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123 results for “review paper”
Dataset of systematic literature review of paper "Analysis of The Impact of Heat Stress on Embryo Development"
<p>This the dataset of the paper. It included</p> <p>1. Abstract of papers</p> <p>2. Details of papers</p> <p>3. PRISMA checklist </p>
Dataset for the paper "How to Work on Equality and Inclusion when Introducing Computational Thinking and Educational Robotics in Early Childhood Education: A Systematic Review"
<p>Resources for the Systematic Literature Review (SLR) about Computational Thinking and Educational Robotics in Early Childhood Education for fostering equality and inclusion. The SLR is related to the project "COEDUIN-Alfabetización digital y STEAM en edades tempranas: propuesta co-educativa inclusiva" funded by Fundación Caja Canarias and Fundación La Caixa (ref. 2020EDU08).</p> <p>The SLR covers papers in WoS and Scopus from 2011 to 2022.</p>
Dataset for the paper "Trends in studies developed in Europe about inclusion and diversity in schools: A systematic research projects review"
<p>Resources for the Systematic Research Projects Review (SRPR) about European research projects on school inclusion and diversity. The SRPR is related to the project "Gamified Values Education For Fostering Migrant Integration at Schools (GAMIGRATION)" funded by Erasmus+ programme of the European Union (ref. 2021-1-ES01-KA220-SCH-000032607).</p> <p>Search conducted on CORDIS and Erasmus+ platform.</p>
NPM3D dataset with instance label. Dataset used in paper "A Review of Panoptic Segmentation for Mobile Mapping Point Clouds"
<p>NPM3D is a public benchmark for point cloud semantic segmentation, with 10 classes including: ground, building, pole (road sign and traffic light), bollard, trash can, barrier, pedestrian, car, natural (vegetation) and unclassified. Results are evaluated only w.r.t. 9 classes, disregarding the "unclassified" label. The data has been captured with a mapping-grade mobile laser scanning system in different cities in France. There are 4 regions designated for training, all captured in Paris and Lille; and 3 regions for testing, captured in Dijon and Ajaccio. The standard 10-class version described above has actually been derived from a more fine-grained version of the dataset by keeping only the most frequent labels. The original annotations feature 50 different semantic classes (most of which are very rare), and also individual object instance labels for the training regions. For panoptic segmentation, a new version has been generated that still uses the 10 semantic category labels listed above, but also includes instance labels. The classes ground, building and barrier are considered "stuff" and are not separated into instances. As no instance labels are available for the 3 test regions, our version for panoptic (or pure instance) segmentation only contains 4 different regions from Paris and Lille. Instead of a fixed training/test split all experiments therefore use 4-fold cross-validation.</p>
z-sh/fse20: Replication package for paper#133 - Gender Bias in Code Review
<p>The replication package for paper#133 - Gender Bias in Code Review written by Huang et. al.</p>
Appendix and Figures for the paper "Artificial Intelligence Applications in Transportation: A Meta-Analytic Review"
<p>This collection includes the Appendix and Figures for the paper "Artificial Intelligence Applications in Transportation: A Meta-Analytic Review", written by Daniele Miranda and Pastor Taco.</p> <p>The Appendix consists of a summary table of the publications that were part of the literature review described on the paper.</p>
bachelor-thesis: Rapid Review - Distribution of the included papers
<p>This item contains the distribution of the included results from the search engines</p>
FIGURES 76–84. 76. Parapolybia bioculata. 77. P. fulvinerva. 78. P. takasagona. 79. P. tinctipennis. 80. P in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 76–84. 76. Parapolybia bioculata. 77. P. fulvinerva. 78. P. takasagona. 79. P. tinctipennis. 80. P. flava sp. nov. 81. P. nana sp. nov. 82. P. nana sp. nov. 83. P. indica. 84. P. crocea sp. nov. 76–81. ♀, dorsal view. 82–84. Vertex, ♀, dorsal view. An arrow indicating flat area behind ocelli.
FIGURES 31–40. Parapolybia fulvenerva, P in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 31–40. Parapolybia fulvenerva, P. crocea sp. nov., P. takasagona and P. tinctipennis. 31–32. Setae of wings. 31. Paraplybia fulvinerva. 32. P. crocea sp. nov. 33–35. Parapolybia takasagona, ♀. 33. Head, lateral view. 34, 35. T1 (34. Lateral view, 35. Dorsal view). 36–40. Parapolybia tinctipennis, ♀. 36, 37. Head (36. Frontal view, 37. Lateral view). 38. Ocelli, dorsal view. 39, 40. T1 (39. Lateral view, 40. Dorsal view). Scale 1 mm.
FIGURES 41–52 in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 41–52. Parapolybia flava sp. nov. 41–46. ♀. 47–52. ♂. 41–42, 47. Head, 41, 47. Frontal view. 42. Lateral view. 43, 48. Ocelli, dorsal view. 44, 45, 49, 50. T1 (44, 49. Dorsal view, 45, 50. Lateral view). 46. T2, laterodorsal view. 51. Volsella and digitus. PS, parameral spine. D, Digitus. 52. Aedeagus. Scale 1 mm.
FIGURES 53–64 in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 53–64. Parapolybia crocea sp. nov. 53–57. ♀. 58–64. ♂. 53–54, 58. Head. 53, 58. Frontal view. 54. Lateral view. 55, 59. Ocelli, dorsal view. 56, 57, 61, 62. T1 (56, 61. Dorsal view, 57, 62. Lateral view). 60. F9–F11 63. Volsella and digitus. 64. Aedeagus. Scale 1 mm.
FIGURES 19–30. Parapolybia bioculata. 19–22 in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 19–30. Parapolybia bioculata. 19–22. ♀. 23–30. ♂. 19, 23. Head, frontal view. 20, 24. Ocelli, dorsal view. 21, 22, 26, 27. T1 (21, 26. Dorsal view, 22, 27. Lateral view). 25. F9–F11 28. Hind tarsus. 29. Volsella and digitus. 30. Aedeagus. Scale 1 mm.
FIGURES 7–11 in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 7–11. Parapolybia indica, holotype ♀. 7, 9. Head. 7. Frontal view. 9. Lateral view. 8. Ocelli, dorsal view. 10. T1, dorsal view. 11. Metasomal segments 1–2, lateral view, segment 2 glued upside down. Scale 1 mm.
FIGURES 1–6. Parapolybia varia, P. c roc e a in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 1–6. Parapolybia varia, P. c roc e a sp. nov. and holotype of P. indica (de Saussure, 1854). 1, 2. Head, lateral view, ♀. 1. P. crocea sp. nov. 2. P. v ar i a. 3, 4. Dried specimens of P. crocea sp. nov. from the same colony. 3. Body color in live more or less well preserved. 4. Discolored while drying. 5, 6. Holotype of P. indica. 5. Dorsal view, metasomal segments 2–6 glued upside down. 6. Labels of holotype.
FIGURES 65–75 in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 65–75. Parapolybia nana sp. nov. and P. albida sp. nov. 65–69. P. nana sp. nov., ♀. 65, 66. Head (65. Frontal view, 66. Lateral view). 67. Ocelli, dorsal view. 68, 69. T1 (68. Lateral view, 69. Dorsal view). 70–75. P. albida sp. nov., ♀. 70, 71, 75. Head (70, 75. Frontal view, 71. Lateral view). 72. Ocelli, dorsal view. 73, 74. T1 (73. Lateral view, 74. Dorsal view). Scale 1 mm.
FIGURES 85–90. Nests. 85. Parapolybia indica. 86. P in Review of the paper wasps of the Parapolybia indica species-group (Hymenoptera: Vespidae, Polistinae) in eastern parts of Asia
FIGURES 85–90. Nests. 85. Parapolybia indica. 86. P. flava sp. nov. 87. P. crocea sp. nov. 88. P. tinctipennis. 89, 90. P. bioculata.
Dataset of the Paper: Demystifying Code Snippets in Code Reviews: A Study of the OpenStack and Qt Communities and A Practitioner Survey
<p>This dataset, which is composed of two parts: the code review data collected from OpenStack and Qt, and the survey data collected from industrial developers, was used to conduct an empirical study on code snippets in code reviews. A brief description of each part of the dataset is provided below:</p> <p><strong>1. Dataset (OpenStack+Qt).xlsx</strong></p> <p>contains 127,182 review comments mined from four popular projects of the OpenStack community (Nova and Neutron) and the Qt community (Qt Base and Qt Creator) from 2020 to 2021. Among the review comments, 3,197 review comments contain code snippets. In the Excel file, for the rows of review comments with code snippets, we marked the code snippets in red color as an indicator.</p> <p><strong>2. Dataset (Survey).xlsx</strong></p> <p>contains 63 valid responses to our survey questionnaire from industrial developers of OpenStack, Qt, and LinkedIn.</p>
Extra Material for the paper: Experiences from conducting rapid reviews in collaboration with practitioners --- Two industrial cases
Open the record for dataset details and reuse information.
Dataset for "In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review" paper
Open the record for dataset details and reuse information.
Artifacts of the paper under review by ISSTA
<h1>Artifacts of the paper under review by ISSTA</h1> <div>Welcome to the online repository of the ISSTA 2024 Submission 1305. We release the source code, dataset, and outputs of our study.</div> <h2>Getting Started</h2> <h3>Requirements</h3> <div>To run the project, please ensure that you have the following dependencies installed:</div> <div> </div> <div><code> pytorch=2.0.0;</code></div> <div><code> torchvision=0.15.1;</code></div> <div><code> torchaudio;</code></div> <div><code> datasets==1.16.1;</code></div> <div><code> transformers==4.21.1;</code></div> <div><code> tensorboard==2.12.2;</code></div> <div><code> tree-sitter==0.19.1;</code></div> <div><code> nltk=3.8.1;</code></div> <div><code> scipy=1.10.1;</code></div> <div>You can either install these requirements manually or execute the following script:</div> <div><code>bash scripts/setup.sh</code></div> <h3>Dataset</h3> <div>We perform our experiments on following datasets:</div> <div> * CodeSearchNet from [here](https://github.com/github/CodeSearchNet).</div> <div> * BFP dataset from Tufano et al.'s TOSEM'19 <a href="https://dl.acm.org/doi/abs/10.1145/3340544">paper.</a></div> <div> * Defects4J dataset from René et al.'s ISSTA'14 <a href="https://dl.acm.org/doi/abs/10.1145/2610384.2628055">paper.</a></div> <div> * VulRD from Fu et al.'s FSE'22 <a href="https://dl.acm.org/doi/abs/10.1145/3540250.3549098">paper</a>.</div> <div>Download the pre-training dataset:</div> <div><code>python Dataset\pre-training\download.py</code></div> <h3>Pretrain the model</h3> <div><code>bash scripts/pre-train.sh -g [GPU_ID]</code></div> <h3>Task 1: bug repair</h3> <div><code>bash scripts/finetune_d4j.sh -g [GPU_ID]</code></div> <div>If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.</div> <div>To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.</div> <div> </div> <h3>Task 2: vulnerability repair</h3> <div><code>bash scripts/finetune_vul.sh -g [GPU_ID]</code></div> <div>If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.</div> <div>To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.</div> <h3>Outputs</h3> <div>The outputs of our tool are stored in the `outputs` folder.</div> <p><br><br></p>
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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.