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8,537 results for “Openness”
Typha latifolia (Typhaceae) - fruit - section or open
Image of Typha latifolia (Typhaceae) - fruit - section or open
Triodanis perfoliata (Campanulaceae) - fruit - section or open
Image of Triodanis perfoliata (Campanulaceae) - fruit - section or open
Populus deltoides (Salicaceae) - fruit - section or open
Image of Populus deltoides (Salicaceae) - fruit - section or open
Tsuga canadensis (Pinaceae) - cone - female - mature open
Image of Tsuga canadensis (Pinaceae) - cone - female - mature open
Fagus grandifolia (Fagaceae) - fruit - section or open
Image of Fagus grandifolia (Fagaceae) - fruit - section or open
Platanus occidentalis (Platanaceae) - fruit - section or open
Image of Platanus occidentalis (Platanaceae) - fruit - section or open
Pinus strobus (Pinaceae) - cone - female - mature open
Image of Pinus strobus (Pinaceae) - cone - female - mature open
РИС. 7. Глохидии Amuranodonta kijaensis иЗ Зейского вдхр.: A. Раковина с Закрытыми створками. B, C. Раковина с открытыми створками, вид иЗнутри. D, Е. Створки, вид иЗнутри и сбоку. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. МасШтабные линейки: 100 мкм. FIG. 7. Glochidia of Amuranodonta kijaensis from Zeya Reservoir: A. Shell with closed valves. B, C. Shells with open valves, interior view. D, Е. Valves, interior and side views. SEM. Scale bars: 100 µm. in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 7. Глохидии Amuranodonta kijaensis иЗ Зейского вдхр.: A. Раковина с Закрытыми створками. B, C. Раковина с открытыми створками, вид иЗнутри. D, Е. Створки, вид иЗнутри и сбоку. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. МасШтабные линейки: 100 мкм. FIG. 7. Glochidia of Amuranodonta kijaensis from Zeya Reservoir: A. Shell with closed valves. B, C. Shells with open valves, interior view. D, Е. Valves, interior and side views. SEM. Scale bars: 100 µm.
РИС. 3. Раковины Зрелых глохидиев Beringiana beringiana в раЗных ракурсах: А–С – Закрытые раковины, вид со стороны створки (А), вентрального угла (В) и лигамента (С); D–F – полностью открытые раковины, вид иЗнутри (D), снаруЖи (E) и боковых краев створок (F); G, H – приоткрытые раковины, стрелка укаЗывает на остатки волокон мускулаЗамыкателЯ. МасштабнаЯ линейка 100 мкм. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. FIG. 3. Mature glochidial shells of Beringiana beringiana from different angles of view: A–C – closed shells, view from the valve side (A), ventral angle (B), and ligament (C); D–F – open shells, interior view (D), exterior view (E), and from lateral margins of valves (F); G, H – ajar shells, the arrows indicate the remains of the adductor muscle fibers. Scale bar 100 µm. Scanning electron microscopy. in Первые данные о морфологии глохидиев двустворчатых моллюсков Beringiana beringiana (Bivalvia, Unionidae) оЗера Дальнее, Камчатка
РИС. 3. Раковины Зрелых глохидиев Beringiana beringiana в раЗных ракурсах: А–С – Закрытые раковины, вид со стороны створки (А), вентрального угла (В) и лигамента (С); D–F – полностью открытые раковины, вид иЗнутри (D), снаруЖи (E) и боковых краев створок (F); G, H – приоткрытые раковины, стрелка укаЗывает на остатки волокон мускулаЗамыкателЯ. МасштабнаЯ линейка 100 мкм. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. FIG. 3. Mature glochidial shells of Beringiana beringiana from different angles of view: A–C – closed shells, view from the valve side (A), ventral angle (B), and ligament (C); D–F – open shells, interior view (D), exterior view (E), and from lateral margins of valves (F); G, H – ajar shells, the arrows indicate the remains of the adductor muscle fibers. Scale bar 100 µm. Scanning electron microscopy.
Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden"
<p>Data supporting the Master thesis "Monitoring von Open Data Praktiken - Herausforderungen beim Auffinden von Datenpublikationen am Beispiel der Publikationen von Forschenden der TU Dresden" (Monitoring open data practices - challenges in finding data publications using the example of publications by researchers at TU Dresden) - Katharina Zinke, Institut für Bibliotheks- und Informationswissenschaften, Humboldt-Universität Berlin, 2023</p> <p>This ZIP-File contains the data the thesis is based on, interim exports of the results and the R script with all pre-processing, data merging and analyses carried out. The documentation of the additional, explorative analysis is also available. The actual PDFs and text files of the scientific papers used are not included as they are published open access.</p> <p>The folder structure is shown below with the file names and a brief description of the contents of each file. For details concerning the analyses approach, please refer to the master's thesis (publication following soon).</p> <p>## Data sources </p> <p>Folder 01_SourceData/</p> <p>- PLOS-Dataset_v2_Mar23.csv (PLOS-OSI dataset)</p> <p>- ScopusSearch_ExportResults.csv (export of Scopus search results from Scopus)</p> <p>- ScopusSearch_ExportResults.ris (export of Scopus search results from Scopus)</p> <p>- Zotero_Export_ScopusSearch.csv (export of the file names and DOIs of the Scopus search results from Zotero)</p> <p>## Automatic classification </p> <p>Folder 02_AutomaticClassification/</p> <p>- (NOT INCLUDED) PDFs folder (Folder for PDFs of all publications identified by the Scopus search, named AuthorLastName_Year_PublicationTitle_Title) </p> <p>- (NOT INCLUDED) PDFs_to_text folder (Folder for all texts extracted from the PDFs by ODDPub, named AuthorLastName_Year_PublicationTitle_Title)</p> <p>- PLOS_ScopusSearch_matched.csv (merge of the Scopus search results with the PLOS_OSI dataset for the files contained in both)</p> <p>- oddpub_results_wDOIs.csv (results file of the ODDPub classification)</p> <p>- PLOS_ODDPub.csv (merge of the results file of the ODDPub classification with the PLOS-OSI dataset for the publications contained in both)</p> <p>## Manual coding </p> <p>Folder 03_ManualCheck/</p> <p>- CodeSheet_ManualCheck.txt (Code sheet with descriptions of the variables for manual coding)</p> <p>- ManualCheck_2023-06-08.csv (Manual coding results file)</p> <p>- PLOS_ODDPub_Manual.csv (Merge of the results file of the ODDPub and PLOS-OSI classification with the results file of the manual coding)</p> <p>## Explorative analysis for the discoverability of open data<br> <br>Folder04_FurtherAnalyses </p> <p>Proof_of_of_Concept_Open_Data_Monitoring.pdf (Description of the explorative analysis of the discoverability of open data publications using the example of a researcher) - in German</p> <p>## R-Script </p> <p>Analyses_MA_OpenDataMonitoring.R (R-Script for preparing, merging and analyzing the data and for performing the ODDPub algorithm)</p>
Data and scripts for the publication "A case for open communication of bugs in climate models"
<p>Primary data and scripts for the publication "A case for open communication of bugs in climate models" (submitted to GMDD as EGUSPHERE-2024-3493)</p>
The OpenITI Self-reuse Data
<p>This data set pertains to the OpenITI <a href="https://zenodo.org/records/10007820">2023.1.8 release</a> of the corpus and the corresponding <a href="https://zenodo.org/records/11501559">passim run</a>. The data will be analysed by Sarah Bowen Savant in a forthcoming monograph under contract with Edinburgh University Press.</p>
Data from "The academic impact of Open Science: a scoping review"
<p>These files include the data from the "The societal impact of Open Science - a scoping review", part of a series of studies conducetd within the PathOS Horizon Europe project on the academic, economic, and societal impacts of Open Science. This study was conducted in two phases. In phase 1 an academic database search was conducted. For phase 2 an automatic snowball search was performed based on results from phase 1 ( and grey literature was searched manually.</p> <p>The upload contains five files:</p> <ol> <li>Main file with extracted information for the 485 studies included in the review ("academic_impact_included_all_data.csv").</li> <li>Excel file documenting the grey literature search ("Grey_Literature_Search.xlsx").</li> <li>R project for the snowball search ("academic_impact_snowball.zip").</li> <li>R project for cleaning data and producing summaries and figures ("academic_impact_stats.zip").</li> <li>Excel file mapping the frascati codes used in file (1) to their textual representations ("Frascati definitions.xlsx")</li> </ol> <p>For more details on the methods see the <a href="https://osf.io/m4rnc">protocol</a> and its <a href="https://osf.io/3b6xj">addendum</a>. For the background, results and discussion see the <a href="../records/7883699">deliverable</a> (reporting on phase 1) and the pre-print.</p>
The macroeconomic determinants of trade openness in Latin American countries: A panel data analysis
<p><strong><span>Background:</span></strong><span> Trade openness shows a positive impact on economic growth, supported by economic theory, and export diversification and economic complexity show a positive dynamic in trade openness in the world; however, a specificity is generated in South American countries. Therefore, the objective of the research is to analyse the macroeconomic determinants of trade openness in Latin American countries.</span></p> <p><strong><span>Methods: </span></strong><span>The research approach was quantitative and explanatory using panel data methodology from the databases of the World Bank, Harvard University and the Economic Commission for Latin America and the Caribbean for the period 2000-2020.</span></p> <p><strong><span>Results: </span></strong><span>The fixed effects panel data model showed that the variables that had a negative impact on trade openness were GDP, the economic complexity index and the logistic performance index, while the variables that had a positive impact were exports of high-tech products (a proxy for innovation), exports, imports, research and development expenditure and interregional trade in goods.</span></p> <p><strong><span>Conclusions: </span></strong><span>Therefore, during the analysis period of 2000-2020 in South America, based on the panel data analysis under fixed effects, a total of 8 countries had a negative impact on trade openness, and only the economies of Chile, French Guiana, and Brazil had a positive impact on trade openness; these economies are characterized by their better performance in the economic complexity index, their higher percentage of budget for research and development expenses, and their trade policies oriented towards the industrialization of their value-added products.</span></p>
Data Extracted for the Systematic Literature Review on Non-profit Open data Intermediaries and their effects on Open data Usability Barriers
<p>The dataset contains the data extracted from the literature for the Systematic Literature Review and is referenced or used in the extended abstract titled "How do Non-profit Open data Intermediaries enhance Open data Usability? A Systematic Literature Review", submitted to the 18th International Symposium on Open Collaboration (Companion), September 6–10, 2022, Madrid, Spain. <a href="https://doi.org/10.1145/3555051.3555061" target="_blank" rel="noopener">https://doi.org/10.1145/3555051.3555061</a> </p>
Open-Set Tagging Dataset (OST)
<p>Open-set Tagging (OST) is a synthetic dataset of 1s clips used to evaluate source-centric representation learning models in the paper <a href="https://ieeexplore.ieee.org/document/10890242">Compositional Audio Representation Learning</a>.</p> <p>Due to the size of the dataset, we only share the source files, and provide the scripts to generate the dataset are available <a href="https://github.com/sripathisridhar/moads">here.</a><br><br>The dataset generation process is as follows:<br>1. From single-source FSD50K audio files, we generate a dataset of 10s soundscapes called Open-set Soundscapes (OSS) using <a href="https://github.com/justinsalamon/scaper">Scaper</a>.</p> <p>2. We then center a 1s window around the center of each sound event in the 10s soundscapes to generate Open-set Tagging (OST), which contains ~500k clips. </p> <p>If you are not going to use OSS, you can choose to synthesize it without audio-- this will synthesize only the <a href="https://github.com/marl/jams">JAMS</a> annotation files needed for the 1s clips. Using the OSS JAMS files, OST clips can be generated deterministically.</p> <p>There are five dataset variants (~17GB each), each with a different random assignment of classes to the known and unknown class categories. For further details, refer to our previous paper <a href="https://dcase.community/documents/workshop2023/proceedings/DCASE2023Workshop_Sridhar_11.pdf">Multi-label open-set audio classification</a>. In <a href="https://ieeexplore.ieee.org/document/10890242">this work</a>, OST dataset variant 1 is referred to as OST for simplicity. <br><br>We also introduce a tiny version of the dataset called OST-Tiny, which contains ~20k clips and only 10 known classes. This is convenient for faster prototyping and to evaluate models in a more challenging open-set classification scenario.</p> <p> </p>
Open Data SORTEE infographics
<p><em>CRediT (in alphabetical order by family name):</em></p> <ul> <li>Amin, Bawan (University College Dublin, Ireland): Conceptualization, Visualization, Writing – original draft, Writing – review & editing</li> <li>Burke, Samantha (University of New South Wales Sydney, Australia): Conceptualization, Visualization, Writing – original draft, Writing – review & editing</li> <li>Drobniak, Szymon (University of New South Wales Sydney, Australia): Visualization, Writing – review & editing</li> <li>Lagisz, Malgorzata (University of New South Wales Sydney, Australia): Visualization, Writing – review & editing</li> <li>Norton, Luke (University of the Witwatersrand, South Africa): Conceptualization, Visualization, Writing – original draft</li> <li>Pottier, Patrice (University of New South Wales Sydney, Australia): Conceptualization, Visualization, Writing – original draft, Writing – review & editing</li> </ul>
Open Code SORTEE infographics
<p>CRediT (in alphabetical order by family name):<br> - Amin, Bawan (University College Dublin, Ireland): Visualization, Writing – review & editing<br> - Burke, Samantha (University of New South Wales Sydney, Australia): Visualization, Writing – review & editing<br> - Drobniak, Szymon (University of New South Wales Sydney, Australia): Conceptualization, Visualization, Writing – original draft, Writing – review & editing<br> - Lagisz, Malgorzata (University of New South Wales Sydney, Australia): Conceptualization, Visualization, Writing – original draft, Writing – review & editing<br> - Pottier, Patrice (University of New South Wales Sydney, Australia): Visualization, Writing – review & editing<br> - Tam, Jessica Tin-Ying (University of New South Wales Sydney, Australia): Conceptualization, Visualization, Writing – original draft</p>
Survey on Developer and Researcher Views on the Ethics of Experiments on Open-Source Projects
<p>Results of a survey of 180 GitHub developers and 44 authors of research papers concerning the ethics of performing experiments on open source projects.</p>
Open-Source Terraform Repositories - SAST (tfsec, terrascan, checkov) vulnerability snapshot
<p>Vulnerability findings of open-source Terraform repositories in GitHub, collected with 3 static-code analysis tools (tfsec, terrascan, checkov).</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.