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Globally distributed lake surface water temperatures collected in situ and by satellites; 1985-2009
Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global dataset of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985-2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues. This dataset accompanies a data publication in the journal Scientific Data
Zooplankton collected with a 2-m, 700-um net towed from surface to 120 m, aboard Palmer Station Antarctica LTER annual cruises off the western Antarctic peninsula, 2009 - 2024.
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton and thus provide a link between primary producers and higher trophic levels. Zooplankton density and biovolume were determined at grid stations on the annual LTER cruises along the western Antarctic Peninsula (WAP). Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Typically, zooplankton were collected with a 2x2 meter, 700um mesh net fitted with a flow meter and towed obliquely to 120m. Zooplankton distributions vary spatially due to water column characteristics, which affect their predators' distributions. As climate change continues to affect the WAP, the relative abundance of the various zooplankton components can also be expected to change.
Standard body length of Euphausia superba collected with a 2-m, 700-um net towed from surface to 120 m, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009 - 2024.
Antarctic krill, Euphausia superba, are a critical food-web link between phytoplankton primary production and higher trophic levels, such as whales, penguins, and seals. Krill standard length was measured from LTER zooplankton tows along the western Antarctic Peninsula. Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Length data provides estimates of age-class abundance and recruitment. Climate-induced changes in krill recruitment are an important consideration in the management and modelling of krill populations.
Length of Salpa thompsoni collected with a 2-m, 700-um net towed from surface to 120 m, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009 - 2024.
Salps (Salpa thompsoni) are conspicuous gelatinous zooplankton capable of rapid population increases, enabling them to respond quickly to unpredictable phytoplankton blooms common in the Antarctic. Body length was measured on salps collected from LTER zooplankton tows along the western Antarctic Peninsula. Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Salps have amongst the highest filtration rates of all zooplankton, and package their waste into large, fast sinking fecal pellets. These pellets provide a mechanism to export carbon fixed in the surface waters into the deep ocean. Since filtration rates and pellet size are positively related to the size of a salp, population estimates of grazing and exported carbon can be determined through length data.
Zooplankton collected with a 1.4-m2 frame, 500-μm mesh Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009-2017
Zooplankton are a morphologically and taxonomically diverse group of animals. Many zooplankton feed on phytoplankton in surface waters and thus provide a link between primary producers and higher trophic levels. Other zooplankton reside in the mesopelagic zone and feed on detritus or on other animals. Depth-discrete density of zooplankton taxa was determined at process study stations on the annual Palmer LTER cruises along the western Antarctic Peninsula. Samples were collected with a 1.4-m2 frame, 500-μm mesh Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) towed obliquely to the surface from a depth of typically 500 m. MOCNESS tows were conducted in consecutive day-night pairs at each process study station. Zooplankton depth distributions vary between day and night as these animals conduct diel vertical migrations. Depth distributions also vary among zooplankton taxa based on species feeding ecology and life history traits. Zooplankton diel vertical migration contributes to the export of carbon and nutrients from the surface ocean to the mesopelagic zone.
Nekton individual data from flume net collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The flume nets were deployed with the purpose of capturing salt marsh nekton. Nekton species were identified to the lowest taxonomic level using species keys. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Nekton species counts and density from flume net collections along Rowley River tidal creeks associated with long term fertilization experiments, Rowley, MA.
The flume nets were deployed with the purpose of capturing salt marsh nekton. Nekton species were identified to the lowest taxonomic level using species keys. The TIDE project aims to simulate eutrophication on a large scale by the addition of NO3- aiming to reach 70μM concentrations from May to September every year during the growing season. This fertilization of the marsh has been going on at Sweeney Creek since the 2004 growing season through 2016 and at Clubhead Creek in 2005 and from 2009 till 2016. Years 2017-2020 are enrichment recovery years.
Models for "A data-driven approach to studying changing vocabularies in historical newspaper collections"
<p>NOTE: This is a badly rendered version of the README within the archive.</p> <p><strong>A data-driven approach to studying changing vocabularies in historical newspaper collections</strong></p> <p>Simon Hengchen,* Ruben Ros,** Jani Marjanen,*** Mikko Tolonen***</p> <p>*<a href="https://spraakbanken.gu.se/en/about/staff/simon">Språkbanken Text</a>, University of Gothenburg, Sweden and <a href="https://iguanodon.ai">iguanodon.ai</a>, Belgium: firstname.lastname@gu.se<br> **<a href="https://www.c2dh.uni.lu/people/ruben-ros">Centre for Contemporary and Digital History (C2DH)</a>, University of Luxembourg: firstname.lastname@uni.lu<br> ***<a href="https://www.helsinki.fi/en/researchgroups/computational-history">COMHIS</a>, University of Helsinki: <a href="mailto:firstname.lastname@helsinki.fi">firstname.lastname@helsinki.fi</a>;</p> <p>These are the supplementary materials for the DH2019 paper <em>A data-driven approach to the changing vocabulary of the ‘nation’ in English, Dutch, Swedish and Finnish newspapers, 1750-1950</em>, as well as the 2021 Digital Scholarship in the Humanities publication available in OpenAccess: <a href="https://academic.oup.com/dsh/article/36/Supplement_2/ii109/6421793">https://academic.oup.com/dsh/article/36/Supplement_2/ii109/6421793</a>. If you end up using whole or parts of this resource, please use the following citation(s):</p> <ul> <li>Hengchen, S., Ros, R., and Marjanen, J. (2019). A data-driven approach to the changing vocabulary of the 'nation' in English, Dutch, Swedish and Finnish newspapers, 1750-1950. In <em>Proceedings of the Digital Humanities (DH) conference 2019, Utrecht, The Netherlands</em></li> </ul> <p>and/or:</p> <ul> <li>Hengchen, S., Ros, R., Marjanen, J. and Tolonen, M., 2021. A data-driven approach to studying changing vocabularies in historical newspaper collections. Digital Scholarship in the Humanities, 36(Supplement_2), pp.ii109-ii126.</li> </ul> <p>or alternatively use one of the following <code>bib</code>s:</p> <pre><code>@inproceedings{hengchen2019nation, title="A data-driven approach to the changing vocabulary of the 'nation' in {E}nglish, {D}utch, {S}wedish and {F}innish newspapers, 1750-1950.", author={Hengchen, Simon and Ros, Ruben and Marjanen, Jani}, year={2019}, address = "Utrecht, The Netherlands", booktitle={Proceedings of the Digital Humanities (DH) conference 2019} }</code></pre> <pre><code>@article{hengchen2021data, title={A data-driven approach to studying changing vocabularies in historical newspaper collections}, author={Hengchen, Simon and Ros, Ruben and Marjanen, Jani and Tolonen, Mikko}, journal={Digital Scholarship in the Humanities}, volume={36}, number={Supplement\_2}, pages={ii109--ii126}, year={2021}, publisher={Oxford University Press} }</code></pre> <p> </p> <p>Files</p> <p>This archive contains two folders -- one per diachronic representation method -- as well as this README. The folders each contain four folders, which contain the models for their respective languages. As can be inferred from the small datasize, most of the earlier models are not reliable and should not be used, but are still made available. This work is licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p> <p><strong>Source material</strong></p> <p>Finnish:</p> <p>The models were created with data from the Finnish Sub-corpus of the Newspaper and Periodical Corpus of the National Library of Finland (National Library of Finland, 2011). We used everything in the corpus.</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h fi* 12M fi_1820_SGNS_corpus_file.gensim 89M fi_1840_SGNS_corpus_file.gensim 797M fi_1860_SGNS_corpus_file.gensim 7.0G fi_1880_SGNS_corpus_file.gensim 22G fi_1900_SGNS_corpus_file.gensim</code></pre> <p>Swedish:</p> <p>The models were created with data from the Kubhist 2 corpus (Språkbanken) -- more precisely, the data dumps available at <a href="https://spraakbanken.gu.se/lb/resurser/meningsmangder/">https://spraakbanken.gu.se</a>. After a manual evaluation of Swedish embeddings trained without pre-processing seemed to show that the embeddings were of low quality, we retrained models, only keeping sentences that were at least 10 tokens long and were constituted of at least 50% of lemmas as per the KORP processing pipeline (Borin et al, 2012).</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h sv* 1.6M sv_1740_SGNS_corpus_file.gensim 44M sv_1760_SGNS_corpus_file.gensim 124M sv_1780_SGNS_corpus_file.gensim 228M sv_1800_SGNS_corpus_file.gensim 678M sv_1820_SGNS_corpus_file.gensim 1.6G sv_1840_SGNS_corpus_file.gensim 4.5G sv_1860_SGNS_corpus_file.gensim 6.5G sv_1880_SGNS_corpus_file.gensim 113M sv_1900_SGNS_corpus_file.gensim</code></pre> <p>Dutch:</p> <p>The models were created with data from the Delpher newspaper archive (Royal Dutch Library, 2017), through data dumps for newspapers until and including 1876, and through API hits for articles from 1877 to 1899 (included).</p> <ul> <li>For anything pre-1877 we discarded full texts that had, in the metadata, anything else than exclusively <code>nl</code> or <code>NL</code> as a language tag.</li> <li>For the full texts between 1877 and 1899: we queried the API for all items in the “artikel” category that contained the determiner <code>de</code>.</li> </ul> <p>Our assumption was that most articles should contain <code>de</code> at least once, and those that didn't were too short to be deemed interesting. A subsequent study showed that was not exactly the case, but we were reassured by the fact that left-out articles were probably "shipping or financial reports" (thanks go to Melvin Wevers). We also did not include the colonial newspapers for our embeddings. This is motivated by our research questions. A list of removed newspapers is available on request.</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h nl* 6.8M nl_1620_SGNS_corpus_file.gensim 7.9M nl_1640_SGNS_corpus_file.gensim 43M nl_1660_SGNS_corpus_file.gensim 78M nl_1680_SGNS_corpus_file.gensim 138M nl_1700_SGNS_corpus_file.gensim 243M nl_1720_SGNS_corpus_file.gensim 287M nl_1740_SGNS_corpus_file.gensim 431M nl_1760_SGNS_corpus_file.gensim 825M nl_1780_SGNS_corpus_file.gensim 1.2G nl_1800_SGNS_corpus_file.gensim 1.8G nl_1820_SGNS_corpus_file.gensim 3.1G nl_1840_SGNS_corpus_file.gensim 5.2G nl_1860_SGNS_corpus_file.gensim 13G nl_1880_SGNS_corpus_file.gensim</code></pre> <p>English:</p> <p>The models were created with data from the British Library Newspapers collection (<a href="https://www.gale.com/intl/primary-sources/british-library-newspapers%5D">link</a>), the Nichols collection (<a href="https://www.gale.com/intl/c/17th-and-18th-century-burney-newspapers-collection">link</a>), and the Burney collection (<a href="https://www.gale.com/intl/c/17th-and-18th-century-nichols-newspapers-collection">link</a>). We used everything in the corpora. For English, only SGNS_ALIGN models are available. We thank Gale Cengage for their help with this project.</p> <p>Filesizes:</p> <pre><code>[simon@taito-login3 SGNS]$ du -h en* 4.3M en_1620_SGNS_corpus_file.gensim 11M en_1640_SGNS_corpus_file.gensim 11M en_1660_SGNS_corpus_file.gensim 106M en_1680_SGNS_corpus_file.gensim 409M en_1700_SGNS_corpus_file.gensim 1.7G en_1720_SGNS_corpus_file.gensim 834M en_1740_SGNS_corpus_file.gensim 2.4G en_1760_SGNS_corpus_file.gensim 5.3G en_1780_SGNS_corpus_file.gensim 5.5G en_1800_SGNS_corpus_file.gensim 15G en_1820_SGNS_corpus_file.gensim 42G en_1840_SGNS_corpus_file.gensim 65G en_1860_SGNS_corpus_file.gensim 88G en_1880_SGNS_corpus_file.gensim 26G en_1900_SGNS_corpus_file.gensim 21G en_1920_SGNS_corpus_file.gensim 6.3G en_1940_SGNS_corpus_file.gensim</code></pre> <p><strong>Word embeddings</strong></p> <p>For every language, we train diachronic embeddings as follows. We divide the data in 20-year time bins. We train SGNS_UPDATE and SGNS_ALIGN models. Current research on German (Schlechtweg et al, 2019) and English (Shoemark et al, 2019) indicates you should use the SGNS_ALIGN models. <strong>For EN, FI, NL, no tokens (including punctuation) were removed nor altered, aside from lowercasing</strong>. For SV, see above. Parameters are as follows: SGNS architecture (Mikolov et al 2013), window size of 5, frequency threshold of 100, 5 epochs, 300 dimensions (or 100 for EN).</p> <ul> <li>For SGNS_UPDATE: We first train a model for the first time bin <code>t</code>. To train the model for <code>t+1</code>, we use the <code>t</code> model to initialise the vectors for <code>t+1</code>, set the learning rate to correspond to the end learning rate of <code>t</code>, and continue training. This approach, closely following Kim et al (2014), has the advantage of avoiding the need for post-training vector space alignment.</li> </ul> <p>The Python snippet below, which makes use of gensim (Rehurek and Sojka, 2010), illustrates the approach. Special thanks go to Sara Budts.</p> <pre><code>## dict_files[key] is a dictionary with double decades as keys and a corresponding LineSentence object as value: https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.LineSentence count = 0 for key in sorted(list(dict_files.keys())): if count == 0: ## This is the first model. model = gensim.models.Word2Vec(corpus_file=dict_files[key], min_count=100, sg=1 ,size=300, workers=64, seed=1830, iter=5) model.save(os.path.join(data_path_final,"KIM",lang+"_"+str(timebin)+".w2v")) print("Model saved, on to the next\n") count += 1 if count > 0: ## this is for the subsequent models. print("model for double decade starting in",str(key)) model = gensim.models.Word2Vec.load(os.path.join(data_path_final,"KIM",lang+"_"+str(timebin-20)+".w2v")) print("previous model loaded") model.build_vocab(corpus_file=dict_files[key], update=True) model.train(corpus_file=dict_files[key], total_words = model.corpus_count, total_examples = model.corpus_count, start_alpha = model.alpha, end_alpha = model.min_alpha, epochs=model.epochs) model.save(os.path.join(data_path_final,"KIM",lang+"_"+str(timebin)+".w2v")) </code></pre> <ul> <li>For SGNS_ALIGN: We independently train models for all time bins. The models in this repository are <em>NOT</em> aligned, leaving you the choice of how to align them. For example, <a href="https://gist.github.com/quadrismegistus/09a93e219a6ffc4f216fb85235535faf">here</a> is a link to code by Ryan Heuser to do just that. Models were trained with the <code>count == 0</code> scenario in the snippet above.</li> </ul> <p><strong>Acknowledgments</strong></p> <p>This work has been supported by the European Union's Horizon 2020 research and innovation programme under grant 770299 <a href="https://www.newseye.eu/">NewsEye</a>. Specials thanks go to the data providers/collection-holding institutions: the Finnish Language Bank, the Swedish Language Bank, the Royal Dutch Library, and Gale Cengage.</p> <p>The authors would like to thank the following persons and group, listed alphabetically: Antoine Doucet, Antti Kanner, Axel-Jean Caurant, Dominik Schlechtweg, Eetu Mäkelä, Elaine Zosa, Estelle Bunout, Haim Dubossarsky, Joris van Eijnatten, Krister Lindén, Lars Borin, Lidia Pivovarova, Melvin Wevers, Nina Tahmasebi, Sara Budts, Senka Drobac, Tanja Säily, the COMHIS group, and Steven Claeyssens. Computational resources were provided by CSC – IT Center for Science Ltd.</p> <p><strong>References</strong></p> <p>Borin, L., Forsberg, M., Roxendal, J. (2012). Korp-the corpus infrastructure of Spräkbanken,in: LREC. pp. 474–478.</p> <p>Kim, Y., Chiu, Y.I., Hanaki, K., Hegde, D. and Petrov, S. (2014). Temporal Analysis of Language through Neural Language Models. <em>ACL 2014</em>, p.61.</p> <p>Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2013). Efficient estimation of word representations in vector space. <em>arXiv preprint arXiv:1301.3781</em>.</p> <p>National Library of Finland (2011). <em>The Finnish Sub-corpus of the Newspaper and Periodical Corpus of the National Library of Finland, Kielipankki Version</em> [text corpus]. Kielipankki. Retrieved from <a href="http://urn.fi/urn:nbn:fi:lb-2016050302">http://urn.fi/urn:nbn:fi:lb-2016050302</a>.</p> <p>Rehurek, R. and Sojka, P. (2010). Software framework for topic modelling with large corpora. In <em>Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks</em>.</p> <p>Royal Dutch Library (2017). <em>Delpher open krantenarchief (1.0)</em>. Den Haag, 2017.</p> <p>Schlechtweg D., Hätty A, del Tredici M., and Schulte im Walde S. (2019). A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains. In <em>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</em>, Florence, Italy. ACL.</p> <p>Shoemark, P., Liza, F.F., Nguyen, D., Hale, S. and McGillivray, B. (2019). Room to Glo: A Systematic Comparison of Semantic Change Detection Approaches with Word Embeddings. 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) (pp. 66-76)</em>, Hong Kong.</p> <p>Språkbanken. <em>The Kubhist Corpus</em>. Department of Swedish, University of Gothenburg. <a href="https://spraakbanken.gu.se/korp/?mode=kubhist">https://spraakbanken.gu.se/korp/?mode=kubhist</a>.</p>
Darwin: an amino acid sequence collection of complete proteomes from eukaryotes with different phylogenetic affinities (v. 03_2020_137)
<p><strong>Background</strong></p> <p>Every time we find an interesting gene in an organism of interest, the first question is often “how widely is this gene distributed in the eukaryotic kingdom?”. Naturally, one could use NCBI BLAST search against the non-redundant sequence database provided by GenBank to answer this question. However, it can be cumbersome to parse the results and assign them to taxonomic units. It is also not straightforward to get an overview of which eukaryotic groups are represented in the results. Top BLAST hits can be crowded with sequences from closely-related organisms making it difficult gain an overview of the overall distribution across eukaryotes. To streamline this process, we developed an in-house database of complete eukaryotic proteomes. We tagged each sequence with a eukaryotic group handle (two-character symbol) and combined them into a single data set searchable by standalone BLAST on one’s own computer. We named this data set “Darwin” to reflect the diverse nature of the sequences it contains. </p> <p><strong>Methods</strong></p> <p>We downloaded predicted proteomes in FASTA format from different sources such as GenBank, Joint Genome Institute (Depart of Energy, USA), Broad Institute (Massachusetts Institute of Technology, USA), Phytozome and a number of other specialized websites catering for a specific organism such as the Arabidopsis Information Resource (TAIR), or the Saccharomyces Genome Database (SGD). All the organisms we included in Darwin are listed in Table 1. To reduce redundancy, we took care not to include the same species more than once unless subspecies were known to show wide diversity. Each sequence header was tagged with a eukaryotic group handle composed of two-character symbols (based on Keeling <em>et al</em>., 2005). These handles clearly appear in BLAST output and can be parsed easily. We combined sequences from all proteomes into a single data set and named it “Darwin”.</p> <p><strong>Results</strong></p> <p>The current version of Darwin (v. 03_2020_137) contains 2,601,132 amino acid sequences from 137 eukaryotes (Table 1, Data file 1). The sizes of the proteomes were diverse, ranging from ~4000 sequences in some alveolates to 60,000-76,000 in plants. Darwin represents most of the supergroups of eukaryotic kingdom described in Keeling <em>et al.,</em> (2005) except those in Rhizaria whose genomes were not available at the time of data set construction. The data set contains larger numbers of proteomes from fungi and plants reflecting areas of interest in our group. </p> <p><strong>Conclusions</strong></p> <p>Darwin is provided as a text fasta file that can be formatted for BLAST searches on standalone computers. The results from the BLAST searches can be parsed to determine how widely a gene of interest is distributed among different eukaryotes. Simple counting of the eukaryotic group handles would also yield an overview of the distribution across taxa. Darwin is also useful for rapidly finding out whether a gene is missing in particular taxa.</p> <p><strong>Reference</strong></p> <p>Keeling PJ, Burger G, Durnford DG, Lang BF, Lee RW, Pearlman RE, Roger AJ, Gray MW (2005) The tree of eukaryotes. <em>Trends Ecol. Evol.</em> <strong>20:</strong> 670-676</p>
Raw spectra measurements of scattered sunlight collected using a MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between the ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the raw spectra measurements of scattered sunlight recorded by the MAX-DOAS onboard a research vessel in the Southern Ocean and Atlantic Ocean. Included are position and vessel inclination data. Data coverage is from December 2016 to April 2017.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_maxdoas_gps.zip</li> <li>GPS_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_inclination.zip</li> <li>Inclination_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_spectra-YYYY-MM.zip</li> <li>- MAXDOAS<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text<br> - ZENITH<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text</li> <li>README.txt, metadata, text</li> <li>data_file_header_gps.txt, metadata, text</li> <li>data_file_header_inclination.txt, metadata, text</li> <li>data_file_header_spectra_atmos.txt, metadata, text</li> <li>data_file_header_spectra_liveinfo.txt, metadata, text</li> </ul> <p>where YYYY is the year and MM is the month. JDDD is the day of the year (Julian day) YYYY in which the file was recorded. hhmmss is the time. WWW is the central wavelength of the measured spectrum in the UV or VIS region.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw spectra of scattered sunlight measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Electromagnetic data (FDEM and ERT) collected in the Venice coastland (Zennare basin)
<p>FDEM and ERT data collected southern of the Venice lagoon (Italy) in the Zennare basin in 2019-2020.</p> <p>FDEM_ZENNARE37.csv: Raw output of Quadrature and Inphase values for the 6 frequencies adopted with the GEM2 FDEM probe.<br> ERT_ROUGHoutput_Zennare.dat: Apparent resistivity data as retrieved with the 48 channels Syscal Pro georesistivimeter ERT.</p>
Dataset and Scripts for: RefPlantNLR: a comprehensive collection of experimentally validated plant NLRs (v.20200528_415)
<p><strong>RefPlantNLR v.20200528_415</strong></p> <p><strong>See </strong>bioRxiv 2020.07.08.193961; doi: <a href="https://doi.org/10.1101/2020.07.08.193961">https://doi.org/10.1101/2020.07.08.193961</a></p> <p>SUPPLEMENTAL DATA</p> <p>Table S1: Description of RefPlantNLR.</p> <p>Table S2: Plant orders represented in RefPlantNLR.</p> <p>Supplemental dataset 1: Amino acid sequences of RefPlantNLR entries (fasta format). This file contains 415 amino acid sequences.</p> <p>Supplemental dataset 2: CDS sequences of RefPlantNLR entries (fasta format). This file contains 400 CDS sequences. CDS sequences could not be retrieved for 15 RefPlantNLR entries.</p> <p>Supplemental dataset 3: Annotated genomic sequences of RefPlantNLR entries (GenBank flat file format). This file contains 329 genomic loci containing the gene models of 344 RefPlantNLR entries and 56 RefPlantNLR mRNA entries lacking genomic information.</p> <p>Supplemental dataset 4: InterProScan annotation of the RefPlantNLR amino acid sequences (GFF3 format). This file contains the InterProScan annotation of 415 amino acid sequences.</p> <p>Supplemental dataset 5: InterProScan annotation of the RefPlantNLR CDS sequences (GFF3 format). This file contains the InterProScan annotation of the 400 CDS sequences.</p> <p>Supplemental dataset 6: Amino acid sequences of the extracted RefPlantNLR NB-ARC domains (fasta format). This file contains 424 NB-ARC domain (SUPERFAMILY signature SSF52540) amino acid sequences belonging to 415 RefPlantNLR entries.</p> <p>Supplemental dataset 7: Amino acid sequences of the unique RefPlantNLR extracted NB-ARC domains (fasta format). This file contains 347 unique NB-ARC domain (SUPERFAMILY signature SSF52540) amino acid sequences.</p> <p>Supplemental dataset 8: Clustal Omega alignment of the unique RefPlantNLR extracted NB-ARC domains (PHYLIP format). This file contains the Clustal Omega alignment of 346 unique NB-ARC domains (SUPERFAMILY signature SSF52540) with all positions with less than 95% coverage removed. Pb1 was omitted from this alignment.</p> <p>Supplemental dataset 9: NB-ARC domain phylogeny of the RefPlantNLR entries using the Maximum likelihood method (Newick format). This file contains the phylogenetic analysis of the NB-ARC domain of the RefPlantNLR entries using the JTT method.</p> <p>Supplemental dataset 10: Amino acid sequences of the non-redundant RefPlantNLR entries (fasta format). This file contains 235 amino acid sequences representing the non-redundant RefPlantNLR entries at a 90% amino acid identity threshold per genus according to the NB-ARC domain.</p> <p>Supplemental dataset 11: Amino acid sequences of the NB-ARC domains of the non-redundant RefPlantNLR entries (fasta format). This file contains 241 amino acid sequences representing the extracted NB-ARC domains of the 235 non-redundant RefPlantNLR.</p> <p>Appendix S1: R script used to generate annotations and figures.</p> <p>Appendix S2: InterProScan descriptions used for generating annotations.</p>
200 kHz pre-processed echosounder data collected on the Antarctic Circumnavigation Expedition during the austral summer of 2016/2017.
<p><strong>Dataset abstract </strong></p> <p>These data consist of pre-processed echosounder observations in the Southern Ocean collected during the Antarctic Circumnavigation Expedition (ACE; Leg2-Leg3) using an EK60 GPT operating at 200 kHz. The instrument was calibrated at South Georgia during the expedition (Leg 3) and corrections were applied prior to calculation of the volume backscattering strength (Sv). The signal-to-noise ratio (SNR) was analysed and was deemed very poor at depths greater than 100 m. Therefore, only data collected between the transducer depth (8.4 m) and 100 m were archived.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACE-DYYYYMMDD-THHMMSS.csv, data files, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This 200 kHz pre-processed echosounder data collected on ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Summary raw meteorological data from the Southern Ocean collected on board the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.
<p><strong>Dataset abstract</strong></p> <p>A Vaisala MAWS240 meteorological station was installed on the R/V Akademik Tryoshnikov during a circumnavigation of Antarctica in the austral summer season of 2016/2017. This dataset contains the raw meteorological data that have been extracted from the original raw text data files. Data coverage is from 17th November 2016 until 11th April 2017, with gaps where the ship was in port.</p> <p>Air temperature, relative humidity, dew point, solar radiation, ultraviolet radiation, cloud level and sky cover were recorded with a resolution of 30 seconds. Averaged wind parameter data are provided.</p> <p>Date_time should be combined with TIMEDIFF to convert it to UTC. Latitude and longitude recorded are not corrected. Underway seawater measurements were recorded as null values.</p> <p>Data from this dataset have been corrected and quality-checked in another published dataset. We recommend these data for further use (Landwehr et al., 2019; DOI 10.5281/zenodo.3379590).</p> <p><strong>Dataset contents</strong></p> <ul> <li>metdata_all_YYYYMMDD_YYYYMMDD.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>ace_meteorology_raw_summary_change_log.txt</li> </ul> <p>Data files contain data for each leg of the Antarctic Circumnavigation Expedition (ACE). Dates included in the file name are the start and end dates of the legs and therefore the data within the files as well.</p> <p><strong>Change log</strong></p> <p><strong>v1.2</strong> - Added missing data from 2017-02-05 - 2017-02-08 inclusive. Updated this change log file.</p> <p><strong>v1.1</strong> - Added additional data coverage from 2016-11-17 - 2016-11-22 inclusive, into the first data file. Updated README.txt with information about data coverage. Added this change log file.</p> <p><strong>v1.0</strong> - Initial release of raw summary meteorological data.</p> <p><strong>Dataset license</strong></p> <p>This raw meteorological dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
ADS-C Air Traffic Data Collected by the OpenSky Network
<p>ADS-C data collected by the OpenSky Network since 7th July 2023. </p> <p>Data underlying (Version 1.1)</p> <h1>A First Look at Exploiting the Automatic Dependent Surveillance-Contract Protocol for Open Aviation Research</h1> <p>https://journals.open.tudelft.nl/joas/article/view/7229</p>
Human intestinal Bacteria Collection (HiBC): Isolates and genomes metadata
<p>The <a href="https://hibc.rwth-aachen.de/" target="_blank" rel="noopener">Human intestinal Bacteria Collection (HiBC)</a> is a collection of bacterial strains, isolated from the human gut for which 16S rRNA gene sequences, genome sequences and culture conditions are made available to the research community. In addition to previously described bacteria, we include strains that represent novel species which have been taxonomically described and validly named, or will be in the future. This collection will be updated regularly.</p> <p>This dataset includes the taxonomy of the isolates, as well as metadata regarding their cultivation and isolation. We also provide metadata regarding the sequencing, genome assembly process and the biological sequences.</p> <p><strong>UPDATE v7</strong>: INSDC accession for <em>Segatella sinensis</em> CLA-AA-H117 was a missing value and is now the correct value of GCA_040324585.2.</p> <p><strong>UPDATE v6: </strong>The growth atmosphere is now indicated by anaerobic or aerobic instead of "Anaerobe/Aerobe" that was a misleading term. The risk group of these two isolates went from 1 to 2:</p> <ul> <li>CLA-AA-H205: <em>Anaerostipes caccae </em></li> <li>CLA-AA-H83: <em>Bacteroides fragilis</em></li> </ul> <p>The risk group of the following isolates has been updated (usually from unknown to 1, or from 2 to 1):</p> <ul> <li>CLA-SR-H026: <em>Aedoeadaptatus acetigenes</em></li> <li>CLA-KB-H139:<em> Bacteroides xylanisolvens</em></li> <li>CLA-SR-H015: <em>Bacteroides xylanisolvens</em></li> <li>CLA-AA-H187: <em>Blautia fusiformis</em></li> <li>CLA-AA-H274: <em>Brotaphodocola catenula</em></li> <li>CLA-AA-H286: <em>Butyricimonas faecihominis</em></li> <li>CLA-AA-H278:<em> Clostridium fessum</em></li> <li>CLA-AA-H147: <em>Dorea ammoniilytica</em></li> <li>CLA-SR-H027: D<em>orea formicigenerans</em></li> <li>CLA-KB-H89: <em>Dorea longicatena</em></li> <li>CLA-KB-H94: <em>Dorea longicatena</em></li> <li>CLA-SR-H022: <em>Enterococcus lactis</em></li> <li>CLA-AA-H250: <em>Hominenteromicrobium mulieris</em></li> <li>CLA-AA-H232: H<em>ominilimicola fabiformis</em></li> <li>CLA-AA-H246: <em>Hominisplanchenecus faecis</em></li> <li>CLA-AA-H276:<em> Hominiventricola filiformis</em></li> <li>CLA-AA-H213:<em> Oliverpabstia intestinalis</em></li> <li>CLA-AA-H241: <em>Oliverpabstia intestinalis</em></li> <li>CLA-AA-H58: <em>Pilosibacter fragilis</em></li> <li>CLA-KB-H110: <em>Ruthenibacterium lactatiformans</em></li> <li>CLA-AA-H174: <em>Segatella sinensis</em></li> <li>CLA-AA-H2: <em>Veillonella parvula</em></li> <li>CLA-AA-H273: <em>Waltera acetigignens</em></li> </ul> <p>Typos in media list have been fixed. </p> <p><strong>UPDATE v5</strong>: The accessions number for the genomes on INSDC databases are added under the column Accession. Plus two typos in the risk group column have been corrected as follow:</p> <ul> <li>CLA-AA-H173: from Risk Group 4 (!) to 2 like the other strain of <em>Sutterella wadsworthensis</em></li> <li>CLA-AA-H198: from Risk Group 4 (!) to 1 like the other <em>Bifidobacterium </em>species.</li> </ul> <p><strong>UPDATE v4</strong>: Only the taxonomy of a couple of isolates has been changed, as follow:</p> <ul> <li>CLA-ER-H4: <em>Collinsella sp900547855</em> instead of <em>Collinsella sp900544645</em></li> <li>CLA-AA-H142: <em>Pilosibacter fragilis</em> (<em>f__Clostridiaceae</em>) instead of <em>Sakamotonia hominis gen. nov.</em> (<em>f__Lachnospiraceae</em>)</li> <li>CLA-AA-H58: <em>Pilosibacter fragilis </em>(<em>f__Clostridiaceae</em>) instead of <em>Sakamotonia hominis gen. nov. </em>(<em>f__Lachnospiraceae</em>)</li> <li>CLA-AA-H89B: <em>Lachnospira intestinalis sp. nov.</em> instead of <em>Lachnospira hominis sp. nov.</em></li> <li>CLA-JM-H10: <em>Lachnospira hominis sp. nov.</em> instead of <em>Lachnospira intestinalis sp. nov.</em></li> <li>CLA-JM-H7B: <em>Faecalibacterium taiwanense</em> instead of <em>Faecalibacterium faecis sp. nov.</em></li> <li>CLA-JM-H45: <em>Merdimmobilis hominis</em> instead of <em>Hominicola intestinalis gen. nov.</em></li> </ul> <p><strong>UPDATE v3</strong>: The genome of one of our isolate had been unfortunately swapped. This mistake has been now corrected on Zenodo and Coscine. The genome of <em>Segatella sinensis</em> CLA-AA-H117 should be considered correct with 103 contigs and 3 671 232 nt. Please note that the genome available at the NCBI is the correct one (GCA_040324585.2). Two typos regarding taxonomy have been corrected as well: <em>Maccoya intestinihominis</em> has been corrected to <em>Maccoyia intestinihominis</em> and <em>Faecousia faecis</em> to <em>Faecousia intestinalis</em>.</p>
Towards standardising the collection of game statistics in Europe: a dataset
<p>This dataset is part of the article:</p><p>Title : <strong>Towards standardising the collection of game statistics in Europe: a case study</strong></p><p>Journal:<i><strong> European Journal of Wildlife Research.</strong></i><br><strong>DOI : 10.1007/s10344-023-01746-3</strong></p><p>Two different data sets have been incorporated : <br>1) Data collected from a questionnaire to regional governmental hunting agencies (mainland Spain)</p><p><a href="https://zenodo.org/api/records/10080464/draft/files/QuestionnaireData_DOI_10.1007_s10344-023-01746-3.csv/content">QuestionnaireData_DOI_10.1007_s10344-023-01746-3.csv</a></p><p>Metadata with variable vocabulary and descriptors has been included</p><p><a href="https://zenodo.org/api/records/10080464/draft/files/QuestionnaireMetadata_DOI_10.1007_s10344-023-01746-3.docx/content">QuestionnaireMetadata_DOI_10.1007_s10344-023-01746-3.docx</a></p><p>2) Characterisation of each of the Autonomous Communities, including information on economic and human resources and the volume or coverage of hunting resources available in each of the regional administrations. </p><p><a href="https://zenodo.org/api/records/10080464/draft/files/SocioEconomicData_DOI_10.1007_s10344-023-01746-3.csv/content">SocioEconomicData_DOI_10.1007_s10344-023-01746-3.csv</a></p><p>Metadata with variable vocabulary and descriptors has been included</p><p><a href="https://zenodo.org/api/records/10080464/draft/files/SocioEconomicMetadata_DOI_10.1007_s10344-023-01746-3.docx/content">SocioEconomicMetadata_DOI_10.1007_s10344-023-01746-3.docx</a></p><p> </p><p>Please remember to cite correctly the doi associated to this databases <strong>10.5281/zenodo.10080464</strong></p><p> </p>
3D-COSI ~ 3D Collection of Surgical Instruments
<h2><strong>COSI - 3D STL Collection of Surgical Instruments</strong></h2><p><i><strong>Due to large file names, we have chosen to use </strong></i><a href="https://www.7-zip.org/"><i><strong>https://www.7-zip.org/</strong></i></a><i><strong> which is 100% free and compatible with WinZip. If you encounter an error using WinZip, it's likely due to large file names, please use 7zip.</strong></i></p><p>Inside the repository, you will find an information overview "<i>Overview.docx", </i>a showcase video "<i>Example video 3D instruments.mp4"</i>, STL files of 103 surgical instruments "<i>Surgical Instruments.7z"</i>, examples of variations of the surgical instruments using Blender add-on or Python script build on the Trimesh library "<i>Blender Part x of 8 ... 7z"</i>, or "<i>Trimesh part x of 9 ... .7z". </i>You will also find the used Blender Add On <i>"MultiMesh.zip", </i>measurements of virtual instruments and settings for the add-on (.xlsx), and the script that was used to perform these measurements inside a single folder, "<i>Scripts, measurements and used Blender settings.7z</i>".</p><p>The proposed data collection consists of 103 3D-scanned medical instruments from the clinical routine, scanned with structured light scanners. The collection consists, for example, of instruments like retractors, forceps, and clamps. The collection is augmented by generating likewise models using 3D software, resulting in an inflated dataset for analysis. The collection can be used for general instrument detection and tracking in operating room settings or a freeform marker-less instrument registration for tool tracking in augmented reality. Furthermore, for medical simulation or training scenarios in virtual reality or mixed reality.<br><br><strong>Related article:</strong><br>Luijten, G., Gsaxner, C., Li, J. <i>et al.</i> 3D surgical instrument collection for computer vision and extended reality. <i>Sci Data</i> <strong>10</strong>, 796 (2023). https://doi.org/10.1038/s41597-023-02684-0</p>
Snow depth, snow water equivalent, ice thickness in Fuglebekken and Revdalen catchments collected in the SnowPilot campaign in Spring 2022
<p>File SnowPilot_snowdepth_along_the_GPR_profile_2022 contains snow depth measurements taken along the GPR profile performed during the SIOS SnowPilot campaign in Spring 2022. File SnowPilot_snowdepth_swe_2022 contains depth, snow water equivalent and basal ice thickness. Snowpits were dug on GPR profile crossings in the Fuglebekken and Revdalen catchments in the Hornsund fiord, Spitsbergen catchment. Snow density was measured with an IG PAS snow tube, and snow depth and basal ice (ice forming on the ground surface) thickness were measured with an avalanche probe. Point locations measured. with handheld GPR reciever.</p>
Collection de romans français du dix-huitième siècle (1750-1800) / Collection of Eighteenth-Century French Novels (1750-1800)
<p><strong>Key information</strong>: This collection of Eighteenth-Century French Novels contains digital texts of novels created or first published between 1751 and 1800. The collection is created in the context of Mining and Modeling Text, a project at the Trier Center for Digital Humanities (TCDH) at Trier University, Germany (2019-2023). The current release contains 200 novels.</p><p><strong>Further information</strong>: <a href="https://github.com/MiMoText/roman18">https://github.com/MiMoText/roman18</a></p><p><strong>Citation suggestion: </strong><i>Collection de romans français du dix-huitième siècle (1751-1800) / Eighteenth-Century French Novels (1751-1800)</i>, edited by Julia Röttgermann, with contributions from Julia Dudar, Henning Gebhard, Anne Klee, Johanna Konstanciak, Damir Padieu, Amelie Probst, Sarah Rebecca Ondraszek and Christof Schöch. Release v.1.2.0. Trier: TCDH, 2023. URL: <a href="https://github.com/mimotext/roman18">https://github.com/mimotext/roman18</a>; DOI: <a href="https://doi.org/10.5281/zenodo.10349902">10.5281/zenodo.10349902</a>. </p><p> </p><p> </p>
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.