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3,684 results for “Extraction”
Extracted trails from airborne laser scanning in the Oostvaardersplassen nature reserve
<p>Ungulates and other mammalian herbivores can create trails in dense vegetation by trampling and browsing. This can affect vegetation structure and results in the fragmentation of closed, high vegetation, with subsequent impacts on biodiversity. Manually mapping trails in the field or from aerial photographs can be challenging and time consuming, especially in inaccessible or difficult to access habitats such as wetlands and if trails occur beneath the canopy. Airborne laser scanning provides an alternative method because it penetrates vegetation canopies and efficiently obtains highly accurate data in the form of dense 3D point clouds. This repository consists of the extracted trails in wetland area of the Oostvaardersplassen nature reserve in the Netherlands using 3D airborne point cloud data (AHN4) and the manually created 50 plots of ground truth in two regions, i.e. grazed only by red deer and grazed by both red deer and geese. </p>
CoAID dataset with multiple extracted features (both sparse and dense) and degraded by OCR
<p>This is the same datasets as:</p> <p>Guillaume Bernard. (2022). CoAID dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630405</p> <p>But with texts degraded by OCR as described in:</p> <p>Guillaume Bernard. (2022). CoAID dataset texts with OCR degradations (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630710</p>
Fibvid dataset with multiple extracted features (both sparse and dense)
<p>This is a publication of the FibVid dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents.</p> <p>Kim, Jisu, Jihwan Aum, SangEun Lee, Yeonju Jang, Eunil Park, et Daejin Choi. 2021. « FibVID: Comprehensive Fake News Diffusion Dataset during the COVID-19 Period ». <em>Telematics and Informatics</em> 64 (novembre): 101688. <a href="https://doi.org/10.1016/j.tele.2021.101688">https://doi.org/10.1016/j.tele.2021.101688</a>.</p> <p>In this dataset, we provide multiple features extracted from the text itself. <strong>Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.</strong></p> <p>Features are extracted using:</p> <p>- A corpus of reference articles in multiple languages languages for TF-IDF weighting. (<em>features_news</em>) [1]</p> <p>- A corpus of tweets reporting news for TF-IDF weighting. (<em>features_tweets)</em> [1]</p> <p>- A S-BERT model [2] that uses <em>distiluse-base-multilingual-cased-v1 </em>(called <em>features_use</em>) [3]</p> <p>- A S-BERT model [2] that uses <em>paraphrase-multilingual-mpnet-base-v2 </em>(called <em>features_mpnet</em>) [4]</p> <p><strong>References:</strong></p> <p>[1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406</p> <p>[2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</em>, 3982‑92. Hong Kong, China: Association for Computational Linguistics. <a href="https://doi.org/10.18653/v1/D19-1410">https://doi.org/10.18653/v1/D19-1410</a>.</p> <p>[3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1</p> <p>[4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2</p>
Event Registry dataset with multiple extracted features (both sparse and dense)
<p>This is a republication of the Event Registry dataset originaly published by:</p> <p>Rupnik, Jan, Andrej Muhic, Gregor Leban, Primoz Skraba, Blaz Fortuna, et Marko Grobelnik. 2016. « News Across Languages - Cross-Lingual Document Similarity and Event Tracking ». <em>Journal of Artificial Intelligence Research</em> 55 (janvier): 283‑316. <a href="https://doi.org/10.1613/jair.4780">https://doi.org/10.1613/jair.4780</a>.</p> <p>And reorganised for document tracking by:</p> <p>Miranda, Sebastião, Artūrs Znotiņš, Shay B. Cohen, et Guntis Barzdins. 2018. « Multilingual Clustering of Streaming News ». In <em>2018 Conference on Empirical Methods in Natural Language Processing</em>, 4535‑44. Brussels, Belgium: Association for Computational Linguistics. <a href="https://www.aclweb.org/anthology/D18-1483/">https://www.aclweb.org/anthology/D18-1483/</a>.</p> <p>In this dataset, we provide multiple features extracted from the text itself. <strong>Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.</strong></p> <p>Features are extracted using:</p> <p>- A corpus of reference articles in multiple languages languages for TF-IDF weighting. (<em>features_news</em>) [1]</p> <p>- A corpus of tweets reporting news for TF-IDF weighting. (<em>features_tweets)</em> [1]</p> <p>- A S-BERT model [2] that uses <em>distiluse-base-multilingual-cased-v1 </em>(called <em>features_use</em>) [3]</p> <p>- A S-BERT model [2] that uses <em>paraphrase-multilingual-mpnet-base-v2 </em>(called <em>features_mpnet</em>) [4]</p> <p><strong>References:</strong></p> <p>[1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406</p> <p>[2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</em>, 3982‑92. Hong Kong, China: Association for Computational Linguistics. <a href="https://doi.org/10.18653/v1/D19-1410">https://doi.org/10.18653/v1/D19-1410</a>.</p> <p>[3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1</p> <p>[4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2</p>
DATASET: characterization of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel
<p>Characterizarion of the seed coat extractable phenolic profile and color in 308 common bean lines of the Spanish Diversity Panel</p>
Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from EEG Time Series
<h2>The files include the 20 training datasets, in csv format, from 20 epileptic patients. Each set of data is described by 1080 features extracted using the sliding window technique.</h2>
Drug Indications Extracted from FAERS
<p>This dataset contains drug indications extracted from the FDA Adverse Event Reporting System (<a href="https://www.fda.gov/drugs/guidancecomplianceregulatoryinformation/surveillance/adversedrugeffects/">FAERS</a>).</p> <p>Source code here: <a href="https://github.com/stuppie/faers">https://github.com/stuppie/faers</a></p> <p><strong>Method Outline</strong></p> <ul> <li>Data files are extracted from zip files, parsed from csvs, and imported into a MySQL database (see parser.py).</li> <li>Duplicate records are then de-duplicated by taking only the most recent version for each case ID (see dedupe.py).</li> <li>Indications are normalized by matching to UMLS terms by string matching. Cross-references to Human Phenotype Ontology are pulled from UMLS and xrefs to Monarch Disease Ontology (MONDO) are pulled from MONDO using the UMLS xrefs. (See normalize_indications.py)</li> <li>Drugs names are normalized first by applying a few simple string cleaning operations (strip, fix slashes and periods). Then they are attempted to be matched to rxnorm by exact string matching. Those that don't match are run against rxnorm's <a href="https://rxnav.nlm.nih.gov/RxNormAPIs.html#uLink=RxNorm_REST_getApproximateMatch">approximate matching service</a>, and are accepted if the score is higher than 67/100. The matched rxnorm CUIs are then mapped to the their Ingredient level rxnorm ID. (See normalize_drugs.py)</li> <li>Indications are then retrieved for each drug ingredient and filtered to require a minimum of 20 individual occurances. (See get_indications.py)</li> </ul>
CrowdTruth Corpus for Open Domain Relation Extraction from Sentences
<p>This repository contains a ground truth corpus for open domain relation extraction from sentences, acquired with crowdsourcing and processed with <strong><a href="http://crowdtruth.org/">CrowdTruth</a></strong> metrics that capture ambiguity in annotations by measuring inter-annotator disagreement.</p> <p>The dataset contains annotations for 4,100 sentences sampled from Angeli et al. (1) and Riedel et al. (2), over 16 relations, with each sentence annotated by 15 workers. The sentences have been pre-processed with Distant Supervision (3) using the Freebase knowledge base, in order to identify the term pairs in each sentence that are likely to express a relation. The crowdsourced data was collected from <a href="http://figure-eight.com/">Figure Eight</a> and <a href="https://www.mturk.com/">Amazon Mechanical Turk</a>.</p> <p>This corpus has been discussed in the following papers:</p> <ul> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="https://arxiv.org/abs/1809.00537">Crowdsourcing Semantic Label Propagation in Relation Classification</a></strong>. <a href="http://fever.ai/">FEVER</a> Workshop at <a href="http://emnlp2018.org/">EMNLP 2018</a>.</li> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="https://arxiv.org/abs/1711.05186">False Positive and Cross-relation Signals in Distant Supervision Data</a></strong>. <a href="http://www.akbc.ws/">AKBC</a> Workshop at <a href="http://nips.cc/">NIPS 2017</a>.</li> <li>Anca Dumitrache, Lora Aroyo and Chris Welty: <strong><a href="http://crowdtruth.org/wp-content/uploads/2017/03/collint17-open-domain.pdf">Disagreement in Crowdsourcing and Active Learning for Better Distant Supervision Quality</a></strong>. <a href="http://collectiveintelligenceconference.org/">Collective Intelligence 2017</a>.</li> </ul> <p>Sentence-level data is available in file: <code>|--data/output/aggregated_sentences.csv</code></p> <p>Worker-level data is available in file: <code>|--data/output/aggregated_workers.csv</code></p> <p>Raw crowdsourcig data is available in folder: <code>|--data/input/</code></p> <p>Results of the relation classification model are available in folder: <code>|--data/model_results/</code></p> <p> </p> <p>References</p> <p>(1) Angeli, Gabor, et al. "Combining distant and partial supervision for relation extraction." Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.</p> <p>(2) Riedel, Sebastian, et al. "Relation extraction with matrix factorization and universal schemas." Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). 2013.</p> <p>(3) Mintz, Mike, et al. "Distant supervision for relation extraction without labeled data." Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2. Association for Computational Linguistics, 2009.</p>
MiRoR11 - P3 - Primary outcomes extracted from registries
<p>Two sets (original and deduplicated) of primary outcomes from trial registries</p>
Microbial biomass and water-extractable carbon on Mt. Kilimanjaro
<p>This dataset presents the value of microbial biomass carbon (MBC) and water-extractable carbon (WOC) at study plots under KiLi project.</p> <p>Microbial biomass carbon (MBC) and water-extractable organic carbon (WOC) – as sensitive and important parameters for soil fertility and C turnover – are strongly affected by land-use changes all over the world. These effects are particularly distinct upon conversion of natural to agricultural ecosystems due to very fast carbon (C) and nutrient cycles and high vulnerability, especially in the tropics. The objective of this study was to use the unique advantage of Mt. Kilimanjaro – altitudinal gradient leading to different tropical ecosystems but developed all on the same soil parent material – to investigate the effects of land-use change and elevation on MBC and WOC contents during a transition phase from dry to wet season. Down to a soil depth of 50 cm, we compared MBC and WOC contents of 2 natural (<em>Ocotea</em> and <em>Podocarpus</em> forest), 3 seminatural (lower montane forest, grassland, savannah), 1 sustainably used (homegarden) and 2 intensively used (maize field, coffee plantation) ecosystems on an elevation gradient from 950 to 2850 m a.s.l.</p> <p>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</p>
Comparative Study of Entomotoxicity of Three Medicinal Plant Extracts against Sitophilus oryzae
<p>The Sitophilus oryzae is the most widespread and destructive primary stored cereals and grain pest in the world. The major effect of Sitophilus oryzae on an infestation by the feeding activity of grubs and adults and increasing the secondary growth of pests by making conditions optimum for optimum and further infestation. Plant extracts Azadirachta indica, Osmium Sanctum, and Mentha piperita were evaluated for Entomotoxicity such as repellency, adulticidal and larvicidal effect against Sitophilus oryzae. The Entomotoxicity of plant extracts expressed in percentage and Repellency were also expressed in class repellency with class 1, class 2, Class 3, Class 4, and class 5.TheRepellency with 80% of Class 4, Adulticidaland larvicidal percentage with 100 % of Azadirachta Indica and Adulticidal highest Entomotoxicity effect than Osmium sanctum and Mentha piperita</p>
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
Guinea baboon vocalizations dataset automatically extracted with a deep neural network from natural audio recordings
<p><strong>Abstract</strong></p> <p>The data collection process consisted of continuously recording during one month a group of Guinea baboons living in semi-liberty at the CNRS primatology center in Rousset-sur-Arc (France). Two microphones we placed nearby their enclosure to continuously record the sounds produced by the group. A convolutional neural network (CNN) was used on these large and noisy audio recordings to automatically extract segments of sound containing a baboon vocal production by following the method of <a href="https://arxiv.org/abs/2302.07640">Bonafos et al. (2023)</a>. The resulting dataset consists of one-second to several-minute wav files of automatically detected vocalizations segments. The dataset thus provides a wide range of baboon vocalizations produced at all times of the day. It can be used to study vocal productions of non-human primates, their repertoire, their distribution over the day, their frequency, and their heterogeneity. In addition to the analysis of animal communication, the dataset can also be used as a learning base for sound classification models.</p> <p> </p> <p><strong>Data acquisition</strong></p> <p>The data are audio recordings of baboons. The recordings were made with a H6 Zoom recorder, using the included XYH-6 stereo microphone. The sample size is 44100 Hertz, 16 bits. The microphones were placed in the vicinity of the enclosure for one month and recorded continuously on a PC computer. A CNN passed over the data with a sliding window of 1 second and an overlap of 80% to detect the vocal productions of the baboons. The dataset consists of the segments predicted by the CNN to contain a baboon vocalization. Windows containing signal less than one second apart were merged into a single vocalization.</p> <p> </p> <p><strong>Data source location</strong></p> <ul> <li>Institution: CNRS, Primate Facility</li> <li> <p>City/Town/Region: Rousset-sur-Arc</p> </li> <li> <p>Country: France</p> </li> <li> <p>Latitude and longitude for collected samples/data: 43.47033535251509, 5.6514732876668905</p> </li> </ul> <p> </p> <p><strong>Value of the data</strong></p> <ul> <li> <p>This dataset is relatively unique in terms of the quantity of vocalizations available.</p> </li> <li> <p>This massive dataset can be very useful to two types of scientific communities: experts in primatology who study the vocal productions of non-human primates, and experts in data science and audio signal processing.</p> </li> <li> <p>The machine learning research community has at its disposal a database of several dozen hours of animal vocalizations, which will make it possible to build up a large learning base, very useful for Environemental Sound Recognition tasks, for example.</p> </li> </ul> <p> </p> <p><strong>Objective</strong></p> <p>This dataset is a follow-up of two studies on the vocal productions of Guinea baboons (Papio papio) in which we carried out analyses of their vocal productions on the basis of a relatively large vocalization sample containing around 1300 vocalizations (<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169321">Boë, Berthommier, Legou, Captier, Kemp, Sawallis, Becker, Rey, & Fagot, 2017</a>; <a href="https://hal.science/hal-01649539">Kemp, Rey, Legou, Boë, Berthommier, Becker, & Fagot, 2017</a>). The aim was to collect a larger database using the technique of deep convolutional neural networks in order to 1) automatically detect vocal productions in a large continuous audio recording and 2) perform a categorization of these vocalizations on a more massive sample. A description of the pipeline that enabled these automatic detections and categorizations is given in <a href="https://arxiv.org/abs/2302.07640">Bonafos, Pudlo, Freyermuth, Legou, Fagot, Tronçon, & Rey (2023)</a>.</p> <p> </p> <p><strong>Data description</strong></p> <p>The data is a set of audio files in wav format. They are at least one second long (the size of the window), up to several minutes, if several windows are consecutively predicted as containing signal. Moreover, we add the labeled data we used to train the CNN which did the prediction. We also provide two hours of the continuous recordings to have an idea of the continuous recordings and test the code of the paper provided on <a href="https://gitlab.com/papers4375727/detection-and-classification-of-vocal-productions">gitlab</a>.</p> <p>In addition, there is a database in csv format listing all the vocalizations, the day and time of their production, and the prediction probabilities of the model.</p> <p> </p> <p><strong>Experimental design, materials and methods</strong></p> <p>The original recordings represent one month of continuous audio recording. Seven hours of this month were manually labelled. They were segmented and labelled according to whether or not there was a monkey vocalization (i.e., noise or vocalization) and, if there was a vocalization, according to the type of vocalization (6 possible classes: bark, copulation grunt, grunt, scream, yak, wahoo). These manually labelled data were used as a training set for a CNN, which was automatically trained following the pipeline of Bonafos et al. (2023). This model was then used to automatically detect and classify vocalization during the whole month of audio recording. It processes the data in the same way when predicting new data as it does when training. It uses a sliding window of one second with an overlap of 80%. It does not take into account information from previous predictions, but calculates the probability of a vocalization in each one-second window independently. It then iterates through the month. For each window, the model predicts two outputs: the probability that there is a vocalization and the probability of each class of vocalization.</p> <p>For the purpose of generating the wav files, if a window has a probability of a vocalization greater than 0.5, it is considered to contain a vocalization. If it is the first one, a vocalization is started at that moment. If the time windows that follow a vocalization also contain a vocalization, then the signal they contain is added to the first segment for which a vocalization has been detected. As soon as a one-second segment no longer contains a signal corresponding to a vocalization, the wav file is closed. If windows are predicted to contain no vocalizations, but are between two windows that contain vocalizations within 1 second of each other, then all windows are merged.</p>
Workflow for detecting biomedical articles with openly available underlying datasets - Datasets and extraction forms
<p>The open data screening datasets contain both automatically detected (TRUE) Open Data statements by <a href="https://github.com/quest-bih/oddpub">ODDPub</a>, and its manual validation using <a href="https://github.com/bgcarlisle/Numbat">Numbat</a> extraction tool. Furthermore, extraction forms for both screenings – 2020 and 2021 – are included. The manually processed dataset for the calculation of the inter-rater reliability of manual validation can be also found here. </p> <p>(i) Data from articles published in 2020 (file ‘<em>charite_open_data_2020.csv</em>’) have been collected applying a slightly different sequence of questions in the extraction workflow than the articles published in 2021 (file ‘<em>charite_open_data_2021.csv</em>’). Both datasets were cleaned for any personal data or internal comments. Thus, they do not contain the default columns which in the raw export from Numbat contained commentaries regarding different question. Also, in another regard these files do not represent raw outputs of the Numbat extraction tool, but a processed version. This means that articles validated by more than two raters were first reconciled in Numbat, resulting in one final decision (output of extractions <strong>after reconciliation</strong>). Then from the output of extractions <strong>before reconciliation</strong> those articles validated by only 1 rater (and thus not part of the inter-rater reliability calculation) were selected, which were afterwards joined with the already reconciled dataset. </p> <p>The actual decision about Openness of validated dataset can be analysed in various ways: </p> <ol> <li>Column ‘<em>open_data_assessment</em>’/’<em>assessment</em>’ shows a binary decision between Open Data TRUE and FALSE. </li> <li>If that column indicates ‘<em>NULL</em>’, the dataset was classified into ‘non’-open category, and the result can be found on one of the following ways: <ul> <li>Column ‘<em>reference_to_data</em>’ as ‘<em>n_a</em>’ for excluded articles, e.g. not producing any data.</li> <li>Column ‘<em>data_access</em>’ as ‘<em>restricted</em>’. </li> <li>Column ‘<em>own_or_reuse_data</em>’ as ‘<em>open_data_reuse</em>’. </li> </ul> </li> </ol> <p>The original extraction form contains an option ‘unsure_open_data’ besides ‘<em>open_data</em>’/’<em>no_open_data</em>’ which was resolved either during reconciliation between multiple raters or by case-related consultation with a second rater in case of doubt, and is not included here. </p> <p>(ii) The inter-rater reliability calculation was made on randomly selected 100 articles for 2 raters. The third rater screened 20 articles sample, which is part of 100 sample. The tables provided here include both article-level data, and dataset-level data. </p> <p>(iii) The Numbat extarction forms used for the screenings in 2020 and 2021 are included in two formats - JSON and Markdown.</p> <p>(iv) ‘<em>data_dictionary_open_data.csv</em>’ table documents all variables of each data file containing here. </p>
Data Source: Synergistic effects of precipitation and groundwater extraction on freshwater wetland inundation
Wetlands provide essential ecosystem services, including nutrient cycling, flood protection, and biodiversity support, that are sensitive to changes in wetland hydrology. Wetland hydrological inputs come from precipitation, groundwater discharge, and surface run-off. Changes to these inputs via climate variation, groundwater extraction, and land development may alter the timing and magnitude of wetland inundation. Data were compiled for 152 wetlands in west-central Florida over 14 years to investigate the response of wetland inundation to the interactive effects of precipitation, groundwater extraction, surrounding land development, basin geomorphology, and wetland vegetation class. Further methods are defined in the Methods section of the journal article associated with this dataset (Synergistic effects of precipitation and groundwater extraction on freshwater wetland inundation, published in the Journal of Environmental Management, 2023).
Chlorophyll and phaeopigments measured from discrete bottle samples from CCE LTER process cruises in the California Current System, determined by extraction and bench fluorometry, 2006 - 2024 (ongoing).
Discrete bottle samples taken from various depths in the CCE region are filtered (known volumes) onto GF/F filters onboard the CCE Process cruises (since 2006, ongoing). The filters are placed into culture tubes containing 90% acetone, and the fluorescence of the samples is read on a fluorometer after 24 to 48 hours. The samples are then acidified to degrade the chlorophyll to phaeopigments (non-photosynthetic pigments) and a second reading is taken. The readings prior to and after acidification are used to calculate concentrations of both chlorophyll a and phaeopigments (i.e. phaeophytin).
Chlorophyll determined by extraction of samples taken approximately weekly from seawater intake starting at Palmer Station by station personnel including during winter-over period, 1991-2024.
Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Chlorophyll a is determined weekly year-round at the laboratory seawater intake (SWI), from a depth of 6 meters. Concentrations are typically very low (< 1 µg Chl a per liter) in winter (April-October), and higher (1-30 µg/L) following the initiation of the annual spring-summer phytoplankton bloom in November - January.
Hypernym-LIBre: A free Web-based corpus from Hypernym Detection [ Hearst Pattern extractions from Hypernym-LIBre]
<p>Hypernym-LIBre ( DOI: 10.5281/zenodo.3662204 ) is a free Web-based corpus for Hypernym detection.</p> <p>Its part-of-speech tagged and dependency annotated version is present at this: (DOI: 10.5281/zenodo.3689303)</p> <p>Here we provide the hypernym-hyponym pairs that were extracted from Hypernym-LIBre using Hearst patterns. This is to further the usage of these extractions with more techniques and methods. We also provide the counts of each pattern in a separate file.</p> <p> </p> <p>Format:</p> <p>hyponym \t hypernym</p> <p> </p> <p>Format for the counts file:</p> <p>pair \t frequency of extraction</p> <p> </p> <p>There are 2 files, one with the pairs, one with unique pair and its counts. Both total ~430MB.</p>
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods
<p>Non-targeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection (and refinement of it) is arguably the most important step for non-targeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255,000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of:</p> <ul> <li>51 converted mass spectral files in mzML format</li> <li>An .RData-file containing the extracted ion chromtograms (EICs)</li> <li>The randomly selected subset and the original output table of MZmine in .csv-format</li> <li>Example .xlsx files for the classification</li> <li>2 central classification tables</li> <li>Several tables with additional information about the sampling, chemical analysis and expert jugdement on EICs</li> </ul> <p>For a full description of the experiment and the data set, please read the related Data Descriptor with the title "A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods" in Metabolites (https://www.mdpi.com/journal/metabolites; DOI: https://doi.org/10.3390/metabo10040162).</p>
TESTAR Test results extracted while executing MyThaiStar as web system under test
<p>TESTAR test results datasets extracted with TESTAR tool using MyThaiStar web application as System Under Test (SUT). These datasets have been generated to be used as an example to be automatically generated and introduced locally in DECODER PKM, from H2020 DECODER Project.</p> <p>TESTAR tool is an open source tool (www.testar.org) for automated testing through graphical user interface (GUI) currently being developed by the Universitat Politecnica de Valencia and the Open University of the Netherlands.</p> <p>MyThaiStar (<a href="https://github.com/devonfw/my-thai-star">github.com/devonfw/my-thai-star</a>) is the reference application that Capgemini uses internally to promote best programming practices and the correct use of last technologies. It’s is developed with Devon Framework, the standard tool for development at the company.</p> <p>PKM is the Persistent Knowledge Monitor developed as main infrastructure from H2020 DECODER Project (www.decoder-project.eu) under grant agreement number 824231.</p> <p>As TESTAR explores automatically the SUT, it will apply a couple of oracles to automatically check if any error or exception is detected at the Document Object Model (DOM) level extracted from MyThaiStar SUT.</p> <p>- MyThaiStar_TestResults_dataset.rar: All the information obtained through the DOM is used graphically and semantically to create screenshots, logs and html reports that indicate how TESTAR has navigated in the exploration and indicates if any error has been detected. </p> <p>- ArtefactTestResults_MyThaiStar_2020.1_2020-06-15_12h14m24s.json: for DECODER project purposes, TESTAR test results knowledge has been summarized and referenced in an artifact JSON file to adapt to PKM input requirements.</p> <p> </p> <p> </p> <p> </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.