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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>
JUMP - Data collection - Part II: Zonal jets using three different approaches, laboratory - Global Climate Models - observations.
<p>The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that <strong>(1)</strong> gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute <strong>(2)</strong> theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes</p> <p>We deliver (1) data collection and (2) numerical codes in the following files attached:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-GRL.pdf</strong> that describes the following data files and nomenclature.</li> <li>A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids <ul> <li><strong>JUMP-JetsInTheLab.zip</strong></li> </ul> </li> <li>A netcdf file of velocity fields of our Saturn reference simulation <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> </ul> </li> <li>Two netcdf files of velocity fields from Cassini observations of Jupiter<strong> </strong> <ul> <li><strong>uvData-JupObs-istep-0-nstep-4-niz-1.nc</strong></li> <li><strong>StatisticalData-JupObs.nc</strong></li> </ul> </li> <li>A zip file of potential vorticity profiles for Saturn and Jupiter observations <ul> <li><strong>IPV-QGPV-Jupiter-Saturn.zip</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> <li>Codes for statistical analysis in cylindrical geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> <li>Codes for statistical analysis in cartesian geometry on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FJUMP&sa=D&sntz=1&usg=AFQjCNGUQ1YIFhSxBAg4Hl_5gOLB_4LxLA">https://github.com/scabanes/JUMP</a></li> </ul> <p> </p> <p>The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver <strong>(1)</strong> velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and <strong>(2)</strong> guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.</p>
Modeled flux data from the Peat Collapse-Saltwater Intrusion Field Experiment within Everglades National Park (FCE), collected from October 2014 to September 2016
With sea level rise increasing, saltwater intrusion into low-lying coastal wetlands is likely to occur. We simulated saltwater intrusion into an Everglades marsh through monthly additions of elevated salinity water. Monthly modeled ecosystem flux measurements were calculated from a brackish water and freshwater marsh. Ecosystem flux was measured 24 hours after dosing. Measurements occurred from Oct 2014 - Sep 2016. Ecosystem flux measured includes gross ecosyetem production, ecosystem respiration of CO2, net ecosystem production, and ecosystem respiration of CH4. These data are published in Wilson, B.J., Servais, S., Mazzei, V., Davis, S.E., Kelly, S., Gaiser, E., Kominoski, J.S., Richards, J., Rudnick, D., Sklar, F., Stachelek, J., and Troxler, T.G. Salinity pulses interact with seasonal dry-down to increase ecosystem carbon loss in marshes of the Florida Everglades. Ecological Applications. Accepted.
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 mm Macro lens and processed with Agisoft Photoscan. The visual comparison of the mollusc shows a similar level of detail between photogrammetry and MechScan for the external surfaces, with still a bit more detail for the MechScan. The HDI Advance has a much lower resolution.
Fig. 6.14. Ishango rod. The left 3D model was acquired with a in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.14. Ishango rod. The left 3D model was acquired with a µCT many years ago. The middle one is scanned with the MechScan structured light scanner. The right one is the combination of both the µCT scan, the structured light scan and the texture of the photogrammetry model.
Model Collection of the Business Process Management Academic Initiative
<p>This model collection contains 29,810 models. The collection originates from the BPM Academic Initiative (BPMAI). In 2012, the initial members of this initiative were the following institutions represented by the corresponding professors: Mathias Weske (HPI, University of Potsdam), Marlon Dumas (University of Tartu), Marcello La Rosa (University of Melbourne), Jan Mendling (WU Vienna), Hajo A. Reijers (Utrecht University), Michael Rosemann (Queensland University of Technology, Australia), Jan Recker (University of Cologne, Germany), Wil van der Aalst (RWTH Aachen, Germany), Michael zur Mühlen (Stevens Institute of Technology, Hoboken, NJ) and Frank Leymann (University of Stuttgart), respectively, complemented by Dr. Gero Decker (Signavio GmbH, Berlin). The vendor Signavio provides a <a href="http://www.signavio.com/en/academic.html">free workspace</a> to the members of the BPM Academic Initiative and the models being created are made available for reseach purposes on the Creative Commons licence.</p> <p>The BPM Academic Initiative collection comprises tens of thousands of models, of various process modeling languages, and size. Models of the collection are available in several revisions, which opens a new perspective in researching the way people model.<br> The BPMAI collection is shared in a JSON format. Tools have been developed at HPI, University of Potsdam for efficiently processing these files. See the <a href="https://github.com/tobiashoppe/promnicat">PromniCat project</a>. Note that this project is no longer further developed, so you have to work with the information available there. Various analysis techniques can be applied for these models using the <a href="https://code.google.com/archive/p/jbpt/">jBPT library</a> or <a href="https://code.google.com/archive/p/apromore/">Apromore</a>.</p>
Word2Vec Models built from a Collection of French 20th-Century Novels
<p>The models were trained using the Gensim library for Python, developed by Radim Rehurek, in 2017. All models are based on the same collection of 20th century French novels that covers the period from 1900 to 2010, with a large range of authors and genres respresented. The collection contains approximately 1,200 novels and about 60 million tokens.</p> <p>The models were created using the SGNS (Skip-Gram with Negative Sampling) architecture, the context window was always of size of 6 + 6 around the target word, and the texts were lemmatised and POS-tagged beforehand. POS-Tags remain attached to each token (as in "souris_nom"). Other parameters vary by model: some have 200, some have 300 dimensional vectors; the minimum frequency of the words in the model varies with values of 50, 100 and 200, something which influences the size of the vocabulary and the size of the model. </p>
Environmental data at the sampling event level collected with Inline instruments, almanach, models and satellites during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples. The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide at the sampling event level, the environmental data originating from all instruments acquiring continuously during the full course of the campaign. This dataset is augmented with the addition of variables originating from almanach (local sun/moon set/rise, local zenith), from operational models obtained from Copernicus Marine Services, but also <strong>f</strong>rom satellite imagery (MODIS-AQUA satellite - Level 3 mapped product, 8 day average, 4km resolution) at <a href="https://oceandata.sci.gsfc.nasa.gov">https://oceandata.sci.gsfc.nasa.gov</a>. The zone corresponding to the station position and date was recovered either by taking a two pixel buffer around the given location (total zone being a 5 by 5 pixels square of 20 km side) and in order to propose an alternative measure in the inevitable case where clouds were present an alternative 12 pixels buffer was taken (total zone being a 25 by 25 pixels square of 100 km side). All data were provided as mean, standard deviation (sd) together with 0.05, 0.25, 0.5, 0.75 and 0.95 quartiles</p>
Data collection for article "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar's Forest Frontier Landscape"
<p>This dataset contains the nine ecosystem service models (in .neta format) underlying the publication "Quantifying Local Ecosystem Service Outcomes by Modelling Their Supply, Demand and Flow in Myanmar’s Forest Frontier Landscape". The ecosystem models were implemented using the commercial software Netica (version 6.05) for constructing and analysing Bayesian Networks.</p>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (Decision-Making Model)
<p>When considering the complex challenges faced by cultural heritage organisations in collecting, preserving and sharing born digital and hybrid objects, it becomes clear that the process of defining solutions as a community of practice is in its early probing phase: characterised as tentative, exploratory, questioning, experimental. The workshops within this Preserving and sharing born-digital and hybrid objects from and across the National Collection project, which examined the case studies from multiple angles, yielded a richly discursive examination of the main considerations.</p> <p>This Decision Model represents an attempt to create a structured representation of those main considerations and the discourse from the workshops, to codify the main decision-making processes that an organisation may go through when assessing an acquisition of such an object, categorised into high level areas. It attempts to create a traversable system that could be used by collections professionals in their work - policy makers, managers, collections management or digital preservation practitioners, conservators.</p>
Data from: Collection methods and distribution modeling for Strepsiptera in the United States
<p>The twisted-wing parasite order (Strepsiptera Kirby, 1813) is difficult to study due to the complexity of strepsipteran life histories, small body sizes, and a lack of accessible distribution data for most species. Here, we present a review of the strepsipteran species known from New York State. We also demonstrate successful collection methods and a survey of species carried out in an old-growth deciduous forest dominated by native New York species (Black Rock Forest, Cornwall, NY) and a private site in the Catskill Mountains (Shandaken, NY). Additionally, we model suitable habitat for Strepsiptera in the United States with species distribution modeling. We base our models on host distributions and climatic variables to inform predictions of where these twisted-wing parasites are likely to be found. With this work, we hope to provide a useful reference for the future collection of Strepsiptera.</p>
Research project on field data collection for honey bee colony model evaluation - datasets
<p><strong>Description of the datasets</strong></p> <p>The file 00_MUSTB_field_data_model.docx contains the data model according to which the data collected in the context of the MUSTB field data collection were reported to EFSA. The current data model description includes some modifications with respect to the specifications published before the beginning of the project (EFSA, 2017, https://doi.org/10.2903/sp.efsa.2017.EN-1234). All the tables included in the data model are published here in csv format. The underlying schemas are also published in xsd format.</p> <p>Sites: General information about the sites where the data collection took place;</p> <p>Polygons: General information about the polygons where the botanical survey took place.</p> <p>Table I: Pesticide application, reporting data on experimental spraying events;</p> <p>Table II: Resource providing unit and landscape fitness, reporting data on abundance of flowering plants in polygons mostly within 1.5 km, but in some cases up to 3 km of the experimental colony;</p> <p>Table III: Master list of all hives included in the study;</p> <p>Table IV: Colony management, reporting the log of the beekeeper regarding input (if material was added to the hive: e.g. empty frames, chemicals for varroa treatment, sugar), output (if material was removed from the hive, e.g. honey combs, supers), queen loss, swarming, or clinical signs observed in the experimental hives;</p> <p>Table V: Hive inspection, reporting data on in-hive measurements in the experimental colonies. This table contained several types of data, including:</p> <ul> <li>Data on brood development and food provision (“cell utilization”) obtained from image analysis of combs;</li> <li>Data on forager activity obtained from automatic video recordings and image analysis by a bee counter;</li> <li>Data on hive weight obtained from automatic logging by a hive scale;</li> <li>Data on adult bee strength, obtained by weight assessment of combs with and without adult bees (“bees per comb data”);</li> </ul> <p>Table VI: SSD2, reporting data on results of laboratory analyses of pollen, pesticide residues and parasites/pathogens. These four types of laboratory analyses involved different methods, and were reported according to different standards. Therefore, a number of the fields in the technical specifications for the SSD2 table (EFSA, 2017) were not applicable for records reporting results of some analyses, in particular palynological, parasite and pathogen analyses. These fields were left empty;</p> <p>Table VII: Colony observation, reporting observations of honey bee waggle dances from observation hives. Orientation denotes the angle of the waggling phase relative to the vertical axis on the comb. Direction denotes the actual direction in the landscape, as calculated from the orientation of the waggle dance.</p> <p>In all the csv files, columns with the suffix "_desc" have been included, where relevant, to include the name corresponding to the EFSA controlled terminology used in the previous column (e.g. resUnit contains EFSA term codes while resUnit_desc contains the term names).</p> <p><strong>Data storage</strong></p> <p>All data collected during the project was stored in a relational database. The database was developed in .NET Entity Framework Core, ran on a PostgreSQL, and was hosted by Amazon Web Service during the whole duration of the project development. Data could be imported or entered manually in the database through a web form. Administrators could create new users and administrators, new sites, and new colonies, i.e., administrators were allowed to enter or change data of all tables. Users were allowed to enter data, and could view, retrieve, and modify their own data of all tables, except for Table III (description of experimental colonies). Administrators could view and retrieve all data. Data was retrieved in CSV and XML formats, and were structured to secure a smooth transmission of data to the Data Collection Framework of EFSA. Furthermore, data flow from the field data collection to the development of ApisRAM was secured by direct communication between the field and modelling teams.</p> <p> </p> <p><strong>Version 2</strong> contains the UTM coordinates in tables Sites, Polygons and Resource providing unit.</p>
Supplemental material for "Liquid argon light collection and veto modeling in GERDA Phase II"
<p>Data set for Fig. 7 of "Liquid argon light collection and veto modeling in GERDA Phase II", scientific publicaton by the GERDA Collaboration.</p>
CESM and FOCI model data as supplementary data for Climate Index Collection based on model data (CICMoD)
<p>The Community Earth System Model (CESM) and the Flexible Ocean and Climate Infrastructure (FOCI) are both fully-coupled, global climate models that provide state-of-the-art computer simulations of the Earth's past, present, and future climate states.</p> <p>This dataset contains results from control runs with conditions of year 1850 without additional external forcing for 1000 years and 999 years for FOCI and CESM, respectively.</p> <p>Included features are:</p> <ul> <li>sea surface temperature</li> <li>surface air temperature</li> <li>sea level pressure</li> <li>sea surface salinity</li> <li>geopotential height (500mb)</li> <li>precipitation</li> </ul>
Data for: Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data
<p>An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order estimate the actual probability of occupancy (i.e., proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. The data archived here are used to explore two ways in which the single-visit records could be used in occupancy models. First, we use empirical data contained within this archive to assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. The archived data are used to illustrate that the locations chosen for data collection by observers were not always representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Second, create a large set of simulated data (output from the simulations contained in this archive) that we used to explore the utility of including data from single-visit records to supplement sets of repeated-visit data.</p>
Figs 51, 52.Ant species collected from 20 in A redescription of Merenius alberti Lessert, 1923 (Araneae: Corinnidae), with remarks on colour polymorphism and its relationship to ant models
Figs 51, 52.Ant species collected from 20 sites sampled in the Ndumo Game Reserve during June–July (51) and November–December 2009 (52) by pitfall trapping over a 10-day period that may be potential models for Merenius alberti Lessert, 1923. Numbers above each column indicate the total number of potential model ants sampled (Table 1), followed in parenthesis by the number of black and red morphs of M. alberti collected by hand at each site. Red crosses indicate sites where no potential ant models or M. alberti were collected. Blue bars – Streblognathus peetersi Robertson, 2002; orange bars – Anoplolepis custodiens (F. Smith, 1858); maroon bars – Camponotus cinctellus (Gerstäcker, 1859); turquoise bars –?Atopomyrmex mocquerysi André, 1889; yellow bars – Odontomachus troglodytes Santschi, 1914; green bars – Polyrhachis gagates F. Smith, 1858; red bars –?Pachycondyla caffraria (F. Smith, 1858).
Data from: Collection methods and distribution modeling for Strepsiptera in the United States
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Data for: Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data
Open the record for dataset details and reuse information.
JUMP - Data collection - Part I: Jets from a Global Climate Model.
<p>We conduct in-depth analysis of statistical flow properties from Global Circulation Model that reproduce Saturn's macroturbulence, namely large-scale zonal winds. We use a high performance Global Climate Models (GCMs), named DYNAMICO, to model the atmospheric circulation of gas giants with appropriate physical parametrizations for Saturn's atmosphere. The high-resolution model DYNAMICO solves for 3D primitive equations of motion. We ran a Saturn simulation covering 15 Saturn years using the Saturn DYNAMICO GCM. Wind fields are output every 20 Saturn days at 32 pressure levels onto 1/2° latitude-longitude grid maps. Details on this Saturn reference simulation are given in Spiga et al. (2020). In addition, to diagnose the relevant 3D dynamical mechanisms in Saturn's turbulent atmosphere, we run a set of four simulations using an idealized version of our Global Climate Model devoid of radiative transfer, with a well-defined Taylor-Green forcing and over several rotation rates (4, 1, 0.5, and 0.25 times Saturn's rotation rate). Here, we deliver a full data set, including velocity maps, at different pressure levels and time steps, from which it is possible to recompute the statistical analysis detailed in Cabanes et al. (2020). The delivered data set includes:</p> <p>Files of our (1) data collection and (2) numerical codes that lead to the statistical analysis:</p> <p>(1) Data collection:</p> <ul> <li>A PDF file named <strong>JUMP-zonal-jets-data-collection-Icarus.pdf</strong> that describes in depththe data set and the associated nomenclature.</li> <li>A netcdf file of velocity fields from our Saturn Reference Simulation (SRS) <ul> <li><strong>uvData-SRS-istep-312000-nstep-50-niz-12.nc</strong></li> <li><strong>StatisticalData.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 4 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-4-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 1 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-1-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.5 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.5-istep-20626.0-nstep-20-niz-8.nc</strong></li> </ul> </li> <li>A netcdf file of velocity fields from idealized simulation at 0.25 times the Satrun's rotation rate, <ul> <li><strong>uvData-Omega-0.25-istep-21026.0-nstep-20-niz-8.nc</strong></li> </ul> </li> </ul> <p>(2) Numerical codes:</p> <ul> <li>Codes for statistical analysis in spherical geometry are on Github. --> <a href="https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fscabanes%2FPOST&sa=D&sntz=1&usg=AFQjCNFuDU0eij4XGxQfReO92CHfJz6PBA">https://github.com/scabanes/POST</a></li> </ul> <p> </p> <p><strong>Acknowledgments</strong>:</p> <p>The authors acknowledge exceptional computing support from Grand Équipement National de Calcul Intensif (GENCI) and Centre Informatique National de l’Enseignement Supérieur (CINES). All the simulations presented in this paper were carried out on the Occigen cluster hosted at CINES. This work was granted access to the High-Performance Computing (HPC) resources of CINES under the allocations A001-0107548, A003-0107548, A004-0110391 made by GENCI. The authors acknowledge funding from Agence Nationale de la Recherche (ANR), project HEAT ANR-14-CE23-0010 and project EMERGIANT ANR-17-CE31-0007. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N° 797012. Fruitful discussions with Sandrine Guerlet, Ehouarn Millour, Thomas Dubos, Frédéric Hourdin and Alexandre Boissinot from our team helped refine some discussions in the paper.</p>
Data from: Climate-mediated hybrid zone movement revealed with genomics, museum collection and simulation modeling
Climate-mediated changes in hybridization will dramatically alter the genetic diversity, adaptive capacity and evolutionary trajectory of interbreeding species. Our ability to predict the consequences of such changes will be key to future conservation and management decisions. Here we tested through simulations how recent warming (over a 32-year period) is affecting the geographic extent of a climate-mediated developmental threshold implicated in maintaining a butterfly hybrid zone (Papilio glaucus and Papilio canadensis; Lepidoptera: Papilionidae). These simulations predict a 68 km shift of this hybrid zone. To empirically test this prediction, we assessed genetic and phenotypic changes using contemporary and museum collections and document a 40 km northward shift of this hybrid zone. Interactions between the two species appear relatively unchanged during hybrid zone movement. We found no change in the frequency of hybridization and regions of the genome that experience little to no introgression moved largely in concert with the shifting hybrid zone. Model predictions based on climate scenarios predict this hybrid zone will continue to move northward, but with substantial spatial heterogeneity in the velocity (55-144 km/1°C), shape, and contiguity of movement. Our findings suggest that the presence of non-climatic barriers (e.g., genetic incompatibilities) and/or non-linear responses to climatic gradients may preserve species boundaries as the species shift. Further, we show that variation in the "geography" of hybrid zone movement could result in evolutionary responses that differ for geographically distinct populations spanning hybrid zones and thus have implications for the conservation and management of genetic diversity.
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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.