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8,537 results for “Openness”
Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"
<p><strong>Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"</strong><br> This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets.</p> <p><strong>Structure</strong><br> The structure of the replication kit is as follows:</p> <ul> <li><strong>additional_visualizations</strong>: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used</li> <li><strong>data_analysis</strong>: contains python scripts that we used to analyze our raw data</li> <li><strong>data_collection_tools</strong>: contains all source code used for the data collection, including the used versions of the <a href="https://github.com/comfort-framework">COMFORT framework</a>, the <a href="https://github.com/ftrautsch/BugFixClassifier">BugFixClassifier</a>, and the used tools of the <a href="https://github.com/smartshark">SmartSHARK environment</a>;</li> <li><strong>mongodb_no_authors</strong>: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.</li> </ul> <p><br> <strong>Additional Visualizations</strong><br> We provide two additional visualizations for each project:<br> 1) <project_name>\_disj\_ieee\_venn (visualizations for the DISJ data set)<br> 2) <project_name>\_all\_ieee\_venn (visualizations for the ALL data set)</p> <p>For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both).</p> <p>Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type.</p> <p><br> <strong>Analysis scripts</strong><br> Requirements:<br> - python3.5<br> - tabulate<br> - scipy<br> - seaborn<br> - mongoengine<br> - pycoshark<br> - pandas<br> - matplotlib</p> <p>Both python files contain all code for the statistical analysis we performed.</p> <p><strong>Data Collection Tools</strong><br> We provide all data collection tools that we have implemented and used throughout our paper:</p> <ul> <li><strong>BugFixClassifier</strong>: Used to classify our defects.</li> <li><strong>comfort-core</strong>: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for these tests.</li> <li><strong>comfort-jacoco-listner</strong>: Used to intercept the coverage collection process as we were executing the tests of our case study projects.</li> <li><strong>jSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Java).<strong> </strong></li> <li><strong>pycoSHARK</strong>: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Python).</li> <li><strong>tools-changedistiller</strong>: Version of ChangeDistiller that we used within our comfort-core framework.</li> <li><strong>vcsSHARK</strong>: Used to collect data from the VCSs of the projects.</li> </ul> <p> </p> <p> </p>
Open forest successional stages and landscape heterogeneity promote wild bee diversity in temperate forests
<p>Recent studies have emphasized forests as crucial habitat for wild bees. In Europe, most forests are managed following the principles of close‐to‐nature silviculture, which combine timber production and nature conservation. However, open late and early successional stages within these forests are largely missing, which could be important for wild bees. This highlights that close‐to‐nature silviculture alone might not be sufficient to conserve bees within temperate forests. Open structures such as canopy gaps and road verges in forests could improve habitat for bees. To provide management recommendations for wild bee conservation in temperate forests, we analyzed how components of bee beta diversity varied between forest management types and tested how open structures, namely clear‐cuts, canopy gaps, and forest road verges influenced bee abundance, richness, and diversity. In addition, we analyzed the abundance and percent of red‐listed bee species at different scales. Bees were sampled using 90 pan traps on 45 (1 ha) plots in 2019 and 2020 in the Black Forest, Germany. Plots were selected in 15 triplets each consisting of three management types related to different successional stages: unmanaged, close‐to‐nature, and small clear‐cut. Beta diversity was not consistently nested highlighting the importance of different management and successional stages within the landscape to support bees in forests. Abundance, species richness, and Shannon diversity of bees were highest on clear‐cuts, compared to unmanaged‐ and close‐to‐nature plots. At landscape scale, wild bee abundance increased with canopy openness while wild bee diversity increased with landscape heterogeneity. Abundance‐ and percent of red‐listed bee species increased with the length of forest road verges. We advocate creating habitats at local scales which offer flowering and nesting resources by providing canopy gaps. At landscape scale, heterogeneity created through different forest successional stages is needed to conserve the entire community of wild bees.</p>
Workshop recording: Towards Implementation of the UNESCO Recommendation on Open Science – How to integrate citizen science into (open) science policy
<p>Recording of the workshop: Towards Implementation of the UNESCO Recommendation on Open Science – How to integrate citizen science into (open) science policy. If you have any questions or comments, please contact the workshop organisers Uta Wehn (<a href="mailto:u.wehn@un-ihe.org">u.wehn@un-ihe.org</a>) and Libby Hepburn (<a href="mailto:libby@atlasoflife.org.au">libby@atlasoflife.org.au</a>).</p>
FIGURES – 7. SEM images of Craspedostauros laevissimus from different populations. 42. Close up of the central area externally, showing the weakly expanded central raphe endings and the cribrate areolae with usually four to five peripheral pores and up to one central pore (sample 13). 43. Close up of the central area internally, showing the narrow stauros located in a wider hyaline area, the central raphe endings with double helictoglossa, and the rounded to rectangular areolar openings internally (sample 13). 44. Close up of the apex, showing the bent distal raphe endings, cribrate areoale with up to six peripheral pores and up to two central pores (sample 13). 45. Close ups of both the valve exterior and interior (sample 11), showing the weakly expanded central raphe endings externally; the areolae near the raphe with usually four to five peripheral pores and one central pore; the central raphe endings with double helictoglossa and the rounded areolar openings internally. 46. Internal valve view, showing the narrow transverse rib of silica at the valve center (strauros) and the central raphe endings with double helictoglossa (sample 11). 47. Close up of the apex of the same valve internally, showing the rounded to square or rectangular areolar openings (sample 11). Scale bars = 5 μm (Fig. 46); 1 μm (Figs 42–45,47). in The genus Craspedostauros E.J.Cox (Bacillariophyta) on the coasts of Livingston Island, Maritime Antarctica
FIGURES – 7. SEM images of Craspedostauros laevissimus from different populations. 42. Close up of the central area externally, showing the weakly expanded central raphe endings and the cribrate areolae with usually four to five peripheral pores and up to one central pore (sample 13). 43. Close up of the central area internally, showing the narrow stauros located in a wider hyaline area, the central raphe endings with double helictoglossa, and the rounded to rectangular areolar openings internally (sample 13). 44. Close up of the apex, showing the bent distal raphe endings, cribrate areoale with up to six peripheral pores and up to two central pores (sample 13). 45. Close ups of both the valve exterior and interior (sample 11), showing the weakly expanded central raphe endings externally; the areolae near the raphe with usually four to five peripheral pores and one central pore; the central raphe endings with double helictoglossa and the rounded areolar openings internally. 46. Internal valve view, showing the narrow transverse rib of silica at the valve center (strauros) and the central raphe endings with double helictoglossa (sample 11). 47. Close up of the apex of the same valve internally, showing the rounded to square or rectangular areolar openings (sample 11). Scale bars = 5 μm (Fig. 46); 1 μm (Figs 42–45,47).
MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions
<p><strong>Please cite the following paper when using this dataset:</strong></p> <p>N. Thakur, “MonkeyPox2022Tweets: A large-scale Twitter dataset on the 2022 Monkeypox outbreak, findings from analysis of Tweets, and open research questions,” Infect. Dis. Rep., vol. 14, no. 6, pp. 855–883, 2022, DOI: https://doi.org/10.3390/idr14060087</p> <p><strong>Abstract</strong></p> <p>The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Therefore, this work presents an open-access dataset of <strong>571,831 Tweets</strong> about monkeypox that have been posted on Twitter since the first detected case of this outbreak on May 7, 2022. The dataset complies with the privacy policy, developer agreement, and guidelines for content redistribution of Twitter, as well as with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) principles for scientific data management.</p> <p> <strong>Data Description</strong></p> <p>The dataset consists of a total of <strong>571,831 Tweet IDs</strong> of the same number of tweets about monkeypox that were posted on Twitter from 7th May 2022 to 11th November (the most recent date at the time of uploading the most recent version of the dataset). The Tweet IDs are presented in 12 different .txt files based on the timelines of the associated tweets. The following represents the details of these dataset files.</p> <ul> <li>Filename: TweetIDs_Part1.txt (No. of Tweet IDs: 13926, Date Range of the associated Tweet IDs: May 7, 2022, to May 21, 2022)</li> <li>Filename: TweetIDs_Part2.txt (No. of Tweet IDs: 17705, Date Range of the associated Tweet IDs: May 21, 2022, to May 27, 2022)</li> <li>Filename: TweetIDs_Part3.txt (No. of Tweet IDs: 17585, Date Range of the associated Tweet IDs: May 27, 2022, to June 5, 2022)</li> <li>Filename: TweetIDs_Part4.txt (No. of Tweet IDs: 19718, Date Range of the associated Tweet IDs: June 5, 2022, to June 11, 2022)</li> <li>Filename: TweetIDs_Part5.txt (No. of Tweet IDs: 46718, Date Range of the associated Tweet IDs: June 12, 2022, to June 30, 2022)</li> <li>Filename: TweetIDs_Part6.txt (No. of Tweet IDs: 138711, Date Range of the associated Tweet IDs: July 1, 2022, to July 23, 2022)</li> <li>Filename: TweetIDs_Part7.txt (No. of Tweet IDs: 105890, Date Range of the associated Tweet IDs: July 24, 2022, to July 31, 2022)</li> <li>Filename: TweetIDs_Part8.txt (No. of Tweet IDs: 93959, Date Range of the associated Tweet IDs: August 1, 2022, to August 9, 2022)</li> <li>Filename: TweetIDs_Part9.txt (No. of Tweet IDs: 50832, Date Range of the associated Tweet IDs: August 10, 2022, to August 24, 2022)</li> <li>Filename: TweetIDs_Part10.txt (No. of Tweet IDs: 39042, Date Range of the associated Tweet IDs: August 25, 2022, to September 19, 2022)</li> <li>Filename: TweetIDs_Part11.txt (No. of Tweet IDs: 12341, Date Range of the associated Tweet IDs: September 20, 2022, to October 9, 2022)</li> <li>Filename: TweetIDs_Part12.txt (No. of Tweet IDs: 15404, Date Range of the associated Tweet IDs: October 10, 2022, to November 11, 2022) </li> </ul> <p>Please note: The dataset contains only Tweet IDs in compliance with the terms and conditions mentioned in the privacy policy, developer agreement, and guidelines for content redistribution of Twitter. The Tweet IDs need to be hydrated to be used. For hydrating this dataset, the <a href="https://github.com/DocNow/hydrator/releases">Hydrator application</a> may be used (a step-by-step process on how to use Hydrator to hydrate this dataset is explained in the above-mentioned paper). </p>
Supplements and raw data for article to be published in Open Linguistics
<p>Supplements A, B and C of article (docx and pdf versions) :</p> <p>Emmanuel Cartier, Alexander Onysko*, Esme Winter-Froemel, Eline Zenner, Gisle Andersen, Béryl Hilberink-Schulpen, Ulrike Nederstigt, Elizabeth Peterson, and Frank van Meurs (2022). Linguistic repercussions of COVID-19:A corpus study on four languages, Open Linguistics 2022; 8:1-16.</p> <p>A supporting web exploration interface is available here : <a href="https://tal.lipn.univ-paris13.fr/neoveille/html/covid19_project/html/data_exploration.php">Link to web interface</a></p> <p>Raw data for the supplements and the web interface are here :</p> <p><a href="https://zenodo.org/api/files/d633542e-cdbc-4f4b-a43b-5bf7895b8a7c/raw_data_virus_names.tar.gz">raw_data_virus_names.tar.gz</a> : the raw data of the virus names (with a file for all languages, and a file per language).</p> <p><a href="https://zenodo.org/api/files/d633542e-cdbc-4f4b-a43b-5bf7895b8a7c/raw_data_virus_associated_words.tar.gz">raw_data_virus_associated_words.tar.gz</a> : the raw data of the virus names associated words (with a file for all languages, and a file per language).</p>
Code & Data for "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals"
<p>This entry contains code and data that was used in the publication: "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals" submitted in Publications journal.</p> <p>*It was version 2 when we added Fig_3_Tab2_Defining_science_disciplines_plus_plot.R script to version 1.</p> <p>*It was version 3 because we added script that calculates median and mean values of the stringency levels to version 2 data.</p> <p>*Latest version is version 4: we added supplementary data.</p> <p>#IDEA:</p> <p>This project was about analyzing policies of two thousand journals within the framework of eight TOP standards: <br> data citation, transparency of data, material, code and design and analysis, replication, plan and study pre-registration, <br> and two effective interventions: “Registered reports” and “Open science badges”. </p> <p># MATERIALS & METHODS<br> We downloaded the TOP Factor (v33, 2022-08-29 3:12 PM) metric from the https://osf.io/kgnva/files/osfstorage/5e13502257341901c3805317 <br> website and analyzed its content with an in-house R script (in this repo):<br> 1) SCRIPT: fig1_Analyzing_journals_policies_and_TOP_guidelines.R<br> 2) SCRIPT: Figure2a_b_TOP_impl_journal_statistist_0_1_piechart_barplot.R<br> In order to get statistics about implementation of the TOP guidelines across discipline-specific journals, <br> we extracted information about journal’s disciplines from the Scopus content database. <br> We downloaded SCOPUS content coverage from the https://www.elsevier.com/solutions/scopus/how-scopus-works/content?dgcid=RN_AGCM_Sourced_300005030 (existJuly2022.xlsx)<br> and used the first Sheet.<br> We identified match between those 2 tables: <br> 3) SCRIPT: Rscript_overlapping_TOP_dataframe_and_SCOPUS_db.R<br> And resulted in Overlap_SCOPUS_TOP.rds file<br> And performed visualization and statistics:<br> 4) SCRIPT: Fig_3_Tab2_Defining_science_disciplines_plus_plot.R</p> <p> </p> <p>#RESULTS Submitted to Publications 30.9.2022.</p> <p>Reviewed 2.11.2022.</p> <p>Latest version: 25.11.2022.</p>
Data accompanying "Calibrated microphone array recordings reveal that a gleaning bat emits low-intensity echolocation calls even in open-space habitat"
<p>Introduction to the dataset: </p> <p>In this manuscript, we analyse recordings of brown long-eared bats in the wild collected with a 4-microphone array, which we use to measure the distance between the bat and the microphone.<br> The distance is then used to correct the recordings for distance-dependent frequency and amplitude attenuation.<br> To ensure that our measurements and corrections are correct, we fully calibrated the microphone array and analysis set-up. <br> The calibration includes a comparison between real distances and calculated distances between a loudspeaker and the microphone array as well as comparisons between the real amplitude and the calculated amplitude of the loudspeaker.<br> To obtain the original amplitude of the calibration recording, we calibrated the loudspeaker as well</p> <p>In this folder, we provide the original bat recordings as well as the datasheet with all analysed call measurements, and the original data from the microphone calibration.</p> <p>DATA & FILE OVERVIEW</p> <p>Folder list: <br> - Bat data: folder containing the original Plecotus recordings and raw call parameters<br> - Calibration data : folder containing the array calibration raw data, as well as codes and recordings necessary to calibrate the loudspeaker. <br> </p>
Open data for "Programmable frequency-bin quantum states in a nano-engineered silicon device"
<p>The folder includes includes the raw data that were used for generation of all Figures in the paper "Programmable frequency-bin quantum states in a nano-engineered silicon device".</p>
FIGURE 3. Borreria savannicola. A. Floral branch. B. Stipular sheath. C. Flower. D. Opened corolla. E in Borreria savannicola (Spermacoceae, Rubiaceae), a new species from savanna patches within the Caatinga, Ceará State, Brazil
FIGURE 3. Borreria savannicola. A. Floral branch. B. Stipular sheath. C. Flower. D. Opened corolla. E. Hypanthium, calyx, style, and stigma. F. Capsule. G. Seed in ventral view. H. Seed in dorsal view. Illustration: L. Simón.
Open Plant Phenotyping Database Seedling Images
<p>The Open Plant Phenotyping Database [OPPD] is a public dataset for visual recognition tasks on images of plant seedlings. The dataset consists of 7,590 images with 315,038 plant objects, representing 64,292 individual plants from 47 different species. Each plant species has been cultivated using three growth conditions (ideal, drought and natural) and tracked temporally to achieve high intra-species variability.</p> <p>This is a subset of the .jpg images available at https://gitlab.au.dk/AUENG-Vision/OPPD/-/archive/master/OPPD-master.zip in the folder: /DATA/images_plants/1COMF/</p>
FIGURE 1 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 1. Megalothorax anterolenis sp. nov. (A) Chaetotaxy of trunk tergites from Th. I to Abd. IV, with representation of the secondary granulation. (B, C, D) Head, (B) dorsal side including labrum and maxillary palp, (C) ventral side including labium basal fields, (D) variant morphology of the connection between linea ventralis and integumentary channels.
FIGURE 6 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 6. Megalothorax zealanterolenis sp. nov. (A) Chaetotaxy of trunk tergites from Th. I to Abd. V, (B) Head dorsal side. (C) Mandibula. (D) Antenna.
FIGURE 5 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 5. Megalothorax tasmanterolenis sp. nov. (A) Chaetotaxy of antenna, arrow indicates a facultative chaeta. (B) Claw I. (C) Claw II. (D) Claw III. (E) Furca posterior side.
FIGURE 9 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 9. Megalothorax tasmanterolenis sp. nov. (A) Integument with secondary granules and area of enlarged primary hexagons devoid of secondary granules, abdominal region dorsal side. (B) Integument, with area of enlarged primary granules, dorso-anterior part of head. (C) Enlarged and clear S-chaeta Sa2 on Ant. IV, compared with (D) normal, dark S-chaetae (e.g. Sb3, Sb4, Sb5) on Ant. IV. Megalothorax zealanterolenis sp. nov. (E) mucro with focus on the smooth and waved internal posterior lamella.
FIGURE 4 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 4. Megalothorax tasmanterolenis sp. nov. (A) Chaetotaxy of trunk tergites from Th. I to Abd. IV, area of repartition of the secondary granulation represented in grey, (B) Head dorsal side. (C, D) anterior side of the labrum anterior process, (D) labrum posterior side. (E) Maxillary outer lobe. (F) Mandibula. (G) Maxilla.
FIGURE 2 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 2. Megalothorax anterolenis sp. nov. (A, B) Labrum anterior process, (A) posterior side, (B) anterior side. (C) Maxillary outer lobe internal side. (D) Mandibula. (E) Maxilla. (F, G) Antenna (F) posterior side, (G) anterior side. (H) Abd. VI tergite and sternite, Abd. V and IV sternites, and posterior side of manubrium and dens (furca), chaeta and lobe in dotted line are missing on the figured specimen but normally present. (I) dens anterior side. (J) Mucro.
FIGURE 8 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 8. (A) Diagram of dorsal head chaetotaxy, chaeta in blue present only in M. tasmanterolenis sp. nov. and M. zealanterolenis sp. nov., channel in blue present only in M. tasmanterolenis sp. nov. (B) Diagram of ventral head chaetotaxy. (C) Diagram of antenna chaetotaxy. (D) Body chaetotaxy diagram, s-chaetae shape based on M. anterolenis sp. nov. (E) Connection of integumentary channels and linea ventralis: (a) crossed, (b) transitional form, (c) circular.
FIGURE 7 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 7. Megalothorax zealanterolenis sp. nov. (A) chaetae of Abd. IV sternites. (B) Leg I. (C) Leg II. (D) Leg III. (E) Claw I. (F) Claw II. (G) Claw III. (H, I, J) Mucro variations.
FIGURE 10 in A new group of species of the genus Megalothorax (Collembola, Neelidae) with Gondwanan distribution, and introducing an open interactive identification key of Megalothorax species
FIGURE 10. Molecular phylogeny of the genus Megalothorax, with optimization of taxonomically relevant character states. New OTUs names are indicated in bold. Bootstrap node support is shown next to the node, only when below 100 %.
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.