Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Many-Body Models for Chirality-Induced Spin Selectivity in Electron Transfer. Open data set

Data supporting the original figures 1, 2, 3 and 4 of the related publication.

Artigo: a percepção de valor do cliente no contexto de open banking

Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil

openOct 2024View details →

SD4EO: Open Street Maps Northern France Patch Dataset

This dataset has been created as part of the deliverables for ESA’s <a href="https://eo4society.esa.int/projects/sd4eo/">SD4EO project</a>. It consists of orthogonal patches from Open Street Maps taken on 2024, which cover large areas in northern France. They corresponds to large Sentinel-2 Tile areas: T31TCJ, T31TCN, T31TDN, T31UDP, T31UEP, T30UWU, T31TCL, T31TDL, T31TDM, T31TEM, T31TEN, T31TFM, T31UDQ, T31UEQ, T30TXT, T30TYS, T30UXU, T30UYU, T31TFN, T31UFP, T31UFQ and T30TVK. Each patch covers an area of approximately 1700x1700 meters (though the exact size may vary slightly with latitude). Patches located at the edges of Sentinel-2 tiles were excluded to avoid potential cutting or distortion issues. Likewise, patches near large bodies of water, including coastlines, were also discarded. It's important to note that, unlike the synthetic image dataset [link], in this case, the patches are always disjoint. To facilitate the use of these images for training generative AI models (such as GANs and Diffusion models), the patch size has been standardized to 512x512 pixels, providing an effective resolution of 3.3 meters per pixel. To simplify their usage, the images are stored in lossless PNG format. Each patch is also accompanied by real RGB+NIR pixel-by-pixel correspondent satellite images from Sentinel-2 in the sibling dataset <a href="https://zenodo.org/records/13957879">doi:10.5281/zenodo.13957879</a> Both this dataset and the Sentinel-2 dataset have been used to train a conditional diffusion model, which generated a synthetic dataset of 46.5 GB that can be accessed at this [<a href="https://zenodo.org/records/13208361">link</a>]. The file naming convention is straightforward:                        Patch_<Tile Name>_<Column Number>_<Row Number>_OSM.png Columns and rows are always referenced within the same tile.   The data from OpenStreetMap (OSM) is licensed under the Open Database License (ODbL). This license allows you to freely use, modify, and distribute the data, provided we follow two key conditions: <ol> <li>Attribution: We must give appropriate credit to OpenStreetMap and its contributors when using their data.</li> <li>Share-Alike: If we modify or build upon the data, you must distribute your derivative works under the same ODbL license.</li> </ol> As a derivative dataset, we share it under the same terms. This ensures that any improvements or enhancements made to the data remain open and available to the public.   The SD4EO Project is funded by ESA’s FutureEO programme under contract no. 4000142334/23/I-DT and is supervised by ESA Φ-lab.

Input Files and Processed Results for FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows

Input Files (StartingFiles.zip) and Processed Results (FreeMHDPaperAllFigures.zip) used to make paper figures.    FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows The extreme heat fluxes in the divertor region of tokamaks may require an alternative to solid plasma-facing components, for the extraction of heat and the protection of the surrounding walls. Flowing liquid metals are proposed as an alternative, but raise additional challenges that require investigation and numerical simulations. Free surface designs are desirable for plasma-facing components (PFCs), but steady flow profiles and surface stability must be ensured to limit undesirable interactions with the plasma. Previous studies have mainly used steady-state, 2D, or simplified models for internal flows and have not been able to adequately model free-surface liquid metal (LM) experiments. Therefore, FreeMHD has been recently developed as an open-source magnetohydrodynamics (MHD) solver for free-surface electrically conductive flows subject to a strong external magnetic field. The FreeMHD solver computes incompressible free-surface flows with multi-region coupling for the investigation of MHD phenomena involving fluid and solid domains. The model utilizes the finite-volume OpenFOAM framework under the low magnetic Reynolds number approximation. FreeMHD is validated using analytical solutions for the velocity profiles of closed channel flows with various Hartmann numbers and wall conductance ratios. Next, experimental measurements are then used to verify FreeMHD, through a series of cases involving dam breaking, 3D magnetic fields, and free-surface LM flows. These results demonstrate that FreeMHD is a reliable tool for the design of LM systems under free surface conditions at the reactor scale. Furthermore, it is flexible, computationally inexpensive, and can be used to solve fully 3D transient MHD flows.

Care to Share: Dataset and resources for Dutch National Open Science Festival hackathon

This dataset contains the materials used in the session "Care to Share? Investigating Open Science practices adoption among researchers: a hackathon" presented at the Dutch National Open Science Festival on 22nd October 2024. The data files are derived from: Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 ad contains two additional fields (Dimensions_Country and Dimensions_FoR) from Dimensions obtained on 15 October 2024, from Digital Science’s Dimensions platform, available at https://app.dimensions.ai.  <h2>File list:</h2> PLOS-Dataset-for-Hackathon.xlsx Data pertaining to the PLOS corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai.  Comparator-Dataset-for-Hackathon.xlsx Data pertaining to the Comparator corpus of articles derived from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686 with additional data from Dimensions.ai. Care to share resource sheet.pdf Document outlining the questions to be investigated during the hackathon as well as key information about the dataset. OSI-Column-Descriptions_v3_Dec23.pdf This file is taken from Public Library of Science (2022) PLOS Open Science Indicators. Figshare. Dataset (version 8). https://doi.org/10.6084/m9.figshare.21687686. It describes the fields used in the two data files with the exception of Dimensions_Country and Dimensions_FoR. Descriptions for these are listed in the README tabs of the data files.

Datasets and scripts related to the paper: "Can Generative AI Help us in Open Coding of Software Engineering Data?"

This replication package contains datasets and scripts related to the paper: "Can Generative AI Help us in Open Coding of Software Engineering Data?" The replication package is organized into two directories: <ul> <li> <code>manual_analysis</code>: This directory contains all sheets used to perform the manual analysis for RQ1, RQ2, and RQ3. </li> <li> <code>stats</code>: This directory contains all datasets, scripts, and results metrics used for the quantitative analyses of RQ1 and RQ2. </li> </ul> In the following, we describe the content of each directory: <h2>manual_analysis</h2> <ul> <li> <code>manual_analysis_rq1</code>: This directory contains all sheets used to perform manual analysis for RQ1 (independent and incremental coding). <ul> <li> The sub-directory <code>incremental_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_incremental.csv</code>, <code>DL_Faults_ISSUE_incremental.csv</code>, <code>DL_Fault_SO_incremental.csv</code>, <code>DRL_Challenges_incremental.csv</code> and <code>Functional_incremental.csv</code>). All these .csv files contain the following columns: <ul> <li>Link: The link to the instances</li> <li>Prompt: Prompt used as input to GPT-4-Turbo</li> <li>ID: Instance ID</li> <li>FinalTag: Tag assigned by the human in the original paper</li> <li>Chatgpt_output_memory: Output of GPT-4-Turbo with incremental coding</li> <li>Chatgpt_output_memory_clean: (only for the DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li>Author1: Label assigned by the first author</li> <li>Author2: Label assigned by the second author</li> <li>FinalOutput: Label assigned after the resolution of the conflicts</li> </ul> </li> <li> The sub-directory <code>independent_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_independent.csv</code>, <code>DL_Faults_ISSUE_ independent.csv</code>, <code>DL_Fault_SO_ independent.csv</code>, <code>DRL_Challenges_ independent.csv</code> and <code>Functional_ independent.csv</code>), containing the following columns: <ul> <li>Link: The link to the instances</li> <li>Prompt: Prompt used as input to GPT-4-Turbo</li> <li>ID: Specific ID for the instance</li> <li>FinalTag: Tag assigned by the human in the original paper</li> <li>Chatgpt_output: Output of GPT-4-Turbo with independent coding</li> <li>Chatgpt_output_clean: (only for DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li>Author1: Label assigned by the first author</li> <li>Author2: Label assigned by the second author</li> <li>FinalOutput: Label assigned after the resolution of the conflicts.</li> </ul> </li> <li> Also, the sub-directory contains sheets with inconsistencies after resolving conflicts. The directory <code>inconsistency_incremental_coding</code> contains .csv files with the following columns: <ul> <li>Dataset: The dataset considered</li> <li>Human: The label assigned by the human in the original paper</li> <li>Machine: The label assigned by GPT-4-Turbo</li> <li>Classification: The final label assigned by the authors after resolving the conflicts. Multiple classifications for a single instance are separated by a comma “,”</li> <li>Final: final label assigned after the resolution of the incompatibilities</li> </ul> </li> <li> Similarly, the sub-directory <code>inconsistency_independent_coding</code> contains a .csv file with the same columns as before, but this is for the case of independent coding. </li> </ul> </li> <li> <code>manual_analysis_rq2</code>: This directory contains .csv files for all datasets (<code>DL_Faults_redundant_tag.csv</code>, <code>DRL_Challenges_redundant_tag.csv</code>, <code>Functional_redundant_tag.csv</code>) to perform manual analysis for RQ2. <ul> <li> The <code>DL_Faults_redundant_tag.csv</code> file contains the following columns: <ul> <li>Tags Redundant: tags identified as redundant by GPT-4-Turbo</li> <li>Matched: inspection by the authors to see if the tags are redundant matching or not</li> <li>FinalTag: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> The <code>Functional_redundant_tag.csv</code> file contains the same columns as before </li> <li> The <code>DRL_Challenges_redundant_tag.csv</code> file is organized as follows: <ul> <li>Tags Suggested: The final tag suggested by GPT-4-Turbo</li> <li>Tags Redundant: tags identified as redundant by GPT-4-Turbo</li> <li>Matched: inspection by the authors to see if the tags redundant matching or not with the tags suggested</li> <li>FinalTag: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> The sub-directory <code>code_consolidation_mapping_overview</code> contains .csv files (<code>DL_Faults_rq2_overview.csv</code>, <code>DRL_Challenges_rq2_overview.csv</code>, <code>Functional_rq2_overview.csv</code>) organized as follows: <ul> <li>Initial_Tags: list of the unique initial tags assigned by GPT-4-Turbo for each dataset</li> <li>Mapped_tags: list of tags mapped by GPT-4-Turbo</li> <li>Unmatched_tags: list of unmatched tags by GPT-4-Turbo</li> <li>Aggregating_tags: list of consolidated tags</li> <li>Final_tags: list of final tags after the consolidation task</li> </ul> </li> </ul> </li> <li> <code>prompt_for_each_rq</code>: This directory contains: - (i) the history of prompts used in each dataset (<code>prompts_history.txt</code>) -(ii) all final prompt used for the analysis of each dataset, prompt used for incremental coding, prompt used in rq2 to consolidate redundant codes, prompt used in rq3 to create taxonomy (<code>generic_prompt.txt</code>) -(iii) all .csv files in which there are indicate, for each dataset, the link and the prompt used (<code>prompt_DL_Faults_COMMIT.csv</code>, <code>prompt_DL_Faults_ISSUE.csv</code>, <code>prompt_DL_Faults_SO.csv</code>, <code>prompt_DRL_Challenges.csv</code>). For the Functional Dataset .csv file contains, instead, Question, Answer and Prompt used (<code>prompt_Functional.csv</code>) </li> <li> <code>rq3</code>: This directory contains the taxonomies obtained from GPT-4-Turbo for the DL Faults and for the DRL Challenges (<code>taxonomy_DL_Faults.txt</code>,<code>taxonomy_DRL_Challenges.txt</code>) </li> </ul> <h2>stats</h2> <ul> <li> <code>RQ1</code>: contains script and datasets used to perform metrics for RQ1. The analysis calculates all possible combinations between Matched, More Abstract, More Specific, and Unmatched. <ul> <li><code>RQ1_Stats.ipynb</code> is a Python Jupyter nooteook to compute the RQ1 metrics. To use it, as explained in the notebook, it is necessary to change the values of variables contained in the first code block.</li> <li><code>independent-prompting</code>: Contains the datasets related to the independent prompting. Each line contains the following fields: <ul> <li>Link: Link to the artifact being tagged</li> <li>Prompt: Prompt sent to GPT-4-Turbo</li> <li>FinalTag: Artifact coding from the replicated study</li> <li>chatgpt_output_text: GPT-4-Turbo output</li> <li>chatgpt_output: Codes parsed from the GPT-4-Turbo output</li> <li>Author1: Annotator 1 evaluation of the coding</li> <li>Author2: Annotator 2 evaluation of the coding</li> <li>FinalOutput: Consolidated evaluation</li> </ul> </li> <li><code>incremental-prompting</code>: Contains the datasets related to the incremental prompting (same format as independent prompting)</li> <li><code>results</code>: contains files for the RQ1 quantitative results. The files are named <code>RQ1\_<<Dataset>>\_<<Prompt method>>\_<<ExcludingNegative>>\_<<MetricAggregation>>.csv</code>, where Dataset is the dataset name, Prompt method indicates whether results are for independent or incremental prompting, Excluding Negatives (for datasets where this applies) whether results have been obtained by excluding negative instances, and MetricAggregation (where it applies) how metrics have been aggregated (macro or weighted average). The files report columns indicating the Dataset, the Matching type, the Accuracy, Precision, Recall, F1 Score, and Cohen's Kappa.</li> </ul> </li> <li> <code>RQ2</code>: contains the script used to perform metrics for RQ2, the datasets it uses, and its output. <ul> <li><code>RQ2_SetStats.ipynb</code> is the Python Jupyter notebook to perform the analyses. The scripts takes as input the following types of files, contained in the directory contains the script used to perform the metrics for RQ2. The script takes in input:</li> <li>RQ1 Data Files (<code>RQ1_DLFaults_Issues.csv</code>, <code>RQ1_DLFaults_Commits.csv</code>, and <code>RQ1_DLFaults_SO.csv</code>, joined in a single .csv <code>RQ1_DLFaults.csv</code>). These are the same files used in RQ1.</li> <li>Mapping Files (<code>RQ2_Mappings_DRL.csv</code>, <code>RQ2_Mappings_Functional.csv</code>, <code>RQ2_Mappings_DLFaults.csv</code>). These contain the mappings between human tags (HumanTags), GPT-4-Turbo tags (Final Tags), with indicated the type of matching (MatchType).</li> <li>Additional codes creating during the consolidation (<code>RQ2_newCodes_DRL.csv</code>, <code>RQ2_newCodes_Functional.csv</code>, <code>RQ2_newCodes_DLFaults.csv</code>), annotated with the matching: new code,old code,human code,match type</li> <li>Set files (<code>RQ2_Sets_DRL.csv</code>, <code>RQ2_Sets_Functional.csv</code>, <code>RQ2_Sets_DLFaults.csv</code>). Each file contains the following columns: <ul> <li>HumanTags: List of tags from the original dataset</li> <li>InitialTags: Set of tags from RQ1,</li> <li>ConsolidatedTags: Tags that have been consolidated,</li> <li>FinalTags: Final set of tags (results of RQ2, used in RQ3)</li> <li>NewTags: New tags created during consolidation</li> </ul> </li> <li><code>RQ2_Set_Metrics.csv</code>: Reports the RQ2 output metrics (Precision, Recall, F1-Score, Jaccard).</li> </ul> </li> </ul>

opencc-by-4.0Dec 2023View details →

Open Access Spiel "Skill It - Unlock Your Science!"

SKILL IT! – Unlock Your Science ist ein interaktives Pen&Paper-Game, das darauf abzielt, Kompetenzen im wissenschaftlichen Publizieren zu vermitteln. Der Schwerpunkt liegt auf Open Access und freien Lizenzen. Es richtet sich an ein breites Publikum, sowohl aus der Wissenschaft als auch an Interessierte ohne wissenschaftlichen Hintergrund. Das Spiel wurde im Rahmen der Langen Nacht der Wissenschaften 2022 entwickelt und eignet sich für 2 bis 6 Spieler. Die Spielzeit beträgt etwa 20 bis 30 Minuten. Ziel des Spiels ist es, die Teilnehmenden in die Rolle von Forschenden zu versetzen, die ihre wissenschaftlichen Ergebnisse veröffentlichen möchten. Die Spieler bewegen sich durch das Museum und sammeln in verschiedenen Stationen Wissen und Fähigkeiten, die sie im späteren Publikationsprozess erfolgreich anwenden. Der Spielablauf gliedert sich in zwei Phasen: eine &bdquo;Skill-Phase”, in der Wissen & Informationen erworben wird, und einen &bdquo;Publikationsprozess“, bei dem das erlernte Wissen auf die Probe gestellt wird. Würfelmechanismen und Entscheidungen beeinflussen den Spielverlauf, wobei verschiedene Fertigkeitsstufen den Erfolg im Publikationsprozess erhöhen können. Das Spiel bietet einen leichten Einstieg, benötigt wenig Vorbereitungszeit und wurde positiv getestet, insbesondere hinsichtlich der Vermittlung wissenschaftlicher Inhalte auf unterhaltsame Weise. Erstellt wurde das Konzept und die erste spielbare Version von Franziska Paech. Die grafische Umsetzung erfolgte durch Esra Woite am Museum für Naturkunde Berlin. Die weitere Überarbeitung und Anpassung der hier publizierten Version erfolgte durch die Koordinierungsstelle wissenschaftliches Publizieren am Museum für Naturkunde Berlin: Stefanie Paß & Clara Schindler. Alle hier publizierten Materialien unterliegen der Creative Common-Lizenz <a href="https://creativecommons.org/licenses/by/4.0/legalcode.de">CC-By 4.0 Namensnennung 4.0 International</a>

Different community open-source evaluation and benchmarking tools with their respective characteristics

Schematic of different community open-source evaluation and benchmarking tools with their respective characteristics.

Frequency of sustainability indicators in open access literature of house energy components

Processed data obtained from open access papers showing frequency of sustainability indicators in open access literature about PV systems and heatpumps in houses. Data used in paper "Systematic literature review of low energy housing electric component material sustainability".

Replication Package for "Classifying Open-Source Pre-Trained Models and Datasets for Software Engineering"

The replication package for the short paper titled 'Classifying Open-Source Pre-Trained Models and Datasets for Software Engineering' is provided. It includes a README file and accompanying scripts with comprehensive instructions to facilitate the replication of the analysis presented in the paper.

Discovery of a Non-Competitive Open-Flap Selective Inhibitor of Plasmepsin II with Antiplasmodial Activity

Here, we predicted that Plasmepsin II (PlmII) can explore open-flap conformations not sampled for the human aspartic proteases: Cathepsin D, Renin, and Pepsin along µs molecular dynamics simulations. We discovered two PlmII non-competitive selective inhibitors: SPB07935 and RH01201, with Ki values in the µM range by targeting the open-flap conformations. Both compounds did not inhibit the human Cathepsin D (hCatD) at high concentrations. We predicted that SPB07935 and RH01201 bind stably to the flap cryptic pocket, keeping this hairpin in an open or semi-open conformation along the MD simulations, respectively. Significantly, SPB07935 inhibited the P. falciparum chloroquine-resistant strain FcB1 growth in vitro, with an IC50 value of 8 µM while having a lower toxicity for HEK-293 human cells (IC50 = 189 µM).

opencc-zeroDec 2014View details →

Data from: Feral cats are better killers in open habitats, revealed by animal-borne video

One of the key gaps in understanding the impacts of predation by small mammalian predators on prey is how habitat structure affects the hunting success of small predators, such as feral cats. These effects are poorly understood due to the difficulty of observing actual hunting behaviours. We attached collar-mounted video cameras to feral cats living in a tropical savanna environment in northern Australia, and measured variation in hunting success among different microhabitats (open areas, dense grass and complex rocks). From 89 hours of footage, we recorded 101 hunting events, of which 32 were successful. Of these kills, 28% were not eaten. Hunting success was highly dependent on microhabitat structure surrounding prey, increasing from 17% in habitats with dense grass or complex rocks to 70% in open areas. This research shows that habitat structure has a profound influence on the impacts of small predators on their prey. This has broad implications for management of vegetation and disturbance processes (like fire and grazing) in areas where feral cats threaten native fauna. Maintaining complex vegetation cover can reduce predation rates of small prey species from feral cat predation.

opencc-zeroJan 2020View details →

Orbit Image Analysis: An open-source whole slide image analysis tool

This is a whole slide image (WSI) dataset for glomeruli segmentation on kidney tissue, in total 88 images. The train-set (58 images) and test-set (32 images) has been used in the publication "Orbit Image Analysis: An open-source whole slide image analysis tool" to train and test the glomeruli segmentation model.

opencc-zeroJun 2021View details →

Ecosystem sulfur accumulation following woody encroachment drives a more open S-cycle in a subtropical savanna

Globally widespread woody encroachment into grass-dominated ecosystems has substantial consequences for carbon (C), nitrogen (N), and phosphorus (P) cycles. Despite its significance as an essential macronutrient, however, little is known regarding potential changes in the sulfur (S) cycle. We quantified S concentrations, stoichiometric relationships, and δ34S values in the plant-soil environment to investigate landscape-scale changes in the S cycle following grassland-to-woodland transitions in a subtropical savanna. Plant tissues of woody species had significantly higher S concentrations and δ34S values than those of herbaceous species, resulting in a landscape-scale correspondence between spatial patterns of S and δ34S in surface soils and vegetation distribution, with higher S and δ34S in soils beneath woody patches. These patterns were more subtle at soil depths > 5 cm. Woody plants had higher N:S ratios but comparable P:S ratios relative to herbaceous species, which contributed to contrasting spatial patterns between N:S and P:S ratios in surface soils. Sulfur in surface soils increased proportionally less relative to N, but proportionally more compared to P. Our findings indicate that grassland-to-woodland transitions amplify landscape-scale S dynamics, especially in surface soils, and create a S-enriched environment that enables woody plants to acquire sufficient S relative to demand to support their continued productivity and proliferation.

opencc-by-4.0Jun 2021View details →

Open Datasets - available file for Design of Experimental (RSM) model, ANOVA table, SEM, VSM and Zeta potecial data for the nanocrystal clusters based on iron oxide.

The open-access data from the statistical model for the design of experiment optimization and as well as the experimental data are available for the community. The data are  based on an obtained results published in the article: https://www.mdpi.com/2079-4991/11/2/360

opencc-zeroJul 2019View details →

Data from: Active sound production of scarab beetle larvae opens up new possibilities for species-specific pest monitoring in soils

Root-feeding Scarabaeidae larvae can pose a serious threat to agricultural and forest ecosystems, but many details of larval ecology are still unknown. We developed an acoustic data analysis method based on active sound production by larvae (i.e. stridulations) for gaining new insights into larval ecology. In a laboratory study, third instar larvae of the Common Cockchafer (Melolontha melolontha) (n = 38) and the Forest Cockchafer (M. hippocastani) (n = 15) kept in soil-filled containers were acoustically monitored for 5 min each, resulting in the first known stridulation recordings for each species. Subsequent continuous monitoring of three M. hippocastani larvae over several hours showed that a single larva could stridulate more than 70 times per hour, and stridulation rates increased drastically with increasing larval abundance. The new fractal dimension-based data analysis method automatically detected audio sections with stridulations and provided a semi-quantitative estimate of stridulation activity. It is the first data analysis method specifically targeting Scarabaeidae larvae stridulations in soils, enabling for the first time non-invasive species-specific pest monitoring.

opennotspecifiedMay 2012View details →

FIGURE 2. Vochysia microphylla. A. Population showing the cespitose growth habit. B. Well-branched individual from Vellozia gigantea trail. C. Flowering branch with flower buds. D. Flowering branch with open flowers. A. Photo L.C. Rodrigues. B–D. Photos G.H in Vochysia microphylla (Vochysiaceae), a new species from Serra do Cipó, Minas Gerais, Brazil

FIGURE 2. Vochysia microphylla. A. Population showing the cespitose growth habit. B. Well-branched individual from Vellozia gigantea trail. C. Flowering branch with flower buds. D. Flowering branch with open flowers. A. Photo L.C. Rodrigues. B–D. Photos G.H. Shimizu.

opencc-by-3.0Jul 2021View details →

"Open Education: Vorträge, Folien & offene Lizenzen"; Online Workshop am 12.07.2021

Lizenzinformation der gesamten Präsentation: Titel: "Open Education: Vorträge, Folien & offene Lizenzen"; Online Workshop am 12.07.2021 Autor (by): Joachim Losehand (Creative Commons Austria) @ WTZ Ost Lizenz: cc by 3.0 (<a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqa0RYaTdmX0UxaWNOUUIxdThmaXVXbk5wZ0JjUXxBQ3Jtc0tuYnZlRzdoZTllMjg2QmM3RGUyQVpGMmUtbHVoQnNKQXN2VlhNVlNXUkJjclQ2NzVlTGNmNklRMjNvQWRaNjBwb0RKTjBFSHcwTmpwSUtNMlZ0dVdCWXRMcEdxcDhFOFI3QzY0OEtEdXRfUWhKdHRMaw&q=https%3A%2F%2Fcreativecommons.org%2Flicenses%2Fby%2F3.0%2F">https://creativecommons.org/licenses/...</a>) Lizenzinformation musikalische Hintergrundmusik: Titel: Musikalische Skizze für Klavier (2021) Autor (by): Joachim Losehand Lizenz: cc by 4.0 (<a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbUNTQ1p2QnJCTXEtZ185eVRwWVp0RUdRbkNKUXxBQ3Jtc0ttRnZQSHlEaXFOaG42LTRoZkRSUGpha0VEWjFDS0JnaThiRHRTbnVUeFM5cUZyWm1Sb0pKVG5saWpQaDJkV25QZEFBeFFvNWZkaklhTVU5VjdNX2c5SkNkd2lEeWpuX24zS2N4bVlWQWF1R0U5RzBFTQ&q=https%3A%2F%2Fcreativecommons.org%2Flicenses%2Fby%2F4.0%2F">https://creativecommons.org/licenses/...</a>) Lizenzinformation des verwendeten Bildmaterials: Kurzinformation direkt beim Bildmaterial sowie ausführliche Information auf der letzten Seite

opennotspecifiedSep 2013View details →

FIGURE 1. Manihot kalungae. A. Habit. B. Stipule. C. Adaxial leaf surface. D. Staminate flower with calyx split and opened. E. Stamen. F. Pistillate flower. G in A new endemic species of Manihot (Euphorbiaceae s. str.) from the Chapada dos Veadeiros, Goiás, Brazil

FIGURE 1. Manihot kalungae. A. Habit. B. Stipule. C. Adaxial leaf surface. D. Staminate flower with calyx split and opened. E. Stamen. F. Pistillate flower. G. Detail of the inner surface of the pistillate sepals showing the villous trichomes. H. Fruit. I. Seed, dorsal side. From M. J. Silva, R. C. Sodré, F. K. L. Silva, A. O. Souza & L. L. C. Antunes 4768 (UFG), drawn by Cristiano Gualberto.