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49 results for “Historical changes”
National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide
<p>A complete description of the dataset is given by <a href="http://doi.org/10.1038/s41597-023-02041-1">Jones et al. (2023)</a>. Key information is provided below.</p> <p><strong>Background</strong></p> <p>A dataset describing the global warming response to national emissions CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from fossil and land use sources during 1851-2021.</p> <p>National CO<sub>2 </sub>emissions data are collated from the Global Carbon Project (Andrew and Peters, 2024; Friedlingstein et al., 2024). </p> <p>National CH<sub>4</sub> and N<sub>2</sub>O emissions data are collated from PRIMAP-hist (HISTTP) (Gütschow et al., 2024).</p> <p>We construct a time series of cumulative CO2-equivalent emissions for each country, gas, and emissions source (fossil or land use). Emissions of CH<sub>4</sub> and N<sub>2</sub>O emissions are related to cumulative CO2-equivalent emissions using the Global Warming Potential (GWP*) approach, with best-estimates of the coefficients taken from the IPCC AR6 (Forster et al., 2021).</p> <p>Warming in response to cumulative CO2-equivalent emissions is estimated using the transient climate response to cumulative carbon emissions (TCRE) approach, with best-estimate value of TCRE taken from the IPCC AR6 (Forster et al., 2021, Canadell et al., 2021). 'Warming' is specifically the change in global mean surface temperature (GMST).</p> <p>The data files provide emissions, cumulative emissions and the GMST response by country, gas (CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or 3-GHG total) and source (fossil emissions, land use emissions or the total).</p> <p><strong>Data records: overview</strong></p> <p>The data records include three comma separated values (.csv) files as described below.</p> <p>All files are in ‘long’ format with one value provided in the <em>Data</em> column for each combination of the categorical variables <em>Year, Country Name, Country ISO3 code, Gas, and Component</em> columns.</p> <p><em>Component</em> specifies fossil emissions, LULUCF emissions or total emissions of the gas.</p> <p><em>Gas</em> specifies CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O or the three-gas total (labelled 3-GHG).</p> <p><em>Country ISO3 codes</em> are specifically the unique ISO 3166-1 alpha-3 codes of each country.</p> <p><strong>Data records: specifics</strong></p> <p>Data are provided relative to 2 reference years (denoted <em>ref_year </em>below): 1850 and 1991. 1850 is a mutual first year of data spanning all input datasets. 1991 is relevant because the United Nations Framework Convention on Climate Change was operationalised in 1992.</p> <p><em>EMISSIONS_ANNUAL_{ref_year-20}-2023.csv:</em> <em>Data </em>includes annual emissions of CO<sub>2</sub> (Pg CO<sub>2</sub> year<sup>-1</sup>), CH<sub>4</sub> (Tg CH<sub>4</sub> year<sup>-1</sup>) and N<sub>2</sub>O (Tg N<sub>2</sub>O year<sup>-1</sup>) during the period <em>ref_year-20 </em>to 2023. The <em>Data</em> column provides values for every combination of the categorical variables. Data are provided from <em>ref_year-20</em> because these data are required to calculate GWP* for CH<sub>4</sub>.</p> <p><em>EMISSIONS_CUMULATIVE_CO2e100_{ref_year+1}-2023.csv: Data </em>includes the cumulative CO<sub>2</sub> equivalent emissions in units Pg CO<sub>2</sub>-e<sub>100</sub> during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><em>GMST_response_{ref_year+1}-2023.csv:</em> <em>Data</em> includes the change in global mean surface temperature (GMST) due to emissions of the three gases in units °C during the period <em>ref_year+1</em> to 2023 (i.e. since the reference year). The <em>Data</em> column provides values for every combination of the categorical variables. </p> <p><strong>Accompanying Code</strong></p> <p>Code is available at: <a href="https://github.com/jonesmattw/National_Warming_Contributions">https://github.com/jonesmattw/National_Warming_Contributions</a> .</p> <p>The code requires Input.zip to run (see README at the GitHub link).</p> <p><strong>Further info: Country Groupings</strong></p> <p>We also provide estimates of the contributions of various country groupings as defined by the UNFCCC:</p> <ul> <li>Annex I countries (number of countries, n = 42)</li> <li>Annex II countries (n = 23)</li> <li>economies in transition (EITs; n = 15)</li> <li>the least developed countries (LDCs; n = 47)</li> <li>the like-minded developing countries (LMDC; n = 24).</li> </ul> <p>And other country groupings:</p> <ul> <li>the organisation for economic co-operation and development (OECD; n = 38)</li> <li>the European Union (EU27 post-Brexit)</li> <li>the Brazil, South Africa, India and China (BASIC) group.</li> </ul> <p>See COUNTRY_GROUPINGS.xlsx for the lists of countries in each group.</p>
Simulations of Historical Impacts of Climate Change and Atmospheric Chemistry at Harvard Forest 1850-2019
This study is a model application aimed at simulating historical carbon (C), nitrogen (N), and water dynamics at a hardwood forest stand at Harvard Forest from 1850 to 2019. We applied the PnET-CN-daily model with a reconstructed historical climate and air quality scenario derived from field observations and regional model simulations. The model outputs were calibrated with field measurements conducted at Harvard Forest. We used field measurements of aboveground biomass (AGB) and foliar mass near the EMS tower to calibrate ecosystem C pools. Gross primary production (GPP), net ecosystem exchange (NEE), and respiration from the EMS eddy flux tower were used to calibrate C fluxes. Net N mineralization data from the chronic N amendment experiment, along with other N dynamics data collected at Harvard Forest, were used to calibrate N pools and fluxes. Additionally, evapotranspiration (ET) and soil water content from the EMS tower were used to calibrate water fluxes. To isolate the effects of individual environmental factors on C, N, and water dynamics, we ran the PnET-CN-daily model with a series of theoretical scenarios. These scenarios were developed based on the reconstructed historical climate and air quality data while keeping non-target input factors at pre-industrial levels. The considered environmental factors include climate, carbon dioxide (CO2) concentration, atmospheric N deposition, and ozone (O3) concentration. This approach allowed us to decompose the influence of each factor on ecosystem dynamics by comparing model outputs across different scenarios.
Cross-site comparison of historical trends in marsh change at three LTER sites: GCE, VCR, and PIE.
The data provided here are shapefiles which were generated from digitizing historical maps and aerial photographs for a cross site comparison of changes in marshes at the Plum Island Ecosystem (PIE), Virginia Coast Reserve (VCR), and Georgia Coastal Ecosystems (GCE) Long Term Ecological Research (LTER) sites. This dataset includes shoreline shapefiles digitized for 3 time intervals at each site: 1930s/40s, 1950s/70s, and 2013 as well as a table of results from the shoreline change analysis. Centerlines for all the channels were generated from the shoreline shapefiles and are attributed with Strahler (1965) channel order and sinuosity. Additionally, this dataset includes shapefiles of important features (channels, upland, ponds, tidal flats) digitized for each of three sites at three time intervals.
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>
A list of Swedish words that have experienced historical semantic changes
<p>This list contains a set of Swedish words that have experienced semantic change during the past centuries. The list has been collected during the VR funded project <a href="https://languagechange.org/">Towards Computational Lexical Semantic Change Detection</a>, ( 2018-01184) and is a work in progress. Because the work is currently on pause, we have chosen to release the list as is in the hope of facilitating collaboration and use in other research.</p> <p>The list has four columns in the following format:</p> <pre><code>Ord*, Vilken betydelseförändring har skett*, När skedde förändringen, Källor (exv SAOL) word, what change occurred, when the change occur, reference</code></pre> <p><br> Not all fields are filled for every word. Where there are multiple change periods, there are multiple lines with empty values for word and what change occurred, see the example with <em>egendomlig </em>below.</p> <pre><code>egendomlig,som utgör (ngns) egendom > karaktäristisk (positiv) > speciell (negativt!!),"A. Sen 1600tal, ",SAOB ,,B. Sen 1850, ,,"C. Sen ??, efter 190",</code></pre> <p>The .xlsx file contains links to the references.</p> <p>The resources are freely available for education, research and other non-commercial purposes.</p> <p> </p>
Supplementary data for: "Historical glacier change on Svalbard predicts doubling of mass loss by 2100"
<p>Supplementary datasets for:</p> <p>Geyman, E.C., van Pelt, W.J.J., Maloof, A.C., Faste Aas, H., and Kohler, J., 2022. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.</p> <p>Abstract:</p> <p>The melting of glaciers and ice caps accounts for about one-third of current sea-level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal climate shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Here we link historical and modern glacier observations to predict that twenty-first century glacier thinning rates will more than double those from 1936 to 2010. Making use of an archive of historical aerial imagery from 1936 and 1938, we use structure-from-motion photogrammetry to reconstruct the three-dimensional geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a more than 70-year timespan, enabling us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr<sup>-1</sup> of water equivalent. Finally, we design a space-for-time substitution8 to combine our historical glacier observations with climate projections and make first-order predictions of twenty-first century glacier change across Svalbard.</p> <p> </p> <p>Dataset description: </p> <p><br> This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder).</p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]: </strong></p> <p><br> 1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file] </p> <p>3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file] </p> <p>4. Shapefile of 1936 glacier extents [ESRI .shp file] </p> <p>5. Glacier-by-glacier statistics [.xlsx file] </p> <p>------------------------------------------------------------------------------------- </p> <p><strong>Regional-datasets: </strong></p> <p><em>Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d: </em></p> <p><em>Zone 1 - South Spitsbergen</em></p> <p><em>Zone 2 - Barentsoya-Edgeoya</em></p> <p><em>Zone 3 - Austfonna</em></p> <p><em>Zone 4 - Vestfonna</em></p> <p><em>Zone 5 - Northeast Spitsbergen</em></p> <p><em>Zone 6 - Central Spitsbergen</em></p> <p><em>Zone 7 - Northwest Spitsbergen</em></p> <p><em>Zone 8 - North Spitsbergen</em></p> <p><br> 6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files] </p> <p>7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files] </p> <p>8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files] </p> <p>9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3.</p> <p>10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.</p>
Historical and future climate change fosters expansion of Australian harvester termites, Drepanotermes
<p>Past evolutionary adaptations to Australia's aridification can help us to understand potential responses of species in the face of global climate change. Here, we focus on the Australian-endemic termite genus <em>Drepanotermes</em>, which is widespread in semi-arid and arid regions of Australia. We used species delineation, phylogenetic inference, and ancestral state reconstruction to investigate the evolution of mound-building and in relation to reconstructed past climatic conditions. Our results suggest that mound-building evolved several times independently, apparently facilitating expansion into tropical and mesic regions of Australia. Strong phylogenetic signal of bioclimatic variables, especially of limiting environmental factors (e.g. precipitation of warmest quarter), indicates that climate exerts a strong selective pressure. Finally, we used environmental niche modeling to predict present and future habitat suitability for eight <em>Drepanotermes</em> species. Abiotic factors such as annual temperature contributed disproportionately to calibrations, while the inclusion of biotic factors like vegetation cover improved ecological niche models in some species. A comparison between present and future habitat suitability under two different emission scenarios revealed continued suitability of current ranges as well as substantial habitat gains for most studied species, irrespective of nesting habit, yet extensive range expansions in the near future are likely precluded by low dispersal abilities.</p>
CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> <p>Decoding file names:</p> <p>Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend. </p> <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> <p>Variables:</p> <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul> <p> </p> <p> </p> <p> </p>
Fig. 5 in Historical biogeography highlights the role of Miocene landscape changes on the diversification of a clade of Amazonian tree frogs
Fig. 5 Dorsolateral views of Osteocephalus. a O. leprieurii sensu stricto, Kaw mountain, French Guiana. b O. leprieurii, AF1824, Mana, French Guiana. c O. yasuni sensu stricto, QCAZ69010, Estación Cientí- fica Yasuní, Orellana, Ecuador. d O. yasuni, QCAZ55994, Lorocachi, Pastaza, Ecuador. e O. deridens, QCAZ56031, Lorocachi, Pastaza, Ecuador. f O. fuscifacies, QCAZ59887, Parque Nacional Llanganates, Pastaza, Ecuador. g O. aff. leoniae 2, AF4513 (sequenced), San Martín, Peru. h O. planiceps, QCAZ55881, Lorocachi, Pastaza, Ecuador. Photographs: a–b, g by Antoine Fouquet; c–f, h by Santiago Ron (BIOWEB, Licence: CC BY-NC-ND 4.0)
Fig. 4 in Historical biogeography highlights the role of Miocene landscape changes on the diversification of a clade of Amazonian tree frogs
Fig. 4 Dorsolateral and ventral views of Osteocephalus. a O. cabrerai, CORBIDI120, Tara- poa, Sucumbíos, Ecuador. b O. helenae, AF2427 (sequenced), Nassau, Suriname. c O. helenae, Amapá, Brazil. d O. helenae, Voltaire, French Guiana. e–f O. aff. helenae 2 'morph cabrerai', AF2357 (sequenced), Mapaou, French Guiana. g O. aff. helenae 2 'morph cabrerai', AF3358 (sequenced), Bakhuis, Surinam. h O. aff. helenae 2 'morph cabrerai', St. Georges, French Guiana. Photographs: a by Pablo Venegas (BIOWEB, Licence: CC BY-NC-ND 4.0); b–h by Antoine Fouquet
Fig. 3 in Historical biogeography highlights the role of Miocene landscape changes on the diversification of a clade of Amazonian tree frogs
Fig. 3 Dorsolateral views of Osteocephalus. a O. alboguttatus sensu stricto, QCAZ15972, Puyo-Canelos Road, Pastaza, Ecuador. b O. mimeticus sensu stricto, AF4442 (sequenced), San José, San Martín, Peru. c O. buckleyi sensu stricto, Canelos, Pastaza, Ecuador. d O. vilmae, QCAZ51205, Pompeya-Iro Road km 80, Orellana, Ecuador. e O. mutabor, QCAZ56066, Lorocachi, Pastaza, Ecuador. f O. mutabor, QCAZ39588, Río Pucayacu, Pastaza, Ecuador. g O. taurinus sensu stricto, Reserva Ducke, Amazonas, Brazil. h O. oophagus sensu stricto, Reserva Ducke, Amazonas, Brazil. Photographs: a, d–f by Santiago Ron (BIOWEB, Licence: CC BY-NC-ND 4.0); b by Antoine Fouquet; c, g–h by Diego Ortiz
Fig. 3 in The Mediterranean deep-sea fauna: historical evolution, bathymetric variations and geographical changes
Fig. 3. Geographical distribution of Lingula dregeri in the Miocene: 1, Korytnica Basin (Poland): Korytnica, Chomentów (Friedberg 1930; Barczyk and Popiel−Barczyk 1977; Gutowski 1984; Radwańska and Radwański 1984; and present data); 2, Wójcza−Pińczów Range (Poland) (Popiel−Barczyk and Barczyk 1990; and present data); 3, Lublin Upland (Poland): Huta Lubycka, Monastyrz, Długi Goraj (Popiel−Barczyk 1977, 1980; Jakubowski and Musiał 1977; present data); 4, Podolia (Ukraine): Obertasów near Zolochiv (Złoczów) (Friedberg 1921); 5–7, Vienna Basin (Austria): 5, Austränk (Dreger 1889); 6, Loretto (Dreger 1889); 7, St. Margarethen (Meznerics 1943; Schmid et al. 2001). A Lingula has been recorded in Lapugiu (Romania) by Bărbulescu and Rado (1984) in the Badenian (Miocene). The following localities are not indicated on the map: Cagliari, and vicinities: Sant Elias, Mirrionis island, San Michele hill, in Sardinia (Italy) (Dreger 1911).
Fig. 2 in The Mediterranean deep-sea fauna: historical evolution, bathymetric variations and geographical changes
Fig. 2. External (A1, B1) and internal (A2, B2) views of two dorsal valves from the Korytnica Basin, with their muscles scars (A3, B3) as visible on the internal side of the valve (see also Fig. 1). A. MZ VIII Bra−1204/1. B. MZ VIII Bra−1204/2. All × 7.
Fig. 1 in The Mediterranean deep-sea fauna: historical evolution, bathymetric variations and geographical changes
Fig. 1. Variability in musculature, established from 5 dorsal valves and in the valve outline of Lingula dregeri. The range of the lophophoral cavity is given in percentage. No data on ventral valves. The scars in grey represent the remnant of the motion of the anterior oblique muscles induced by the growth of the shell. Muscles: aA, anterior Adductor; pA, posterior Adductor; 1, Anterior Obliques; 2, Anterior Lateral Oblique + Median Lateral Oblique + Anterior Internal Oblique + Posterior Internal Oblique; 3. Anterior Lateral Oblique + Median Lateral Oblique + Median Internal Oblique (Emig 1982).
SECURES-Met - A European wide meteorological data set suitable for electricity modelling (supply and demand) for historical climate and climate change projections
<p>For the modelling of electricity production and demand, meteorological conditions are becoming more relevant due to the increasing contribution from renewable electricity production. But the requirements on meteorological data sets for electricity modelling are quite high. One challenge is the high temporal resolution, since a typical time step for modelling electricity production and demand is one hour. On the other side the European electricity market is highly connected, so that a pure country based modelling does not make sense and at least the whole European Union area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21st century. Thus, we have developed an aggregated European wide data set that has a temporal resolution of one hour, covers the whole EU area, has a reasonable size but is considering the high spatial variability. This meteorological data set for Europe for the historical period and climate change projections fulfills all relevant criteria for energy modelling. It has a hourly temporal resolution, considers local effects up to a spatial resolution of 1 km and has a suitable size, as all variables are aggregated to NUTS regions. Additionally meteorological information from wind speed and river run-off is directly converted into power productions, using state of the art methods and the current information on the location of power plants. Within the research project SECURES (https://www.secures.at/) this data set has been widely used for energy modelling.</p> <p> </p> <p>The SECURES-Met dataset provides variables visible in the table.</p> <table> <tbody><tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Aggregation methods</th> <th>Temporal resolution</th> </tr> </tbody><tbody> <tr> <th>Temperature (2m)</th> <td>T2M</td> <td> <p>°C</p> <p>°C</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th>Radiation</th> <td> <p>GLO (mean global radiation)</p> <p>BNI (direct normal irradiation)</p> </td> <td> <p>Wm-2</p> <p>Wm-2</p> </td> <td> <p>spatial mean</p> <p>population weighted mean (recommended)</p> </td> <td>hourly</td> </tr> <tr> <th><strong>Potential Wind Power </strong></th> <td>WP</td> <td>1</td> <td>normalized with potentially available area</td> <td>hourly</td> </tr> <tr> <th><strong>Hydro Power Potential</strong></th> <td> <p>HYD-RES (reservoir)</p> <p>HYD-ROR (run-of-river)</p> </td> <td> <p>MW</p> <p>1</p> </td> <td> <p>summed power production</p> <p>summed power production normalized with average daily production</p> </td> <td>daily</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p>SECURES-Met is available in a tabular csv format for the historical period (1981-2020, Hydro only until 2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 1951-2100, wind power starting from 1981, hydro power from 1971) created from one CMIP5 EUROCORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E, ensemble run: r12i1p1) on the <strong>spatial aggregation level</strong></p> <ul> <li>NUTS0 (country-wide),</li> <li>NUTS2 (province-wide),</li> <li>NUTS3 (Austria only),</li> <li>and EEZ (Exclusive Economic Zones, offshore only).</li> </ul> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shape files of the different NUTS levels. As <strong>population weighted</strong> temperature and radiation represent values in geographical areas more relevant for solar power, it is highly relevant to use population weighted files. Spatial mean should be used for reference only.</p> <p>The project SECURES, in which this dataset was produced, was funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p>
Data and code from: Decades of historical outbreak cycles in a multivoltine insect reveal a plastic phenological response to climate change
Open the record for dataset details and reuse information.
Historical and future climate change fosters expansion of Australian harvester termites, Drepanotermes
Open the record for dataset details and reuse information.
A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change
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Data from: Historical demography and climate driven distributional changes in a widespread Neotropical freshwater species with high economic importance
The Neotropical region exhibits the greatest worldwide diversity and the diversification history of several clades is related to the puzzling geomorphologic and climatic history of this region. The freshwater Amazon ecoregion contains the main hydrographic basins of the Neotropical region that are highly dendritic and ecologically diverse. It contains a rich and endemic fish fauna, including one of its most iconic and economically important representatives, the bony-tongue Arapaima gigas (Teleostei, Osteoglossiformes). Here, we evaluated the projected distribution of the genus in different historical periods (Present, Last Glacial Maximum, Last Interglacial Maximum and Near Future) and interpreted these results in light of the genomic diversity and modeled historical demography. For that, we combined species distribution models, population genetic analysis using SNPs and deep learning model selection. We analyzed a representative sample of the genus from the two basins where it naturally occurs, four localities in the Amazon (Am) and three in the Tocantins-Araguaia (To-Ar) basin, as well as individuals from three fish farms. We inferred a potentially smaller distribution in the glacial period, with a possible refuge in central Am. Our genetic data agrees with this result, suggesting a higher level of genetic diversity in the Am basin, compared to that observed in To-Ar. Our deep learning model comparison indicated that the To-Ar basin was colonized by the population from the Am basin. Considering a global warming scenario in the near future, A. gigas could reach an even larger range, especially if anthropogenic related dispersal occurs, potentially invading new areas and impacting their communities.
Data from: Isolation by instability: historical climate change shapes population structure and genomic divergence of treefrogs in the Neotropical Cerrado savanna
Although the impact of Pleistocene glacial cycles on the diversification of the tropical biota was once dismissed, increasing evidence suggests that Pleistocene climatic fluctuations greatly affected the distribution and population divergence of tropical organisms. Landscape genomic analyses coupled with paleoclimatic distribution models provide a powerful way to understand the consequences of past climate changes on the present-day tropical biota. Using genome-wide SNP data and mitochondrial DNA, combined with projections of the species distribution across the late Quaternary until the present, we evaluate the effect of paleoclimatic shifts on the genetic structure and population differentiation of Hypsiboas lundii, a treefrog endemic to the South American Cerrado savanna. Our results show a recent and strong genetic divergence in H. lundii across the Cerrado landscape, yielding four genetic clusters that do not seem congruent with any current physical barrier to gene flow. Isolation by distance (IBD) explains some of the population differentiation, but we also find strong support for past climate changes promoting range shifts and structuring populations even in the presence of IBD. Post Pleistocene population persistence in four main areas of historical stable climate in the Cerrado seems to have played a major role establishing the present genetic structure of this treefrog. This pattern is consistent with a model of reduced gene-flow in areas with high climatic instability promoting isolation of populations, defined here as "isolation by instability", highlighting the effects of Pleistocene climatic fluctuations structuring populations in tropical savannas.
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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.