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COCO, LVIS, Open Images V4 classes mapping
<p>This repository contains a mapping between the classes of COCO, LVIS, and Open Images V4 datasets into a unique set of 1460 classes.</p> <p>COCO [Lin et al 2014] contains 80 classes, LVIS [gupta2019lvis] contains 1460 classes, Open Images V4 [Kuznetsova et al. 2020] contains 601 classes.</p> <p>We built a mapping of these classes using a semi-automatic procedure in order to have a unique final list of 1460 classes. We also generated a hierarchy for each class, using <a href="https://wordnet.princeton.edu/">wordnet</a></p> <p>This repository contains the following files:</p> <ul> <li><em>coco_classes_map.txt</em>, contains the mapping for the 80 coco classes</li> <li><em>lvis_classes_map.txt</em>, contains the mapping for the 1460 coco classes</li> <li><em>openimages_classes_map.txt</em>, contains the mapping for the 601 coco classes</li> <li><em>classname_hyperset_definition.csv</em>, contains the final set of 1460 classes, their definition and hierarchy</li> <li><em>all-classnames.xlsx</em>, contains a side-by-side view of all classes considered</li> </ul> <p>This mapping was used in VISIONE [Amato et al. 2021, Amato et al. 2022] that is a content-based retrieval system that supports various search functionalities (text search, object/color-based search, semantic and visual similarity search, temporal search). For the object detection VISIONE uses three pre-trained models: VfNet [Zhang et al. 2021] (trained on COCO dataset), Mask R-CNN [He et al. 2017] (trained on LVIS), and a <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4).</p> <p>This is repository is released under a Creative Commons Attribution license, please cite the following paper if you use it in your work in any form:</p> <blockquote> <pre>@inproceedings{amato2021visione, title={The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Debole, Franca and Falchi, Fabrizio and Gennaro, Claudio and Vadicamo, Lucia and Vairo, Claudio}, journal={Journal of Imaging}, volume={7}, number={5}, pages={76}, year={2021}, publisher={Multidisciplinary Digital Publishing Institute} } </pre> </blockquote> <p> </p> <p> </p> <p><em><strong>References:</strong></em></p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. <em>MMM 2022. Lecture Notes in Computer Science</em>, vol 13142. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-98355-0_52">https://doi.org/10.1007/978-3-030-98355-0_52</a></p> <p>[Amato et al. 2021] Amato, G., Bolettieri, P., Carrara, F., Debole, F., Falchi, F., Gennaro, C., Vadicamo, L. and Vairo, C., 2021. The visione video search system: exploiting off-the-shelf text search engines for large-scale video retrieval. <em>Journal of Imaging</em>, <em>7</em>(5), p.76.</p> <p>[Gupta et al.2019] Gupta, A., Dollar, P. and Girshick, R., 2019. Lvis: A dataset for large vocabulary instance segmentation. In <em>Proceedings of the IEEE/CVF conference on computer vision and pattern recognition</em> (pp. 5356-5364).</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In <em>Proceedings of the IEEE international conference on computer vision</em> (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. <em>International Journal of Computer Vision</em>, <em>128</em>(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In <em>European conference on computer vision</em> (pp. 740-755). Springer, Cham.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In <em>Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</em> (pp. 8514-8523).</p> <p> </p>
Native tree growth and reproduction in response to reduction in the coconut palm (Cocos nucifera) canopy at Palmyra Atoll
These data describe competition for light (open solar path) between introduced coconut palm trees (Cocos nucifera) and native tree species between 2004 and 2008 at Palmyra Atoll, Northern Line Islands, Pacific Ocean. Data are contained in one table, including values from the start, end, and intermediate samples. The dataset measures the change in tree growth (DBH and height) and reproductive potential (flower and fruit production) in relation to time and open solar path value. Two treatments are considered: OSP values less than 50% created by C. nucifera removal, and OSP values greater than 50%.
Synthetically Spoken COCO
<p>Synthetically Spoken COCO</p> <p>Version 1.0</p> <p>This dataset contain synthetically generated spoken versions of MS COCO [1] captions. This<br> dataset was created as part the research reported in [5].<br> The speech was generated using gTTS [2]. The dataset consists of the following files:</p> <p>- dataset.json: Captions associated with MS COCO images. This information comes from [3]. <br> - sentid.txt: List of caption IDs. This file can be used to locate MFCC features of the MP3 files<br> in the numpy array stored in dataset.mfcc.npy.<br> - mp3.tgz: MP3 files with the audio. Each file name corresponds to caption ID in dataset.json<br> and in sentid.txt.<br> - dataset.mfcc.npy: Numpy array with the Mel Frequence Cepstral Coefficients extracted from <br> the audio. Each row corresponds to a caption. The order or the captions corresponds to the<br> ordering in the file sentid.txt. MFCCs were extracted using [4].</p> <p>[1] http://mscoco.org/dataset/#overview<br> [2] https://pypi.python.org/pypi/gTTS<br> [3] https://github.com/karpathy/neuraltalk<br> [4] https://github.com/jameslyons/python_speech_features<br> [5] https://arxiv.org/abs/1702.01991</p>
National Checklists 2017: Cocos Islands
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Cocos Islands collected using effechecka and geonames polygons
National Checklists 2019: Cocos Islands
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Cocos Islands collected using effechecka and geonames polygons
Fig. 7 in Coordinated hunting behaviors of mixed-species groups of piscivores and associated species at Isla del Coco National Park (Eastern Tropical Pacific)
Fig. 7. Cluster analysis of species co-occurrences in groups based on the Ward linkage method and Pearson distance. Abbreviations for each species are defined in Tab. 1. Note two dominant clusters (cluster 1 at left and cluster 2 at right). An asterisk below species codes indicate membership in top 10 species based on mixed-species links.
Fig. 5 in Coordinated hunting behaviors of mixed-species groups of piscivores and associated species at Isla del Coco National Park (Eastern Tropical Pacific)
Fig. 5. Network visualization of the web of associations between species (33 species with 288 pair-wise links). The size of each species node is weighted relative to the frequency of each species in any group (abbreviations for each species are defined in Tab. 1). The lines between species nodes are weighted by the relative frequency of associations between each species pair.
Fig. 3 in Coordinated hunting behaviors of mixed-species groups of piscivores and associated species at Isla del Coco National Park (Eastern Tropical Pacific)
Fig. 3. Examples of mixed-species hunting groups observed to ca. 35 m depth. (A) Caranx melampygus, Dermatolepis dermatolepis and Bodianus diplotenia at a crevice. Note D. dermatolepis and B. diplotaenia are able to maneuver deep into the crevice while C. melampygus follow from above and search for escaping prey. (B) Group composed of C. melampygus, Trianodon obesus, Cephalopholis panamensis and unidentified muraenid eel (hidden within crevices) hunt for prey within crevices amongst coral and coral rubble along reef edge. (C) Lutjanus argentiventris, D. dermatolepis, C. melampygus and B. diplotaenia hunt for prey as group traverses low relief volcanic pavement along a pinnacle. (D) Group composed of D. dermatolepis, C. melampygus and Aulostomus chinensis. Note position of A. chinensis in lead over C. melampygus. (E) C. melampygus follows above a muraenid eel hunting within narrow crevices. (F) As in previous image, D. dermatolepis follows above muraenid eel hunting within narrow crevice. (G) C. melampygus follow B. diplotaenia hunting over sand and volcanic rubble habitat. (H) B. diplotaenia and A. chinensis hunt in tandem along edge of pinnacle.
SPEECH-COCO
<p><strong>SpeechCoco</strong></p> <p><em>Introduction</em></p> <p>Our corpus is an extension of the MS COCO image recognition and captioning dataset. MS COCO comprises images paired with a set of five captions. Yet, it does not include any speech. Therefore, we used <a href="https://www.voxygen.fr/">Voxygen's text-to-speech system</a> to synthesise the available captions.</p> <p>The addition of speech as a new modality enables MSCOCO to be used for researches in the field of language acquisition, unsupervised term discovery, keyword spotting, or semantic embedding using speech and vision.</p> <p>Our corpus is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 License</a>.</p> <p><em>Data Set</em></p> <ul> <li> <p>This corpus contains <strong>616,767</strong> spoken captions from MSCOCO's val2014 and train2014 subsets (respectively 414,113 for train2014 and 202,654 for val2014).</p> </li> <li> <p>We used 8 different voices. 4 of them have a British accent (Paul, Bronwen, Judith, and Elizabeth) and the 4 others have an American accent (Phil, Bruce, Amanda, Jenny).</p> </li> <li> <p>In order to make the captions sound more natural, we used SOX <em>tempo</em> command, enabling us to change the speed without changing the pitch. 1/3 of the captions are 10% slower than the original pace, 1/3 are 10% faster. The last third of the captions was kept untouched.</p> </li> <li> <p>We also modified approximately 30% of the original captions and added <strong>disfluencies</strong> such as "um", "uh", "er" so that the captions would sound more natural.</p> </li> <li> <p>Each WAV file is paired with a JSON file containing various information: timecode of each word in the caption, name of the speaker, name of the WAV file, etc. The JSON files have the following data structure:</p> </li> </ul> <pre><code class="language-json">{ "duration": float, "speaker": string, "synthesisedCaption": string, "timecode": list, "speed": float, "wavFilename": string, "captionID": int, "imgID": int, "disfluency": list }</code></pre> <ul> <li> <p>On average, each caption comprises 10.79 tokens, disfluencies included. The WAV files are on average 3.52 seconds long.</p> </li> </ul> <p><em>Repository</em></p> <p>The repository is organized as follows:</p> <ul> <li> <p>CORPUS-MSCOCO (~75GB once decompressed)</p> <blockquote> <ul> <li> <p><strong>train2014/</strong> : folder contains 413,915 captions</p> <ul> <li> <p>json/</p> </li> <li> <p>wav/</p> </li> <li> <p>translations/</p> <ul> <li> <p>train_en_ja.txt</p> </li> <li> <p>train_translate.sqlite3</p> </li> </ul> </li> <li> <p>train_2014.sqlite3</p> </li> </ul> </li> <li> <p><strong>val2014/</strong> : folder contains 202,520 captions</p> <ul> <li> <p>json/</p> </li> <li> <p>wav/</p> </li> <li> <p>translations/</p> <ul> <li> <p>train_en_ja.txt</p> </li> <li> <p>train_translate.sqlite3</p> </li> </ul> </li> <li> <p>val_2014.sqlite3</p> </li> </ul> </li> <li> <p><strong>speechcoco_API/</strong></p> <ul> <li> <p>speechcoco/</p> <ul> <li> <p>__init__.py</p> </li> <li> <p>speechcoco.py</p> </li> </ul> </li> <li> <p>setup.py</p> </li> </ul> </li> </ul> </blockquote> </li> </ul> <p><em>Filenames</em></p> <p><strong>.wav</strong> files contain the spoken version of a caption</p> <p><strong>.json</strong> files contain all the metadata of a given WAV file</p> <p><strong>.sqlite3</strong> files are SQLite databases containing all the information contained in the JSON files</p> <p>We adopted the following naming convention for both the WAV and JSON files:</p> <p><em>imageID_captionID_Speaker_DisfluencyPosition_Speed[.wav/.json]</em></p> <p><em>Script</em></p> <p>We created a script called <strong>speechcoco.py</strong> in order to handle the metadata and allow the user to easily find captions according to specific filters. The script uses the *.db files.</p> <p>Features:</p> <ul> <li> <p><strong>Aggregate all the information in the JSON files into a single SQLite database</strong></p> </li> <li> <p><strong>Find captions according to specific filters (name, gender and nationality of the speaker, disfluency position, speed, duration, and words in the caption).</strong> <em>The script automatically builds the SQLite query. The user can also provide his own SQLite query.</em></p> </li> </ul> <p><em>The following Python code returns all the captions spoken by a male with an American accent for which the speed was slowed down by 10% and that contain "keys" at any position</em></p> <pre><code class="language-python"># create SpeechCoco object db = SpeechCoco(train_2014.sqlite3, train_translate.sqlite3, verbose=True) # filter captions (returns Caption Objects) captions = db.filterCaptions(gender="Male", nationality="US", speed=0.9, text='%keys%') for caption in captions: print('\n{}\t{}\t{}\t{}\t{}\t{}\t\t{}'.format(caption.imageID, caption.captionID, caption.speaker.name, caption.speaker.nationality, caption.speed, caption.filename, caption.text))</code></pre> <pre><code>... 298817 26763 Phil 0.9 298817_26763_Phil_None_0-9.wav A group of turkeys with bushes in the background. 108505 147972 Phil 0.9 108505_147972_Phil_Middle_0-9.wav Person using a, um, slider cell phone with blue backlit keys. 258289 154380 Bruce 0.9 258289_154380_Bruce_None_0-9.wav Some donkeys and sheep are in their green pens . 545312 201303 Phil 0.9 545312_201303_Phil_None_0-9.wav A man walking next to a couple of donkeys. ...</code></pre> <ul> <li> <p><strong>Find all the captions belonging to a specific image</strong></p> </li> </ul> <pre><code class="language-python">captions = db.getImgCaptions(298817) for caption in captions: print('\n{}'.format(caption.text))</code></pre> <pre><code>Birds wondering through grassy ground next to bushes. A flock of turkeys are making their way up a hill. Um, ah. Two wild turkeys in a field walking around. Four wild turkeys and some bushes trees and weeds. A group of turkeys with bushes in the background.</code></pre> <ul> <li> <p><strong>Parse the timecodes and have them structured</strong></p> </li> </ul> <p><strong>input</strong>:</p> <pre><code>... [1926.3068, "SYL", ""], [1926.3068, "SEPR", " "], [1926.3068, "WORD", "white"], [1926.3068, "PHO", "w"], [2050.7955, "PHO", "ai"], [2144.6591, "PHO", "t"], [2179.3182, "SYL", ""], [2179.3182, "SEPR", " "] ...</code></pre> <p><strong>output</strong>:</p> <pre><code class="language-python">print(caption.timecode.parse())</code></pre> <pre><code>... { 'begin': 1926.3068, 'end': 2179.3182, 'syllable': [{'begin': 1926.3068, 'end': 2179.3182, 'phoneme': [{'begin': 1926.3068, 'end': 2050.7955, 'value': 'w'}, {'begin': 2050.7955, 'end': 2144.6591, 'value': 'ai'}, {'begin': 2144.6591, 'end': 2179.3182, 'value': 't'}], 'value': 'wait'}], 'value': 'white' }, ...</code></pre> <ul> <li> <p><strong>Convert the timecodes to Praat TextGrid files</strong></p> </li> </ul> <pre><code class="language-python">caption.timecode.toTextgrid(outputDir, level=3)</code></pre> <ul> <li> <p><strong>Get the words, syllables and phonemes between</strong> <em>n</em> <strong>seconds/milliseconds</strong></p> </li> </ul> <p><em>The following Python code returns all the words between 0.2 and 0.6 seconds for which at least 50% of the word's total length is within the specified interval</em></p> <pre><code class="language-python">pprint(caption.getWords(0.20, 0.60, seconds=True, level=1, olapthr=50))</code></pre> <pre><code>... 404537 827239 Bruce US 0.9 404537_827239_Bruce_None_0-9.wav Eyeglasses, a cellphone, some keys and other pocket items are all laid out on the cloth. . [ { 'begin': 0.0, 'end': 0.7202778, 'overlapPercentage': 55.53412863758955, 'word': 'eyeglasses' } ] ...</code></pre> <ul> <li> <p><strong>Get the translations of the selected captions</strong></p> </li> </ul> <p><em>As for now, only japanese translations are available. We also used</em> <a href="http://www.phontron.com/kytea/">Kytea</a> <em>to tokenize and tag the captions translated with Google Translate</em></p> <pre><code class="language-python">captions = db.getImgCaptions(298817) for caption in captions: print('\n{}'.format(caption.text)) # Get translations and POS print('\tja_google: {}'.format(db.getTranslation(caption.captionID, "ja_google"))) print('\t\tja_google_tokens: {}'.format(db.getTokens(caption.captionID, "ja_google"))) print('\t\tja_google_pos: {}'.format(db.getPOS(caption.captionID, "ja_google"))) print('\tja_excite: {}'.format(db.getTranslation(caption.captionID, "ja_excite")))</code></pre> <pre><code> Birds wondering through grassy ground next to bushes. ja_google: 鳥は茂みの下に茂った地面を抱えています。 ja_google_tokens: 鳥 は 茂み の 下 に 茂 っ た 地面 を 抱え て い ま す 。 ja_google_pos: 鳥/名詞/とり は/助詞/は 茂み/名詞/しげみ の/助詞/の 下/名詞/した に/助詞/に 茂/動詞/しげ っ/語尾/っ た/助動詞/た 地面/名詞/じめん を/助詞/を 抱え/動詞/かかえ て/助詞/て い/動詞/い ま/助動詞/ま す/語尾/す 。/補助記号/。 ja_excite: 低木と隣接した草深いグラウンドを通って疑う鳥。 A flock of turkeys are making their way up a hill. ja_google: 七面鳥の群れが丘を上っています。 ja_google_tokens: 七 面 鳥 の 群れ が 丘 を 上 っ て い ま す 。 ja_google_pos: 七/名詞/なな 面/名詞/めん 鳥/名詞/とり の/助詞/の 群れ/名詞/むれ が/助詞/が 丘/名詞/おか を/助詞/を 上/動詞/のぼ っ/語尾/っ て/助詞/て い/動詞/い ま/助動詞/ま す/語尾/す 。/補助記号/。 ja_excite: 七面鳥の群れは丘の上で進んでいる。 Um, ah. Two wild turkeys in a field walking around. ja_google: 野生のシチメンチョウ、野生の七面鳥 ja_google_tokens: 野生 の シチメンチョウ 、 野生 の 七 面 鳥 ja_google_pos: 野生/名詞/やせい の/助詞/の シチメンチョウ/名詞/しちめんちょう 、/補助記号/、 野生/名詞/やせい の/助詞/の 七/名詞/なな 面/名詞/めん 鳥/名詞/ちょう ja_excite: まわりで移動しているフィールドの2羽の野生の七面鳥 Four wild turkeys and some bushes trees and weeds. ja_google: 4本の野生のシチメンチョウといくつかの茂みの木と雑草 ja_google_tokens: 4 本 の 野生 の シチメンチョウ と いく つ か の 茂み の 木 と 雑草 ja_google_pos: 4/名詞/4 本/接尾辞/ほん の/助詞/の 野生/名詞/やせい の/助詞/の シチメンチョウ/名詞/しちめんちょう と/助詞/と いく/名詞/いく つ/接尾辞/つ か/助詞/か の/助詞/の 茂み/名詞/しげみ の/助詞/の 木/名詞/き と/助詞/と 雑草/名詞/ざっそう ja_excite: 4羽の野生の七面鳥およびいくつかの低木木と雑草 A group of turkeys with bushes in the background. ja_google: 背景に茂みを持つ七面鳥の群 ja_google_tokens: 背景 に 茂み を 持 つ 七 面 鳥 の 群 ja_google_pos: 背景/名詞/はいけい に/助詞/に 茂み/名詞/しげみ を/助詞/を 持/動詞/も つ/語尾/つ 七/名詞/なな 面/名詞/めん 鳥/名詞/ちょう の/助詞/の 群/名詞/むれ ja_excite: 背景の低木を持つ七面鳥のグループ</code></pre> <p> </p>
Figs. 10 –13. Coconotus williamsi Anderson and Lanteri. 10 in New Genera and Species of Weevils from the Galapagos Islands, Ecuador, and Cocos Island, Costa Rica (Coleoptera; Curculionidae; Entiminae; Entimini)
Figs. 10 –13. Coconotus williamsi Anderson and Lanteri. 10, Lateral habitus, female; 11, dorsal habitus, female; 12, lateral habitus, male; 13, dorsal habitus, male.
Fig. 3–9 in New Genera and Species of Weevils from the Galapagos Islands, Ecuador, and Cocos Island, Costa Rica (Coleoptera; Curculionidae; Entiminae; Entimini)
Fig. 3–9. Galapagonotus cuneiformis (Waterhouse). 3, Male abdomen, ventral view; 4, female abdomen, ventral view; 5, ovipositor, sternite 8, tergites 7 and 8; 6, spermatheca; 7, aedeagus, lateral view; 8, aedeagus, dorsal view; 9, sternites 8 and 9, tergite 8. Scale line = 1 mm, except spermatheca, = 0.25 mm.
Figs. 23–26. Coconotus kuscheli Anderson and Lanteri. 23 in New Genera and Species of Weevils from the Galapagos Islands, Ecuador, and Cocos Island, Costa Rica (Coleoptera; Curculionidae; Entiminae; Entimini)
Figs. 23–26. Coconotus kuscheli Anderson and Lanteri. 23, Lateral habitus, female; 24, dorsal habitus, female; 25, lateral habitus, male; 26, dorsal habitus, male.
Figs. 14–22. Coconotus williamsi Anderson and Lanteri. 14 in New Genera and Species of Weevils from the Galapagos Islands, Ecuador, and Cocos Island, Costa Rica (Coleoptera; Curculionidae; Entiminae; Entimini)
Figs. 14–22. Coconotus williamsi Anderson and Lanteri. 14, Male abdomen, ventral view; 15, female abdomen, ventral view; 16, sternite 8; 17, tergites 7 and 8; 18, hemisternites; 19,spermatheca; 20, aedeagus, lateral view; 21, aedeagus, dorsal view; 22, sternites 8 and 9, tergite 8. Scale lines = 1 mm, except spermatheca = 0.25 mm.
Figs. 27–35. Coconotus kuscheli Anderson and Lanteri. 27 in New Genera and Species of Weevils from the Galapagos Islands, Ecuador, and Cocos Island, Costa Rica (Coleoptera; Curculionidae; Entiminae; Entimini)
Figs. 27–35. Coconotus kuscheli Anderson and Lanteri. 27, Male abdomen, ventral view; 28, female abdomen, ventral view; 29, sternite 8; 30, tergites 7 and 8; 31, hemisternites; 32, spermatheca; 33, aedeagus, lateral view; 34, aedeagus, dorsal view (inset shows large curved sclerite of internal sac at base of aedeagus); 35, sternites 8 and 9, tergite 8. Scale line = 1 mm, except spermatheca = 0.25 mm.
Figs. 37– 41. Coconotus tuberculatus Anderson and Lanteri. 37 in New Genera and Species of Weevils from the Galapagos Islands, Ecuador, and Cocos Island, Costa Rica (Coleoptera; Curculionidae; Entiminae; Entimini)
Figs. 37– 41. Coconotus tuberculatus Anderson and Lanteri. 37, female abdomen, ventral view; 38, sternite 8; 39, tergites 7 and 8; 40, hemisternites, 41, spermatheca. Scale line = 1 mm, except spermatheca = 0.25 mm.
National Checklists: Cocos Islands
Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>
Fig. 4 in Unraveling the taxonomic identity of Cocos nucifera f. palmyrensis (Arecaceae: Cocoseae)
Fig. 4. – Lateral view of the lectotype of Cocos nucifera var. palmyrensis (Beccari) Pignotti & Baldini. [FI018792] [Photo: L. Pignotti]
Fig. 2 in Unraveling the taxonomic identity of Cocos nucifera f. palmyrensis (Arecaceae: Cocoseae)
Fig. 2. – Original material of Cocos nucifera var. palmyrensis (Beccari) Pignotti & Baldini. A. top: lectotype; bottom: upper view and cross sectioned by Beccari of a fruit now missing at FI; B. top: syntype; bottom: lateral view of a slender coconut, now missing at FI. [A top: FI018792; B top: FI018793] [Reproduced from the Bull. Coll. Hawaii Publ. 4: tab. XVII, XVIII]
material of Cocos nucifera var. palmyrensis (Beccari) Pignotti & Baldini. A. Lectotype; B. Syntype. [A: FI018792; B: FI018793] [Photos: D. Nesti, L. Pignotti] in Unraveling the taxonomic identity of Cocos nucifera f. palmyrensis (Arecaceae: Cocoseae)
material of Cocos nucifera var. palmyrensis (Beccari) Pignotti & Baldini. A. Lectotype; B. Syntype. [A: FI018792; B: FI018793] [Photos: D. Nesti, L. Pignotti]
Fig. 3 in Unraveling the taxonomic identity of Cocos nucifera f. palmyrensis (Arecaceae: Cocoseae)
Fig. 3. – Close-up of the original note on original material of Cocos nucifera var. palmyrensis (Beccari) Pignotti & Baldini. A. Lectotype; B. Syntype. [A: FI018792; B: FI018793] [Photos: D. Nesti, L. Pignotti]
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.