviors in a project. But in order for the bot to be effective, it must be accepted by the developers and project community with which the bot interacts. The main purpose of this study is to enumerate some of these factors, and to explain what leads to perceived bad behavior in the context of a bot's autonomous actions and persona. We find developers prefer bots which are personable but show little autonomy. This dataset contains the replication package of the study. For Phase I, we shared the full and detailed interview guide, anonymized interview transcripts, and the result of the data analysis, i.e., codebook. For Phase II, the survey design and data. <br> </p>
Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?
<p>Artifact for "Are Neural Bug Detectors Comparable to Software Developers on Variable Misuse Bugs?"</p> <p><strong>Abstract:</strong> </p> <p>Debugging, that is, identifying and fixing bugs in software, is a central part of software development. Developers are therefore often confronted with the task of deciding whether a given code snippet contains a bug, and if yes, where. Recently, data-driven methods have been employed to learn this task of bug detection, resulting (amongst others) in so called neural bug detectors. Neural bug detectors are trained on millions of buggy and correct code snippets.</p> <p>Given the “neural learning” procedure, it seems likely that neu- ral bug detectors – on the specific task of finding bugs – have a performance similar to human software developers. For this work, we set out to substantiate or refute such a hypothesis. We report on the results of an empirical study with over 100 software developers, targeting the comparison of humans and neural bug detectors. As detection task, we chose a specific form of bugs (variable misuse bugs) for which neural bug detectors have recently made significant progress. Our study shows that despite the fact that neural bug detectors see millions of such misuse bugs during training, software developers – when conducting bug detection as a majority decision – are slightly better than neural bug detectors on this class of bugs. Altogether, we find a large overlap in the performance, both for classifying code as buggy and for localizing the buggy line in the code. In comparison to developers, one of the two evaluated neural bug detectors, however, raises a higher number of false alarms in our study.</p> <p><strong>Content:</strong> The artifact includes the following components:</p> <ul> <li> <p><strong>Web UI:</strong> The developer survey was performed online in the browser of the participants. For this, we created a custom web interface tailored for our study task. We included both the implementation of the frontend (website) and backend implementation (buisness logic and database) in this artifact. Therefore, it is not only possible to replicate our survey with same interface and a new group of participants but it is also possible to extend the interface for future studies. </p> </li> <li> <p><strong>Neural bug detectors: </strong>We evaluate the performance of the developers against two neural bug detectors. In this artifact, we include the bug detectors (implementation + trained models) and the evaluation script used for producing our results. Besides the replication of our bug detector evaluation, the detectors can also be used in future projects for detecting variable misuse bugs in Java methods.</p> </li> <li> <p><strong>Analysis scripts</strong>: After collecting the raw results from the developers and neural bug detectors, we performed several analysis to gain insights how developers and bug detectors compare on the variable misuse task. We include all analysis steps in form of Jupyter notebooks in the artifact. With this, it is possible to reproduce all the figures of our paper. </p> </li> </ul> <p>In addition, we also provide further artifacts that were successfully evaluated at ASE 2022:</p> <p><strong>ASE 2022 Artifact: </strong><a href="https://doi.org/10.5281/zenodo.6958242">10.5281/zenodo.6958242</a></p> <p><strong>Virtual machine: </strong><a href="https://doi.org/10.5281/zenodo.6957849">10.5281/zenodo.6957849</a></p>
Shared biophysical mechanisms determine early biofilm architecture development across different bacterial species
<p>These data are the numerical values that underlie the Figures 1 - 4, and the Supplementary Figures 1 - 10 for the article "Shared biophysical mechanisms determine early biofilm architecture development across different bacterial species" by the same authors. The content of the data files is organized according to the figures and figure panels of this article. </p>
Transcriptional regulation underlying the temperature response of embryonic development rate in the winter moth
<p>Climate change will strongly affect the developmental timing of insects, as their development rate largely depends on ambient temperature. However, we know little about the genetic mechanisms underlying the temperature sensitivity of embryonic development in insects. We investigated embryonic development rate in the winter moth (<em>Operophtera brumata</em>), a species with egg dormancy that has been under selection due to climate change. We used RNAseq to investigate which genes are involved in the regulation of winter moth embryonic development rate in response to temperature. Over the course of development, we sampled eggs before and after an experimental change in ambient temperature, including two early development weeks when the temperature sensitivity of eggs is low and two late development weeks when temperature sensitivity is high. We found temperature-responsive genes that responded in a similar way across development, as well as genes with a temperature response specific to a particular development week. Moreover, we identified genes whose temperature effect size changed around the switch in temperature sensitivity of development rate. Interesting candidate genes for regulating the temperature sensitivity of egg development rate included genes involved in histone modification, hormonal signalling, nervous system development, and circadian clock genes. In conclusion, the diverse sets of temperature-responsive genes we found here indicate that there are many potential targets of selection to change the temperature sensitivity of embryonic development rate. Identifying for which of these genes there is genetic variation in wild insect populations will give insight into their adaptive potential in the face of climate change.</p>
REMODEL. WP6. Sensory Systems and Mechatronic Tools. T6-2. Development and optimization of sensory system components. Data related to a paper published in MDPI Machines 2021
<p>The datasets contain the data related to the experiment presented in the publication:</p> <p>A. Cirillo, G. Laudante, and S. Pirozzi, “Proximity sensor for thin wire recognition and manipulation,” Machines, vol. 9, no. 9, art. 188, Sept. 2021. (DOI: 10.3390/machines9090188)</p>
Figure 3 in Notes on the embryological development of the Brevipalpus yothersi (Acari: Tenuipalpidae)
Figure 3 Internal analysis of the B. yothersi eggs: small perforations with a possible association with
Figure 1 in Notes on the embryological development of the Brevipalpus yothersi (Acari: Tenuipalpidae)
Figure 1 Development timeline of the Brevipalpus yothersi: (a) – B. yothersi female; (b) – Uncleaved egg: nucleus in the center of the egg; (c)
Figure 3 in Genome-wide characterization of microsatellites and development of polymorphic markers shared between two weevils of Eucryptorrhynchus (Coleoptera: Curculionidae)
Figure 3. Genetic structure of Eucryptorrhynchus brandti (a) and E. scrobiculatus (b) populations based on 14 microsatellite markers inferred using the software STRUCTURE. Each bin indicates an individual. Different colors show the identified clusters. The best number of clusters (K) is 3. Abbreviations: BJHD—Haidian District, Beijing; NXZW—Zhongwei, Ningxia; SDTA—Tai'an, Shandong; SXYL—Yangling, Shaanxi.
Figure 2 in Genome-wide characterization of microsatellites and development of polymorphic markers shared between two weevils of Eucryptorrhynchus (Coleoptera: Curculionidae)
Figure 2. Frequency distribution of microsatellites among different motifs in the Eucryptorrhynchus brandti and E. scrobiculatus. The "others" category represents summed motifs with counts below 100.
Figure 1 in Genome-wide characterization of microsatellites and development of polymorphic markers shared between two weevils of Eucryptorrhynchus (Coleoptera: Curculionidae)
Figure 1. Collection sites for specimens of Eucryptorrhynchus brandti (red) and E. scrobiculatus (green). Abbreviations: BJHD— Haidian District, Beijing (116.22°E, 40.04°N); NXZW—Zhongwei, Ningxia (105.12°E, 37.50°N); SDTA—Tai'an, Shandong (116.72°E, 36.27°N); SXYL—Yangling, Shaanxi (108.07°E, 34.26°N).
Text-fig. 10. Langtonia bisulcata REID et CHANDLER. a, b, e–g: Holotype, V. 22984, from micro-CT data. a: Dorsiventral view surface rendering. b: Dorsiventral view translucent volume rendering showing outline of locule cast. c: Equatorial transverse fracture showing paired dorsal infolds and locules with shape of a ε in cross section, reflected light, V. 22993. d: Digital transverse section from micro-CT data, of fruit with two well developed ε-shaped locules, V. 22985. e–g: Successive digital transverse sections with one well developed ε-shaped locule and infolds of the abortive locule visible in (g) (arrows). h–j: Physical transverse thin sections of specimen from middle Eocene Clarno Formation, Oregon, USA with well-preserved mesocarp including longitudinal canals in (j) (arrows), USNM 424875; Scale bars 0.5 cm in (a, b), 2.5 mm in (c–g), 5 mm in (h), 2 mm in (i), 1 mm in (j); (a, b) share same scale bar; (c, d) share same scale bar; (e, f, g) share same scale bar. in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 10. Langtonia bisulcata REID et CHANDLER. a, b, e–g: Holotype, V. 22984, from micro-CT data. a: Dorsiventral view surface rendering. b: Dorsiventral view translucent volume rendering showing outline of locule cast. c: Equatorial transverse fracture showing paired dorsal infolds and locules with shape of a ε in cross section, reflected light, V. 22993. d: Digital transverse section from micro-CT data, of fruit with two well developed ε-shaped locules, V. 22985. e–g: Successive digital transverse sections with one well developed ε-shaped locule and infolds of the abortive locule visible in (g) (arrows). h–j: Physical transverse thin sections of specimen from middle Eocene Clarno Formation, Oregon, USA with well-preserved mesocarp including longitudinal canals in (j) (arrows), USNM 424875; Scale bars 0.5 cm in (a, b), 2.5 mm in (c–g), 5 mm in (h), 2 mm in (i), 1 mm in (j); (a, b) share same scale bar; (c, d) share same scale bar; (e, f, g) share same scale bar.
REMODEL. WP6. Sensory Systems and Mechatronic Tools. T6-2. Development and optimization of sensory system components. Data related to IMM2021 publication
<p>The datasets contain data related to the experiments presented in the publication:</p> <p>Costanzo, M., Pirozzi, S., “Optical Force/Tactile Sensors for Robotic Applications” (2021) IEEE Instrumentation and Measurement Magazine, 24 (5), art. no. 9491003, pp. 28-35. DOI: 10.1109/MIM.2021.9491003</p>
Development and evaluation of E3SM-MOSAIC: Spatial distributions and radiative effects of nitrate aerosol
<p>FC20TR-MOZ_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MZT_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_SLOW_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_MTC_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_WGT_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_MTC-SPLC_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_SPLC_PD</p> <p> </p> <p>NO3_TM_2005-2014.nc 10-yr mean nitrate burden</p> <p>NO3_AQCH_GAEX_2005-2014.nc 10-yr mean for nitrate chemistry production</p> <p>NO3_DRF_2005-2014.nc 10-yr mean nitrate direct forcing between PD and PI</p> <p>NO3_INDRF_2005-2014.nc 10-yr mean nitrate indirect forcing between PD and PI</p> <p> </p> <p>NH4_TM_2005-2014.nc 10-yr mean for ammonium burden</p> <p>NH4_DRF_2005-2014.nc 10-yr mean ammonium direct forcing between PD and PI</p> <p> </p> <p>SO4_TM_2005-2014.nc 10-yr mean for sulfate burden</p> <p>SO4_DRF_2005-2014.nc 10-yr mean sulfate direct forcing between PD and PI</p> <p> </p> <p>CCN3_1850.nc Cloud condensation nuclei number concentrations at 0.1% super saturation at PI</p> <p>CCN3_2005-2014.nc CCN3 at PD</p> <p>CCN3_NONO3_1850.nc CCN3 at PI without nitrate formation</p> <p>CCN3_NONO3_2005-2014.nc CCN3 at PD without nitrate formation</p> <p> </p> <p>CDNC_*.nc cloud droplet number concentrations</p> <p>CLDFRC_*.nc cloud fraction</p> <p>CWP_*.nc cloud liquid water path</p> <p> </p> <p> </p>
Ecore version of the metamodel for developing learning ecosystems
<p>The learning ecosystem metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. The main objective of this metamodel is to provide a Computing Independent Model (CIM) for describing learning ecosystems build from software components, human elements and information flows between components which are represented by web services.<br> It is a transformation and improvement of https://doi.org/10.5281/zenodo.829859.</p>
Datasets used for developing sulfuric acid-water neutral and ion-induced particle formation parameterizations
<p>Supporting data for "New parameterizations for neutral and ion-induced sulfuric acid-water particle formation in nucleation and kinetic regimes" by Määttänen et al. (2017). A README file describing the datasets is included.</p> <p>Five datasets used for developing sulfuric acid-water neutral and ion-induced particle formation parameterizations, published in Määttänen et al. 2017, JGR Atmospheres (submitted) and at https://doi.org/10.5281/zenodo.1044366.</p> <p>Two datasets that have been used to produce Fig 8. of the manuscript: these datasets contain global model output of nucleation rates and total aerosol number concentrations.</p>
(c) simulation on Repast: after queen adaptive development-MODELING SELF-ORGANIZING SYSTEMS WITH SOCIAL INSECTS ALGORITHMS
<p>On figures (b) and (c), simulations on RePast [11, 16, 18] are<br> provided at successive times. The last figure shows the adaptive mechanism<br> of the queen which grows with time according to the material density around<br> it, like in natural observations.</p>
Figure 10. Conceptual diagram illustrating vector quantization codeword formation-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>After the conversion process converts the analogue speech signal into a series words,<br> then converting recognized word to facial animation based on VRML.</p>
Figure 9. Mel Cepstral Coefficients in time domain-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>In this section, Feature matching Algorithm has been discussed. The goal of feature<br> matching is to classify objects into one of a number of categories or classes. In this project, Vector<br> Quantization approach will be used and the best matching result will be the desired voice.</p>
Figure 6. Change the frequency at Hertz scale to Mel Scale.-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Following computing the spectrum power and applying the above equation on frequency<br> axis, some mediating filters equal to identical overlapping are applied on the scaled spectrum and<br> each filters energy is computed as particularity. This is because conception of a particular frequency<br> by the auditory system is affected by a critical band of frequencies surrounding it. Number of filters<br> is usually between 20 and 30. Logarithmic non linear change operations on obtained particulates for<br> adjusting amplified of particularities and their important though coordinating them with the<br> structure of the auditory system following computing energy of each filter is done as follows: in the<br> following equation F1 is filter in I th, and e (i) is logarithm of energy at ith band.</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.