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Supplementary material for 'The MAP metric in Information Retrieval Fault Localization'
<pre># map_bench4bl This is the supplementary material, data, and evaluation source code for the paper "The MAP metric in Information Retrieval Fault Localization" by Thomas Hirsch and Birgit Hofer. ## Preliminaries ### Python environment - Python 3.8 - pandas - numpy - matplotlib ## Datasets The [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) dataset has been used in this evaluation, with the addition of intermediate files taken from the [SABL](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>) experiment performed on this Bench4BL dataset. All data used in our evaluation is included in this repository. However, if the data is to be re-imported directly from these benchmark and datasets they have to be downloaded first and their local paths have to be set in [paths.py](<em>paths.py</em>). ### Bench4BL The Bench4BL dataset was published with the paper "Bench4BL: Reproducibility study on the performance of IR-based bug localization" by Lee, J., Kim, D., Bissyandé, T.F., Jung, W. and Le Traon, Y.. The dataset can be obtained [here](<em>https://github.com/exatoa/Bench4BL</em>). Follow the steps described in the corresponding [README](<em>https://github.com/exatoa/Bench4BL/blob/master/README.md</em>) to set up the dataset. The Bench4BL dataset contains the _old subjects_ subdataset, containing 558 bugs from AspectJ, JDT, PDE, SWT, and ZXing that have been widely used in older IRFL studies. This _old subjects_ subdataset was used in answering our RQ1, as discussed below, the corresponding scripts use _old subjects_ in their name to highlight this. #### SABL The SABL dataset is the online appendix of the paper "An Extensive Study of Smell-Aware Bug Localization" by TTakahashi, A., Sae-Lim, N., Hayashi, S. and Saeki, M.. The dataset can be downloaded [here](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>). The experiments in this dataset build on top of Bench4BL and intermediate files are provided in the datapackage. #### Rankings Rankings for BLIA, BRTracer, and BugLocator were produced by running these tools on Bench4BL locally. Rankings for AmaLgam and BLUiR were taken from the SABL experiment dataset. ## Structure ### Folders Bench4BL ground truths: - bench4bl_old_subjects_summary - bench4bl_summary Localization results of the included tools in Bench4BL: - bench4bl_localization_results - bench4bl_localization_results_sabl Target projects size metrics: - cloc_results - cloc_results_old_subjects Utility functions: - utils Output folders containing results, generated figures and tables: - results - results_old_subjects ### Scripts Scripts for re-importing data from Bench4BL and SABL datasets: - data_preparation_step_1_cloc_bench4bl.py - data_preparation_step_1_cloc_old_subjects_bench4bl.py - data_preparation_step_2_import_ground_truth_from_bench4bl.py - data_preparation_step_2_import_ground_truth_from_old_subjects_bench4bl.py - data_preparation_step_3_import_bench4bl_ranking_results.py - data_preparation_step_3_import_sabl_ranking_results.py Utilities: - paths.py - utils/bench4bl_utils.py - utils/Logger.py ### Evaluation scripts for the corresponding research questions: **Dataset analysis:** - rq_0_dataset_analysis_bench4bl_issues.py **RQ1: How big is the average ground truth in Bench4BL datasets, and what proportion of bugs have a ground truth containing multiple files?** - rq_1_bench4bl_ground_truth_size.py - rq_1_old_subjects_bench4bl_ground_truth_size.py RQ2: Do the IRFL tools included in Bench4BL truncate their results? - rq_2_ranking_lengths.py **RQ3: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results on the Bench4BL dataset? RQ3a: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results when considering the bloated ground truth issue found in Bench4BL?** - rq_3_truncating_BugLocator_rankings_bench4bl.py **RQ3b: How strong is $AP_{asrd}$ overestimating $AP_{mb}$ for truncated BugLocator retrieval results when undefined $AP$ values are simply ignored?** - rq_3b_undefined_ap_BugLocator_rankings_bench4bl.py ## Licence All code and results are licensed under [CCA v4](<em>https://creativecommons.org/licenses/by/4.0/</em>), according to LICENSE file. Other licences may apply for some tools and datasets contained in this repo: [cloc-1.92.pl](<em>https://github.com/AlDanial/cloc</em>) under GPL v2, [Bench4BL](<em>https://github.com/exatoa/Bench4BL</em>) and [SABL](<em>http://dx.doi.org/10.5281/zenodo.4681242</em>) under CCA 4.0.</pre>
Spatial soil properties maps for Switzerland at 30 m resolution
<p>The Swiss Soil Property Map (SSPM) was developed using the quantile random forest machine learning algorithm and remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. The SSPM dataset provides maps at 30 m resolution for different soil depths (0, 30, 60, and 100 cm) in GeoTIFF format. The mean and respective uncertainty information is provided for each map. Please note that the phosphorus spatial map is only available for the topsoil (0-20 cm) due to the unavailability of the dataset at deeper depths.</p> <table> <caption>Description of soil properties (SP) and their units</caption> <tbody> <tr> <td>SP</td> <td>Description</td> <td>units</td> </tr> <tr> <td>Sand </td> <td>Sand content</td> <td>%</td> </tr> <tr> <td>Clay</td> <td>Clay content</td> <td>%</td> </tr> <tr> <td>OC</td> <td>Organic carbon content </td> <td>%</td> </tr> <tr> <td>N</td> <td>Nitrogen content</td> <td>% </td> </tr> <tr> <td>P</td> <td>Phosphorus content</td> <td>mg/kg</td> </tr> </tbody> </table> <p>For more details / to cite this dataset please use:</p> <ul> <li><strong>Gupta, S. </strong>, Hasler, K. J., Alewell, C.: Mapping soil properties of Switzerland using remote sensing datasets and machine learning approach. Manuscript <strong>submitted</strong>, <strong>Geoderma Regional</strong>, 2023</li> </ul> <p> </p>
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS230322s Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS230322s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS230322s Preliminary
Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS230322s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2020arXivNicePaper
Foliar N, P and K global upscaled maps in woody plants
<p>Global foliar N, P and K maps in woody plants.</p> <p>Further details in: Vallicrosa, H., Sardans, J., Maspons, J., Zuccarini, P., Fernández-Martínez, M., Bauters, M., Goll, D.S., Ciais, P., Obersteiner, M., Janssens, I.A. and Peñuelas, J. (2022), Global maps and factors driving forest foliar elemental composition: the importance of evolutionary history. New Phytol, 233: 169-181. <a href="https://doi.org/10.1111/nph.17771">https://doi.org/10.1111/nph.17771</a></p>
STEM and EDX maps of biomass-MnO2 assemblages
<p>Raw images and maps described in:</p> <p>Contaminant loading and competitive access of Pb, Zn and Mn(III) to vacancy sites in biogenic MnO<sub>2</sub></p> <p>Julia Gonzalez Holguera<sup>1</sup>, Imelda Dossou Etui<sup>1</sup>, Jasquelin Peña<sup>1*</sup></p>
Submitted Completed pointings to the Gravitational Wave Treasure Map for event TEST_EVENT
Attached in a .json file is the completed pointing information for 14 observation(s) for the EM counterpart search associated with the gravitational wave event TEST_EVENT. These observations were taken on the DLT40 instrument.
DeepREM: Deep-Learning-Based Radio Environment Map Estimation from Sparse Measurements
<p><strong>DeepREM: Deep-Learning-Based Radio Environment Map Estimation from Sparse Measurements</strong></p> <p>DeepREM combines two deep-learning models (U-Net and CGAN) that estimate Radio Environment Maps (REMs) from sparse measurements. In this repository, we present the dataset and the interactive app developed to use the resulting models derived from the research. </p> <p><strong>Urban REMs dataset: </strong>We present a (REM) dataset of urban scenarios. Each map provides coverage information in areas from 2290 x 3670 m<sup>2</sup> to 3810 x 5160 m<sup>2</sup> with a resolution of 10 m. Coverage areas are sampled from Colombian cities (Armenia, Bogota, Cali, Ibague, Manizales, Medellin, and Pasto) and U.S. cities (Columbus and Washington). The simulations include topographic and building vector database information and Intelligent Ray-Tracing as a propagation model.</p> <p>In our <strong>second version</strong>, 400 new REMs were added to the dataset, including 4 new city areas with different topographic and building features (100 REMs for each area). The new cities are Barranquilla, Bucaramanga, Popayan, and North Pasto, all in Colombia. In addition, we also present an interactive application developed in the Streamlit framework to test CGAN and UNET performance in RSRP and BS coverage predictions either with REMs from our dataset or with completely new user-supplied REMs.The repository of the app can be downloaded at the following link: <a href="https://gitlab.com/andreajohanacv/deeprem">DeepREMapp</a></p>
Ostrinia population genomics: Custom scripts and synteny mapping results
<p>We sampled seven <em>Ostrinia furnacalis </em>(Guenée) populations across 23 degrees of latitude in China to elucidate the genetic basis of diapause variation and evolutionary mechanisms driving parallel clinal responses. Using pooled whole-genome sequencing (Pool-seq) data, population genomic analyses revealed hundreds of single nucleotide polymorphisms (SNP) whose allele frequencies covaried with mean diapause phenotypes along the cline. Archived on Dryad are the custom R scripts used in the population genomic analyses as well as the mapping of <em>O. furnacalis</em> scaffolds to <em>Bombyx mori</em> reference chromosomes (for visualizing population genetic statistics across chromosomes).</p>
52 Block--63k or 50k scale Maps of South Asia
<p>73 maps of the Survey of India covering block 52.</p>
D6.1 - Report on non-technical barrier and legal issues - Literature-mapping database
<p>This data set presents a literature map for D6.1 "Report on non-technical barrier and legal issues" considering relevant non-technical and legal challenges for consideration during the deployment of a pay-per-wash business model in European.</p>
Review data for: SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping
<p>Climatology of snow water equivalent of Switzerland between winters 1962 and 2021. Obtained using quartile mapping between a model using data assimilation since 1998 and a model without data assimilation. This version of the dataset corresponds to the publication revision time. The publication has been submitted to GMD Copernicus journal as: <em>SnowQM 1.0: A fast R Package for bias-correcting spatial fields of snow water equivalent using quantile mapping</em></p>
126k Maps of South Asia
<p>A set of about 2,022 historical maps of South Asia produced by the Survey of India, at a scale of 2 miles per inch (1:126,720). These maps cover the territory from Eastern Persia (Iran) through present day Afghanistan, Pakistan, India and Burma, and in general were produced during the first half of the 20th century. Each map is a half degree wide by a half degree high, and thus covers the area of one quarter of a 1 degree x 1 degree block.</p> <p>The file naming convention is to first give the <strong><em>number</em></strong> of the 4 degree x 4 degree block followed by the <strong><em>letter (A to P)</em></strong> of the sixteen 1 degree x 1 degree blocks in each 4 degree block, followed by the designation NE, NW, SE or SW to designate one of the four quarters of the one degree block--eg. 38 D SW. This is followed by the name of a town or geographical area encompassed by the map, followed by the <strong><em>date of publication</em></strong> in parenthesis. If a map is reprinted without changes sometime after its initial publication, it is the date of initial publication that is used.</p> <p>An <strong><em>Index Map</em></strong> with the title "0 Topographical Indexes of Survey of India 253k and 126k" showing the sheet numbering system is provided as the first entry in the list of files. This index map can be downloaded to assist in selecting particular maps of interest to the viewer. The maps are then listed in numerical order by sheet number. More than one map will be shown for each geographic block when maps with different publication dates are available.</p> <p>The viewer may find it convenient to use the Search Function (Windows: Ctrl F or Mac: Cmd F) to quickly navigate to a particular map of interest. A click on the "Preview" button will open a preview image, but it will be located at the very top of the file list. A click on the "Download" button will download the map file to the viewer's computer. </p> <p>The map scans are collected from many sources, and the quality of the scans as well as the underlying paper maps is uneven. Some scans are professionally done, while others are of poor quality. On the assumption that 'some map is better than no map,' we include the poor quality scans until a better copy can be obtained.</p>
63k Maps of Nepal
<p>A special set of 250 Nepal maps produced by the Survey of India from 1957 to 1975, at a scale of 1 inch per mile (63, 360). These maps were based on 1:40,000 scale aerial photographs taken between 1956 and 1958. Terrain is represented by contours at 100 feet intervals, with hill shading. </p> <p>The naming convention for these maps is the same as that used by the Survey of India for other South Asian maps. The <strong><em>number</em></strong> of the 4 degree x 4 degree block is given first, followed by the <strong><em>letter (A to P)</em></strong> of the sixteen 1 degree x 1 degree blocks in each 4 degree block eg. 38 D, and this is followed by a "]" symbol to indicate a slash and then a <strong><em>number</em></strong> from 1 to 16 to indicate the number of the map in the 1 degree block. This <strong><em>Number Letter Number</em></strong> designation is followed by the word '<strong><em>Nepal</em></strong>' to indicate this is part of a special set of Nepal maps, and then comes the <strong><em>date of publication</em></strong> in parenthesis. </p> <p>Two different index maps are provided. The first with the title "0 Nepal 63k map Index" is taken from Harka Gurung's <em>Maps of Nepal. </em>A second index with the title "0 Index to One Inch Sheets of Nepal by Survey of India" provides a colour coded indication of the source of various maps. </p>
An Evaluation Framework for Mapping News Headlines to Event Classes in a Knowledge Graph
<p>News headline to event classes corpus derived from Wikidata.</p> <p>Github: https://github.com/mbouadeus/news-headline-event-linking</p>
Data in support of: `Two-Dimensional Strain Mapping with Scanning Precession Electron Diffraction: An Investigation into Data Analysis Routines'
<p>This upload contains data in support of a manuscript currently under review. More details to follow.</p>
Replication Package: A Systematic Mapping Study on Security in Configurable Safety-critical Systems Based on Product-Line Concepts
<p><strong>Welcome to the public repository for the additional content of the paper "A Systematic Mapping Study on Security in Configurable Safety-critical Systems Based on Product-Line Concepts", accepted at the ICSOFT 2023.</strong></p> <p>This repository provides additional information to the conducted mapping study, including the following files:</p> <ul> <li>fetched_results_ICSOFT2023.csv: sheet containing all papers fetched from IEEEXplore, Scopus, and the ACM Guide to Computing Literature.</li> <li>excluded_paper.csv: sheet containing all excluded papers related to safety-critical systems but not referring to security.</li> <li>analysis_sheet_ICSOFT2023.csv: sheet containing information regarding the analysis results of 44 included papers based on the extraction criteria.</li> </ul>
DATABASE OF THE SYSTEMATIC MAPPING OF SOCIAL SUPPORT ANS STIGMA IN POPULATION LIVING WITH HIV
<p>DATABASE OF THE SYSTEMATIC MAPPING OF SOCIAL SUPPORT ANS STIGMA IN POPULATION LIVING WITH HIV. IT WAS USED THE PRISMA (2020) METHODOLOGY.</p>
Multivariate mapping of ontogeny, taphonomy, and phylogeny to reconstruct problematic fossil taxa
<p>Exceptionally preserved fossils of soft-bodied organisms provide unique evidence of evolutionary history, but they are often contentious; different approaches frequently produce radically different reconstructions of taxa and their affinities. Conflict arises due to difficulties in disentangling the three non-independent factors that underlie all morphological variation within and between fossils: ontogeny, taphonomy, and phylogeny. Comparative data from extant organisms can be extremely powerful in this context, but is often difficult to apply given the multidimensionality of anatomical variation. Here we present a multivariate ordination method using discrete morphological character data from modern taxa at different ontogenetic and taphonomic stages (semaphoront and 'semataphonts'). Analysing multiple axes of morphological variation simultaneously allows us to visualise character combinations that are likely to exist in fossil specimens at intersecting stages of growth and decay, and thus constrain interpretation of fossils. Application to early vertebrates finds variation in fossil specimens to be accounted for by all three axes: primarily decay in <em>Mayomyzon</em>, ontogeny in <em>Priscomyzon</em>, and phylogeny in 'euphaneropoids' and <em>Palaeospondylus</em>. Our demonstration of empirical multifactorial variation underscores the need for multivariate approaches to fossil interpretation, especially non-biomineralized taxa. As such, this conceptual approach provides a new method for resolving enigmatic taxa throughout the fossil record.</p>
Supplementary Material to "Identifying major research themes in Sport Science" (Interactive Science Maps)
<p><strong>Links to interactive maps</strong></p> <p>(1) Link: <strong><a href="https://app.vosviewer.com/?map=https://drive.google.com/uc?id=182n1HEZygAoS_9Caxj83hi27y41N6SWm&item_size=4">World Map</a></strong> (<a href="https://tinyurl.com/2zufzslg">https://tinyurl.com/2zufzslg</a>)</p> <p>(2) Link: <strong><a href="https://app.vosviewer.com/?map=https://drive.google.com/uc?id=182n1HEZygAoS_9Caxj83hi27y41N6SWm&item_size=5&item_size_variation=0.4&scale=0.9">Sport Science Map</a></strong> (<a href="https://tinyurl.com/2fztyjhf">https://tinyurl.com/2fztyjhf</a>)</p>
ScienceDex guides
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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.