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1. Data from: Ecological diversification in an adaptive radiation of plants: the role of de novo mutation and introgression

   https://doi.org/10.6084/m9.figshare.24480499  

   Stone, Benjamin; Wessinger, Carolyn A. (January 2023, figshare)

   Data from: Stone and Wessinger 2023, "Ecological diversification in an adaptive radiation of plants: the role of de novo mutation and introgression"DOI: 10.1101/2023.11.01.565185The code used to conduct analyses from this study can be found here: https://github.com/benstemon/MBE-23-0936The raw sequencing reads generated from this study have been deposited on the SRA under Project number: PRJNA1057825This repository contains a README.md file, which contains information on all files included. 

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2. Automatically Reproducing Timing-Dependent Flaky-Test Failures

   https://doi.org/10.5281/zenodo.7041599  

   Shanto Rahman, Aaron Massey (January 2023, Zenodo)

   This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake   Contents:   Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz  Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven) Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset). Output from running FlakeRake on each flaky test in the FlakeFlagger dataset: For bisection mode: results-bis.tgz For one-by-one mode: results-obo.tgz Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz  Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv 

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3. Sequential change‐point detection: Computation versus statistical performance

   https://doi.org/10.1002/wics.1628  

   Wang, Haoyun; Xie, Yao (July 2023, WIREs Computational Statistics)

   Abstract Change‐point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large‐scale, high‐dimensional, and complex streaming data call for computationally (memory) efficient sequential change‐point detection algorithms that are also statistically powerful. This gives rise to a computation versus statistical power trade‐off, an aspect less emphasized in the past in classic literature. This tutorial takes this new perspective and reviews several sequential change‐point detection procedures, ranging from classic sequential change‐point detection algorithms to more recent non‐parametric procedures that consider computation, memory efficiency, and model robustness in the algorithm design. Our survey also contains classic performance analysis, which provides useful techniques for analyzing new procedures. This article is categorized under:Statistical Models > Time Series ModelsAlgorithms and Computational Methods > AlgorithmsData: Types and Structure > Time Series, Stochastic Processes, and Functional Data 

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4. Syllable as a unit of information transfer in linguistic communication: The entropy syllable parsing model

   https://doi.org/10.1002/wcs.1518  

   Malaia, Evie A.; Wilbur, Ronnie B. (September 2019, WIREs Cognitive Science)

   Abstract To understand human language—both spoken and signed—the listener or viewer has to parse the continuous external signal into components. The question of what those components are (e.g., phrases, words, sounds, phonemes?) has been a subject of long‐standing debate. We re‐frame this question to ask: What properties of the incoming visual or auditory signal are indispensable to eliciting language comprehension? In this review, we assess the phenomenon of language parsing from modality‐independent viewpoint. We show that the interplay between dynamic changes in the entropy of the signal and between neural entrainment to the signal at syllable level (4–5 Hz range) is causally related to language comprehension in both speech and sign language. This modality‐independent Entropy Syllable Parsing model for the linguistic signal offers insight into the mechanisms of language processing, suggesting common neurocomputational bases for syllables in speech and sign language. This article is categorized under:Linguistics > Linguistic TheoryLinguistics > Language in Mind and BrainLinguistics > Computational Models of LanguagePsychology > Language 

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5. UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC) Data Archive and Biodiversity Dataset Graph

   https://doi.org/10.5281/zenodo.5660088  

   Cheadle Center for Biodiversity and Ecological Restoration, University of (January 2021, Zenodo)

   {"Abstract":["A biodiversity dataset graph: UCSB-IZC<\/p>\n\nThe intended use of this archive is to facilitate (meta-)analysis of the UC Santa Barbara Invertebrate Zoology Collection (UCSB-IZC). UCSB-IZC is a natural history collection of invertebrate zoology at Cheadle Center of Biodiversity and Ecological Restoration, University of California Santa Barbara.<\/p>\n\nThis dataset provides versioned snapshots of the UCSB-IZC network as tracked by Preston [2,3] between 2021-10-08 and 2021-11-04 using [preston track "https://api.gbif.org/v1/occurrence/search/?datasetKey=d6097f75-f99e-4c2a-b8a5-b0fc213ecbd0"].<\/p>\n\nThis archive contains 14349 images related to 32533 occurrence/specimen records. See included sample-image.jpg and their associated meta-data sample-image.json [4].<\/p>\n\nThe images were counted using:<\/p>\n\n$$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\\\n | grep -o -P ".*depict"\\\n | sort\\\n | uniq\\\n | wc -l<\/p>\n\nAnd the occurrences were counted using:<\/p>\n\n$$ preston cat hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c\\\n | grep -o -P "occurrence/([0-9])+"\\\n | sort\\\n | uniq\\\n | wc -l<\/p>\n\nThe archive consists of 256 individual parts (e.g., preston-00.tar.gz, preston-01.tar.gz, ...) to allow for parallel file downloads. The archive contains three types of files: index files, provenance files and data files. Only two index and provenance files are included and have been individually included in this dataset publication. Index files provide a way to links provenance files in time to establish a versioning mechanism.<\/p>\n\nTo retrieve and verify the downloaded UCSB-IZC biodiversity dataset graph, first download preston-*.tar.gz. Then, extract the archives into a "data" folder. Alternatively, you can use the Preston [2,3] command-line tool to "clone" this dataset using:<\/p>\n\n$$ java -jar preston.jar clone --remote https://archive.org/download/preston-ucsb-izc/data.zip/,https://zenodo.org/record/5557670/files,https://zenodo.org/record/5557670/files/5660088<\/p>\n\nAfter that, verify the index of the archive by reproducing the following provenance log history:<\/p>\n\n$$ java -jar preston.jar history\n<urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .\n<hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c> <http://purl.org/pav/previousVersion> <hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36> .<\/p>\n\nTo check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.<\/p>\n\n$ java -jar preston.jar verify\nhash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c    file:/home/jhpoelen/ucsb-izc/data/ce/1d/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c    OK    CONTENT_PRESENT_VALID_HASH    66438    hash://sha256/ce1dc2468dfb1706a6f972f11b5489dc635bdcf9c9fd62a942af14898c488b2c\nhash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844    file:/home/jhpoelen/ucsb-izc/data/f6/8d/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844    OK    CONTENT_PRESENT_VALID_HASH    4093    hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844\nhash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef    file:/home/jhpoelen/ucsb-izc/data/3e/70/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef    OK    CONTENT_PRESENT_VALID_HASH    5746    hash://sha256/3e70b7adc1a342e5551b598d732c20b96a0102bb1e7f42cfc2ae8a2c4227edef\nhash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b    file:/home/jhpoelen/ucsb-izc/data/99/58/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b    OK    CONTENT_PRESENT_VALID_HASH    6147    hash://sha256/995806159ae2fdffdc35eef2a7eccf362cb663522c308aa6aa52e2faca8bb25b<\/p>\n\nNote that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston".<\/p>\n\nFiles in this data publication:<\/p>\n\n--- start of file descriptions ---<\/p>\n\n-- description of archive and its contents (this file) --\nREADME<\/p>\n\n-- executable java jar containing preston [2,3] v0.3.1. --\npreston.jar<\/p>\n\n-- preston archive containing UCSB-IZC (meta-)data/image files, associated provenance logs and a provenance index --\npreston-[00-ff].tar.gz<\/p>\n\n-- individual provenance index files --\n2a5de79372318317a382ea9a2cef069780b852b01210ef59e06b640a3539cb5a<\/p>\n\n-- example image and meta-data --\nsample-image.jpg (with hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c)\nsample-image.json (with hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844)<\/p>\n\n--- end of file descriptions ---<\/p>\n\n\nReferences<\/p>\n\n[1] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-11-04 as indexed by the Global Biodiversity Informatics Facility (GBIF) with provenance hash://sha256/d5eb492d3e0304afadcc85f968de1e23042479ad670a5819cee00f2c2c277f36 hash://sha256/80c0f5fc598be1446d23c95141e87880c9e53773cb2e0b5b54cb57a8ea00b20c.\n[2] https://preston.guoda.bio, https://doi.org/10.5281/zenodo.1410543 .\n[3] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132\n[4] Cheadle Center for Biodiversity and Ecological Restoration (2021). University of California Santa Barbara Invertebrate Zoology Collection. Occurrence dataset https://doi.org/10.15468/w6hvhv accessed via GBIF.org on 2021-10-08. https://www.gbif.org/occurrence/3323647301 . hash://sha256/f68d489a9275cb9d1249767244b594c09ab23fd00b82374cb5877cabaa4d0844 hash://sha256/916ba5dc6ad37a3c16634e1a0e3d2a09969f2527bb207220e3dbdbcf4d6b810c<\/p>"],"Other":["This work is funded in part by grant NSF OAC 1839201 and NSF DBI 2102006 from the National Science Foundation."]} 

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