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FIGURE 17 in A new species of the hysius species-group of Calisto Hübner (Lepidoptera, Nymphalidae, Satyrinae) and insights into the status of different populations currently attributed to C. grannus Bates
FIGURE 17. Geographic distribution of the hysius species group of Calisto. Black rhombus—type locality of C. bahoruco, new species; question marks—potential distribution of C. bahoruco following information by Schwartz (1989) and Warren et al. (2015); white rhombus—type locality of C. hysius, after Johnson & Hedges (1998); white circles—distribution of C. hysius, after Schwartz (1989).
FIGURES 13–14 in A new species of the hysius species-group of Calisto Hübner (Lepidoptera, Nymphalidae, Satyrinae) and insights into the status of different populations currently attributed to C. grannus Bates
FIGURES 13–14. Male genitalia of the hysius species group of Calisto, lateral view 13—C. bahoruco, new species. 14—C. hysius. Scale bar 0.5 mm.
FIGURES 9–12 in A new species of the hysius species-group of Calisto Hübner (Lepidoptera, Nymphalidae, Satyrinae) and insights into the status of different populations currently attributed to C. grannus Bates
FIGURES 9–12. Living adults of the hysius species group of Calisto. 9–10 C. bahoruco new species, Villa Nizao, Paraíso, Barahona, República Dominicana. 11–12 C. hysius, Los Arroyos, Pedernales, Sierra de Bahoruco, República Dominicana. Pictures by Pérez–Asso.
FIGURES 15–16 in A new species of the hysius species-group of Calisto Hübner (Lepidoptera, Nymphalidae, Satyrinae) and insights into the status of different populations currently attributed to C. grannus Bates
FIGURES 15–16. Female genitalia of the hysius species group of Calisto, ventral view. 15—C. bahoruco, new species. 16— C. hysius. Scale bar 1 mm.
FIGURES 19–20. 19 in A new species of the hysius species-group of Calisto Hübner (Lepidoptera, Nymphalidae, Satyrinae) and insights into the status of different populations currently attributed to C. grannus Bates
FIGURES 19–20. 19—Geographic distribution of C. grannus populations, nomenclature follows Sourakov & Zakharov (2011). 20—Median Joining Haplotype network obtained in Network v. 5.0; circles size proportional to number of sequences; gray circles are hypothetical haplotypes; black circles represent changes; colors represent subspecies sensu Sourakov & Zakharov (2011).
FIGURES 1–8 in A new species of the hysius species-group of Calisto Hübner (Lepidoptera, Nymphalidae, Satyrinae) and insights into the status of different populations currently attributed to C. grannus Bates
FIGURES 1–8. Adults of the hysius species group of Calisto. 1–2 C. bahoruco new species, male holotype, Villa Nizao, Paraíso, Barahona, República Dominicana: 1—upper surface, 2—under surface. 3–4 C. bahoruco new species, male paratype, same data: 3—upper surface, 4—under surface. 5–6 C. bahoruco new species, female paratype, same data: 5—upper surface, 6—under surface. 7–8 Under surface C. hysius, Los Arroyos, Pedernales, Sierra de Bahoruco, República Dominicana: 7— male, 8—female. Scale bar 10 mm. Pictures by Antonio R. Pérez–Asso.
Supplementary material 1 from: Uludağ A, Aksoy N, Yazlık A, Arslan ZF, Yazmış E, Üremiş I, Cossu TA, Groom Q, Pergl J, Pyšek P, Brundu G (2017) Alien flora of Turkey: checklist, taxonomic composition and ecological attributes. NeoBiota 35: 61-85. https://doi.org/10.3897/neobiota.35.12460
Alien flora of Turkey: checklist, taxonomic composition and ecological attributes : Explanation note: List of alien taxa in the flora of Turkey. Taxa are ordered alphabetically. Each taxon is listed together with its family, residence time, invasion status, life-form according to Raunkiaer, growth for according to the Thesaurus of Plant Characteristics for Ecology and Evolution, simplified growth-form, life history, reasons for intentional and accidental introduction. The last five columns on the right list habitats where the species is found in Turkey. This list includes also 47 frequently planted taxa.
Supplemental Material: Effects of Environmental Sustainability Enhancing Architectural Patterns and Tactics on different Quality Attributes
<p>These are the additional resources to the thesis with the same title.</p> <p>Context: Environmental sustainability is increasingly vital amid global challenges,<br>as IT presents both solutions and threats to the environment. Integrating environ-<br>mental sustainability into software engineering practices is essential, necessitating a<br>comprehensive understanding of environmental sustainability design decisions and<br>how they impact sustainability and other quality attributes within software architec-<br>tures.<br>Objectives: This research aims to address two primary objectives: firstly, to elicit environ-<br>mental sustainability scenarios and patterns and tactics from domain experts, and secondly,<br>to assess the tradeoffs of these design decisions in the context of a case study. Through qual-<br>itative insights garnered from a workshop and quantitative analysis via experiments, this<br>study seeks to offer nuanced insights into these tradeoffs.<br>Methodology: Utilizing a case study approach in collaboration with an industrial partner,<br>this work employs a combination of qualitative and quantitative analysis methods.<br>Qualitative insights are gathered through an Architecture Tradeoff Analysis Method<br>(ATAM) workshop with industrial partners, aimed at eliciting environmental sustainability<br>scenarios, design decisions, and their tradeoffs. Afterward, quantitative experiments<br>are conducted based on the workshop results using the Goal-Question-Metric (GQM)<br>approach, to measure the impact of environmental sustainability design decisions on<br>specific quality attributes in a mock implementation based on the industrial partner’s<br>system.<br>Results: We were able to capture eight environmental sustainability scenarios and<br>patterns and tactics and tradeoffs for five of them during the workshop. In the ex-<br>periments we implemented two tactics, expecting them to negatively impact the per-<br>formance. The experimentation provided empirical data, revealing nuanced insights,<br>sometimes contradicting initial hypotheses, highlighting the importance of empirical<br>validation.<br>Conclusion: This study underscores the complexity of balancing environmental sustain-<br>ability with other quality attributes in software design. It emphasizes the complementary<br>role of qualitative and quantitative analysis in understanding tradeoffs and making informed<br>decisions. By integrating insights from both approaches, this research contributes to ad-<br>vancing discussions on sustainability in software engineering, benefiting both researchers<br>and practitioners. Continued collaboration between academia and industry is crucial for<br>furthering sustainable software engineering practices.</p> <p> </p>
WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models
<p>#############</p> <h1>WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models</h1> <p>#############</p> <p>Authors: Valentin Gabeff, Marc Russwurm, Devis Tuia & Alexander Mathis</p> <p>Affiliation: EPFL</p> <p>Date: January, 2024</p> <p>Link to the article: <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6">https://link.springer.com/article/10.1007/s11263-024-02026-6</a></p> <p>--------------------------------</p> <p>WildCLIP is a fine-tuned CLIP model that allows to retrieve camera-trap events with natural language from the Snapshot Serengeti dataset. This project intends to demonstrate how vision-language models may assist the annotation process of camera-trap datasets.</p> <p>Here we provide the processed Snapshot Serengeti data used to train and evaluate WildCLIP, along with two versions of WildCLIP (model weights).</p> <p>Details on how to run these models can be found in the project <a href="https://github.com/amathislab/wildclip">github repository</a>.</p> <h2>Provided data (images and attribute annotations): </h2> <p>The data consists of 380 x 380 image crops corresponding to the MegaDetector output of Snapshot Serengeti with a confidence threshold above 0.7. We considered only camera trap images containing single individuals.</p> <p>A description of the original data can be found on LILA <a href="https://lila.science/datasets/snapshot-serengeti">here</a>, released under the <a href="https://cdla.dev/permissive-1-0/" rel="nofollow">Community Data License Agreement (permissive variant)</a>.</p> <p>We warmly thank the authors of LILA for making the MegaDetector outputs publicly available, as well as for structuring the dataset and facilitating its access.</p> <h2>Adapted CLIP model (model weights): </h2> <p>WildCLIP models provided:</p> <ul> <li><strong>[New] WildCLIP_vitb16_t1.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>[New] WildCLIP_vitb16_t1_lwf.pth: </strong>CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1, and with the additional VR-LwF loss. Trained on both base and novel vocabulary (see paper for details).</li> <li><strong>WildCLIP_vitb16_t1_base.pth:</strong> CLIP model with the ViT-B/16 visual backbone trained on data with captions following template 1. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1.pth</em>)</li> <li><strong>WildCLIP_vitb16_t1t7_lwf_base.pth</strong>: CLIP model with the ViT-B/16 visual backbone trained on data with captions following templates 1 to 7, and with the additional VR-LwF loss. Model used for evaluation and trained on base vocabulary only. (previously named <em>WildCLIP_vitb16_t1t7_lwf.pth</em>)</li> </ul> <p>We also provide the CSV files containing the train / val / test splits. The train / test splits follow camera split from LILA (https://lila.science/datasets/snapshot-serengeti). The validation split is custom, and also at the camera level.</p> <ul> <li><strong>train_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Train set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>val_dataset_crops_single_animal_template_captions_T1T7_ID.csv</strong>: Validation set with captions from templates 1 through 7 (column "all captions") or template 1 only (column "template 1")</li> <li><strong>test_dataset_crops_single_animal_template_captions_T1T8T10.csv</strong>: Test set with captions from templates 1, 8, 9 and 10 (columns "all captions")</li> </ul> <p>Details on how the models were trained can be found in the associated <a href="https://link.springer.com/article/10.1007/s11263-024-02026-6" target="_blank" rel="noopener">publication</a>.</p> <h2>References: </h2> <p>If you find our code, or weights, please cite:</p> <pre>@article{gabeff2024wildclip, title={WildCLIP: Scene and animal attribute retrieval from camera trap data with domain-adapted vision-language models}, author={Gabeff, Valentin and Ru{\ss}wurm, Marc and Tuia, Devis and Mathis, Alexander}, journal={International Journal of Computer Vision}, pages={1--17}, year={2024}, publisher={Springer} }</pre> <p>If you use the adapted Snapshot Serengeti data please also cite their article:</p> <pre>@article{swanson2015snapshot, title={Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna}, author={Swanson, Alexandra and Kosmala, Margaret and Lintott, Chris and Simpson, Robert and Smith, Arfon and Packer, Craig}, journal={Scientific data}, volume={2}, number={1}, pages={1--14}, year={2015}, publisher={Nature Publishing Group} }</pre>
Supplementary material - Blind comparison of binaural auralisations to a real loudspeaker in an audiovisual virtual classroom scenario: Effect of room acoustic simulation, HRTF dataset and head worn devices on rated room-acoustical attributes
<p>Additional Material for the paper "Blind comparison of binaural auralisations to a real loudspeaker in an audiovisual virtual classroom scenario: Effect of room acoustic simulation, HRTF dataset and head worn devices on rated room-acoustical attributes".</p> <p> </p> <p>This work is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the project ID 422686707, SPP2236 – AUDICTIVE – Auditory Cognition in Interactive Virtual Environments</p>
Augmented Reality App Attributes on Consumer Perceived Values
Open the record for dataset details and reuse information.
Albanian Authorship Attribution Corpus
<p>This dataset includes the following information:</p> <ul> <li><strong>data folder</strong>: Contains two subfolders that categorize texts for each dataset.</li> <ul> <li><strong>literary folder</strong>: folders representing contemporary Albanian authors.</li> <li><strong>columns folder</strong>: folders representing Albanian journalists and editors from various web sources.</li> </ul> </ul> <p>Each subfolder contains the written texts of specific writers.</p> <p>To ensure confidentiality and protect user privacy, all identifying information about the authors and their writings, has been anonymized. This measure prevents the disclosure of any personally identifiable information and respects the authors' right to privacy. Texts are denoted as [number].txt within their respective folders.</p> <p><span>This corpus was utilized in our work “Automatic Authorship Attribution in Albanian Texts”.</span></p>
Data from: Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944
<p><strong>Paper Abstract:</strong></p> <p>Urban expansion, especially suburbanization, represents a major social, economic and environmental shift that has escalated since the mid-1900s in North America. Suburban development leads to corresponding changes in the treed environment of urban-rural fringes. It is important to understand where, when and why trees change in response to development over many decades, but this is difficult since long-term data are scarce. We used 70+ years (1944–2017) of leaf-off aerial photographs, often representing the only long-term landscape record, to quantify and map spatio-temporal changes in tree density through the entirety of the agricultural-suburban transitional period. Photo-interpretation of individual tree locations, along with recording observable drivers of change, was completed across six different modern landscapes in Mississauga, Ontario, Canada. Results indicate that tree density often recovers, or even increases, post-development. There are differences between landscapes, but most tree density gains are associated with forest expansion and tree planting, while most losses are associated with building and road construction. The influence of these drivers, along with the temporal trajectory of tree density changes, are shaped by a landscape’s socioecological legacy and the length, scope and intensity of development (as decided by decision makers). Processes include initial tree losses followed by recovery from tree planting, and forest succession in abandoned fields after land purchase and nearby development. We assert that the spatio-temporal changes in tree density and related drivers quantified here can be generalized to gain knowledge on how tree density and distribution across agricultural landscapes will change under different development scenarios.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper: <a href="https://www.sciencedirect.com/science/article/pii/S0169204619301914?via%3Dihub">Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944 - ScienceDirect</a></p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/SuburbanizingTreeDensity_1944to2017">ZZMitch/SuburbanizingTreeDensity_1944to2017: Code from "Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944" (L&UP, 2019) (github.com)</a></p> <p>- Note: High resolution imagery is not included in this repository. </p> <p> </p> <p><strong>If you use these data, please reference: </strong></p> <p>Bonney, M.T., He, Y., 2019. Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944. Landscape and Urban Planning 192, https://doi.org/10.1016/j.landurbplan.2019.103652. </p>
FIGURE 11 in Septemoecia a new genus of halocyprid ostracod (Myodocopa, Halocyprididae, Bathyconchoeciinae) for the seven-spined species formerly attributed to Bathyconchoecia
FIGURE 11. ZOOgeOgraphy Of Septemoecia speCies. NOte: nOt all the reCOrds Of S. longispinata COuld be plOtted at this sCale.
FIGURE 10 in Septemoecia a new genus of halocyprid ostracod (Myodocopa, Halocyprididae, Bathyconchoeciinae) for the seven-spined species formerly attributed to Bathyconchoecia
FIGURE 10. CarapaCe Outlines Of all knOwn speCies Of Septemoecia reprOduCed at the same sCale, illustrating the eXternal differenCes between the speCies. A, S. omega, A-1 male (redrawn frOm KOrniCker & RudJakOv 2004); B, S. longispinata, male (COpied frOm Ellis 1987 (fig. 1A, B); C, S. aff. deeveyae (COpied frOm KOrniCker 1981, fig. 1a); D, S. septemspinosa, Juvenile female (reprOduCed frOm Angel 1970, fig. 7A, B); E, S. georgei, female (Original drawing Of adult female desCribed here): F, S. deeveyae, Juvenile (redrawn frOm KOrniCker 1969, pl. 1b). The length sCale is 1 mm.
FIGURE 9 in Septemoecia a new genus of halocyprid ostracod (Myodocopa, Halocyprididae, Bathyconchoeciinae) for the seven-spined species formerly attributed to Bathyconchoecia
FIGURE 9. Septemoecia georgei, female (Discovery statiOn 54001#31). A, mandibular COXale tOOthlists; B, maXilla; C, fifth limb; D, siXth limb; E, Caudal furCa.
FIGURE 8 in Septemoecia a new genus of halocyprid ostracod (Myodocopa, Halocyprididae, Bathyconchoeciinae) for the seven-spined species formerly attributed to Bathyconchoecia
FIGURE 8. Septemoecia georgei, female (Discovery statiOn 54001#31). A, CarapaCe lateral aspeCt; B, CarapaCe ventral aspeCt; C, first antenna; D, seCOnd antenna; E, seCOnd antenna endOpOdite; F, mandible.
FIGURE 6 in Septemoecia a new genus of halocyprid ostracod (Myodocopa, Halocyprididae, Bathyconchoeciinae) for the seven-spined species formerly attributed to Bathyconchoecia
FIGURE 6. Septemoecia septemspinosa, male (Discovery StatiOn 8012). A, mandible tOOthed edges Of basale; B, fifth limb; C, siXth limb; D, Caudal furCa and COpulatOry appendage.
FIGURE 3 in Septemoecia a new genus of halocyprid ostracod (Myodocopa, Halocyprididae, Bathyconchoeciinae) for the seven-spined species formerly attributed to Bathyconchoecia
FIGURE 3. Septemoecia longispinata (Ellis, 1987). SCanning eleCtrOn miCrOgraph Of an adult female Caught at 08°09.23' N, 49°04.37' W. The pOsitiOn Of the Opening Of the CarapaCe gland Just anteriOr tO the pOsteriOr dOrsal COrner is labelled A, and the first Of the hOriZOntal bars that line the anteriOr margin Of the CarapaCe belOw the rOstral inCisures is labelled B.
FIGURE 4 in Septemoecia a new genus of halocyprid ostracod (Myodocopa, Halocyprididae, Bathyconchoeciinae) for the seven-spined species formerly attributed to Bathyconchoecia
FIGURE 4. Septemoecia longispinata (Ellis, 1987), A-1 female (Polarstern StatiOn MOC10#4). A, siXth limb; B, Caudal furCa.
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.