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545 results for “Latin”
ERA5-Land selected indicators daily aggregates for the Latin America region, 2021
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2021.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2020
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2020.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2018
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2018.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2019
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2019.</p><p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p><p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p><p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Late Latin Charter Treebank 2 (LLCT2), version 1.2
<p>Version 1.2 of the Late Latin Charter Treebank 2 (LLCT2). Contains a number of minor corrections and replaces the version 1.0 published at Zenodo in 2019. Early Medieval Latin documentary texts from Italy between AD 774-897 with morphological and syntactic annotation. Latin Dependency Treebank (LDT) compatible linguistic annotation, CoNLL treebank format. Note that LLCT2 is also available open-access in the Universal Dependencies format at the <a href="https://github.com/UniversalDependencies/UD_Latin-LLCT">website</a> of the Universal Dependencies consortium. For a detailed description of the Late Latin Charter Treebanks, see the pre-print of the paper 'Late Latin Charter Treebank: contents and annotation', to be published in Corpora, 16:2 (2021), at the <a href="https://researchportal.helsinki.fi/fi/publications/late-latin-charter-treebank-contents-and-annotation">institutional repository of the University of Helsinki</a>. See also Korkiakangas, T. and Lassila, M. (2013), <a href="https://www.academia.edu/5491302/Korkiakangas_Timo_and_Lassila_Matti_Abbreviations_fragmentary_words_formulaic_language_treebanking_mediaeval_charter_material_"><em>Abbreviations, fragmentary words, formulaic language: treebanking medieval charter material</em></a>, in Mambrini, F., Passarotti, M. and Sporleder, C., <em>Proceedings of the third workshop on annotation of corpora for research in the humanities</em>, pp. 61–72, and Korkiakangas, T. and Passarotti, M. (2011), <a href="https://pdfs.semanticscholar.org/6825/a8ad70fe6e2a77540d9ff2774b8f34804fd0.pdf?_ga=2.234021022.1247020789.1580545874-419947753.1580545874"><em>Challenges in Annotating Medieval Latin Charters</em></a>, in «Journal of Language Technology and Computational Linguistics», 26, pp. 103–114.</p>
Learner Data from a Study on Latin Language Learning
<p>The dataset contains test results from a digital intervention study of the <a href="https://www.projekte.hu-berlin.de/en/callidus-en">CALLIDUS Project</a> in a high school in Berlin. 13 Students were randomly sampled in two groups and completed various linguistic tasks. The focus of the study was to find out whether learning Latin vocabulary in authentic contexts leads to higher lexical competence, compared to memorizing traditional vocabulary lists.</p> <p>The data is available in <a href="https://www.json.org">JSON format</a> as provided by the <a href="https://h5p.org">H5P implementation</a> of <a href="https://www.valamis.com/hub/xapi">XAPI</a>. File names indicate the time of test completion, in the concatenated form of "year-month-day-hour-minute-second-millisecond". This allows us to trace the development of single learners who were fast enough to perform the test twice in a row.</p> <p>Changelog:</p> <p>Version 2.0: Each exercise now has a unique ID that is consistent in the whole dataset, so evaluation/visualization can refer to specific exercises more easily.</p> <p>Version 3.0: A simplified Excel Spreadsheet has been added to enhance the reusability of the dataset. It contains a slightly reduced overview of the data, but the core information (user ID, task statement, correct solution, given answer, score, duration) is still present.</p>
The Marginal Latin (LaM) readings of 2 Samuel - The collation file
<p>The collected cases of Marginal Latin (LaM) readings in 2 Samuel. Made at the time of sending the draft of the article to a publisher for an evaluation.</p>
Paphos, Cyprus. Tomb niche, Latin church in the Chrysopolitissa complex.
<p>Paphos, Cyprus. Tomb niche, north side of the Latin church in the Chrysopolitissa complex, as documented in 1974.</p>
Paphos, Cyprus. General view of the Latin church in the Chrysopolitissa complex.
<p>Paphos, Cyprus. General view of the Latin church in the Chrysopolitissa complex, seen from the south east, as documented in 1974.</p>
Paphos, Cyprus. Ruin of Latin cathedral.
<p>Ruin generally described as the Latin Cathedral, as documented in 1974; located approximately at 34°45'34"N 32°24'42"E.</p>
Hate Speech and Bias against Asians, Blacks, Jews, Latines, and Muslims: A Dataset for Machine Learning and Text Analytics
<h1>Institute for the Study of Contemporary Antisemitism (ISCA) at Indiana University Dataset on bias against Asians, Blacks, Jews, Latines, and Muslims </h1> <div> <h2> </h2> <h2>Description </h2> </div> <div> <p>The dataset is a product of a research project at Indiana University on biased messages on Twitter against ethnic and religious minorities. We scraped all live messages with the keywords "Asians, Blacks, Jews, Latinos, and Muslims" from the Twitter archive in 2020, 2021, and 2022.</p> <p>Random samples of 600 tweets were created for each keyword and year, including retweets. The samples were annotated in subsamples of 100 tweets by undergraduate students in Professor Gunther Jikeli's class 'Researching White Supremacism and Antisemitism on Social Media' in the fall of 2022 and 2023. A total of 120 students participated in 2022. They annotated datasets from 2020 and 2021. 134 students participated in 2023. They annotated datasets from the years 2021 and 2022. The annotation was done using the <a href="https://annotationportal.com/" target="_blank" rel="noreferrer noopener">Annotation Portal</a> (Jikeli, Soemer and Karali, 2024). The updated version of our portal, <a href="https://portal2.annotationportal.com/" target="_blank" rel="noreferrer noopener">AnnotHate</a>, is now publicly available. Each subsample was annotated by an average of 5.65 students per sample in 2022 and 8.32 students per sample in 2023, with a range of three to ten and three to thirteen students, respectively. Annotation included questions about bias and calling out bias. </p> </div> <div> <p>Annotators used a scale from 1 to 5 on the bias scale (confident not biased, probably not biased, don't know, probably biased, confident biased), using definitions of bias against each ethnic or religious group that can be found in the research reports from <a href="https://isca.indiana.edu/publication-research/social-media-project/Research-Report-BIAS-on-Twitter-against-Asians--Blacks-Jews-Latinos-Muslims-final-002.pdf" target="_blank" rel="noreferrer noopener">2022</a> and <a href="https://isca.indiana.edu/documents/BIAS%20Against%20Asian-Black-Hispanic-Jewish-and-%20Muslim-People%20on%20X-Twitter%20in%202021%20and%202022.pdf" target="_blank" rel="noreferrer noopener">2023</a>. If the annotators interpreted a message as biased according to the definition, they were instructed to choose the specific stereotype from the definition that was most applicable. Tweets that denounced bias against a minority were labeled as "calling out bias". </p> </div> <div> <p>The label was determined by a 75% majority vote. We classified “probably biased” and “confident biased” as biased, and “confident not biased,” “probably not biased,” and “don't know” as not biased. </p> </div> <div> <p>The stereotypes about the different minorities varied. About a third of all biased tweets were classified as general 'hate' towards the minority. The nature of specific stereotypes varied by group. Asians were blamed for the Covid-19 pandemic, alongside positive but harmful stereotypes about their perceived excessive privilege. Black people were associated with criminal activity and were subjected to views that portrayed them as inferior. Jews were depicted as wielding undue power and were collectively held accountable for the actions of the Israeli government. In addition, some tweets denied the Holocaust. Hispanic people/Latines faced accusations of being undocumented immigrants and "invaders," along with persistent stereotypes of them as lazy, unintelligent, or having too many children. Muslims were often collectively blamed for acts of terrorism and violence, particularly in discussions about Muslims in India. </p> </div> <div> <p>The annotation results from both cohorts (Class of 2022 and Class of 2023) will not be merged. They can be identified by the "cohort" column. While both cohorts (Class of 2022 and Class of 2023) annotated the same data from 2021,* their annotation results differ. The class of 2022 identified more tweets as biased for the keywords "Asians, Latinos, and Muslims" than the class of 2023, but nearly all of the tweets identified by the class of 2023 were also identified as biased by the class of 2022. The percentage of biased tweets with the keyword 'Blacks' remained nearly the same. </p> </div> <div> <p>*Due to a sampling error for the keyword "Jews" in 2021, the data are not identical between the two cohorts. The 2022 cohort annotated two samples for the keyword Jews, one from 2020 and the other from 2021, while the 2023 cohort annotated samples from 2021 and 2022.The 2021 sample for the keyword "Jews" that the 2022 cohort annotated was not representative. It has only 453 tweets from 2021 and 147 from the first eight months of 2022, and it includes some tweets from the query with the keyword "Israel". The 2021 sample for the keyword "Jews" that the 2023 cohort annotated was drawn proportionally for each trimester of 2021 for the keyword "Jews". </p> </div> <div> <h2> </h2> <h2>Content</h2> <h3>Cohort 2022 </h3> </div> <div> <p>This dataset contains 5880 tweets that cover a wide range of topics common in conversations about Asians, Blacks, Jews, Latines, and Muslims. 357 tweets (6.1 %) are labeled as biased and 5523 (93.9 %) are labeled as not biased. 1365 tweets (23.2 %) are labeled as calling out or denouncing bias. </p> </div> <div> <p>1180 out of 5880 tweets (20.1 %) contain the keyword "Asians," 590 were posted in 2020 and 590 in 2021. 39 tweets (3.3 %) are biased against Asian people. 370 tweets (31,4 %) call out bias against Asians. </p> </div> <div> <p>1160 out of 5880 tweets (19.7%) contain the keyword "Blacks," 578 were posted in 2020 and 582 in 2021. 101 tweets (8.7 %) are biased against Black people. 334 tweets (28.8 %) call out bias against Blacks. </p> </div> <div> <p>1189 out of 5880 tweets (20.2 %) contain the keyword "Jews," 592 were posted in 2020, 451 in 2021, and ––as mentioned above––146 tweets from 2022. 83 tweets (7 %) are biased against Jewish people. 220 tweets (18.5 %) call out bias against Jews. </p> </div> <div> <p>1169 out of 5880 tweets (19.9 %) contain the keyword "Latinos," 584 were posted in 2020 and 585 in 2021. 29 tweets (2.5 %) are biased against Latines. 181 tweets (15.5 %) call out bias against Latines. </p> </div> <div> <p>1182 out of 5880 tweets (20.1 %) contain the keyword "Muslims," 593 were posted in 2020 and 589 in 2021. 105 tweets (8.9 %) are biased against Muslims. 260 tweets (22 %) call out bias against Muslims. </p> </div> <div> <h3>Cohort 2023 </h3> </div> <div> <p>The dataset contains 5363 tweets with the keywords “Asians, Blacks, Jews, Latinos and Muslims” from 2021 and 2022. 261 tweets (4.9 %) are labeled as biased, and 5102 tweets (95.1 %) were labeled as not biased. 975 tweets (18.1 %) were labeled as calling out or denouncing bias. </p> </div> <div> <p>1068 out of 5363 tweets (19.9 %) contain the keyword "Asians," 559 were posted in 2021 and 509 in 2022. 42 tweets (3.9 %) are biased against Asian people. 280 tweets (26.2 %) call out bias against Asians. </p> </div> <div> <p>1130 out of 5363 tweets (21.1 %) contain the keyword "Blacks," 586 were posted in 2021 and 544 in 2022. 76 tweets (6.7 %) are biased against Black people. 146 tweets (12.9 %) call out bias against Blacks. </p> </div> <div> <p>971 out of 5363 tweets (18.1 %) contain the keyword "Jews," 460 were posted in 2021 and 511 in 2022. 49 tweets (5 %) are biased against Jewish people. 201 tweets (20.7 %) call out bias against Jews. </p> </div> <div> <p>1072 out of 5363 tweets (19.9 %) contain the keyword "Latinos," 583 were posted in 2021 and 489 in 2022. 32 tweets (2.9 %) are biased against Latines. 108 tweets (10.1 %) call out bias against Latines. </p> </div> <div> <p>1122 out of 5363 tweets (20.9 %) contain the keyword "Muslims," 576 were posted in 2021 and 546 in 2022. 62 tweets (5.5 %) are biased against Muslims. 240 tweets (21.3 %) call out bias against Muslims. </p> </div> <div> <h2> </h2> <h2>File Description</h2> </div> <div> <p>The dataset is provided in a csv file format, with each row representing a single message, including replies, quotes, and retweets. The file contains the following columns: </p> <p>'TweetID': Represents the tweet ID. </p> </div> <div> <p>'Username': Represents the username who published the tweet (if it is a retweet, it will be the user who retweetet the original tweet. </p> </div> <div> <p>'Text': Represents the full text of the tweet (not pre-processed). </p> </div> <div> <p>'CreateDate': Represents the date the tweet was created. </p> </div> <div> <p>'Biased': Represents the labeled by our annotators if the tweet is biased (1) or not (0). </p> </div> <div> <p>'Calling_Out': Represents the label by our annotators if the tweet is calling out bias against minority groups (1) or not (0). </p> </div> <div> <p>'Keyword': Represents the keyword that was used in the query. The keyword can be in the text, including mentioned names, or the username. </p> </div> <div> <p> ‘Cohort’: Represents the year the data was annotated (class of 2022 or class of 2023) </p> </div> <div> <h2> </h2> <h2>Acknowledgements </h2> </div> <div> <p>We are grateful for the technical collaboration with Indiana University's Observatory on Social Media (OSoMe). We thank all class participants for the annotations and contributions, including Kate Baba, Eleni Ballis, Garrett Banuelos, Savannah Benjamin, Luke Bianco, Zoe Bogan, Elisha S. Breton, Aidan Calderaro, Anaye Caldron, Olivia Cozzi, Daj Crisler, Jenna Eidson, Ella Fanning, Victoria Ford, Jess Gruettner, Ronan Hancock, Isabel Hawes, Brennan Hensler, Kyra Horton, Maxwell Idczak, Sanjana Iyer, Jacob Joffe, Katie Johnson, Allison Jones, Kassidy Keltner, Sophia Knoll, Jillian Kolesky, Emily Lowrey, Rachael Morara, Benjamin Nadolne, Rachel Neglia, Seungmin Oh, Kirsten Pecsenye, Sophia Perkovich, Joey Philpott, Katelin Ray, Kaleb Samuels, Chloe Sherman, Rachel Weber, Molly Winkeljohn, Ally Wolfgang, Rowan Wolke, Michael Wong, Jane Woods, Kaleb Woodworth, Aurora Young, Sydney Allen, Hundre Askie, Norah Bardol, Olivia Baren, Samuel Barth, Emma Bender, Noam Biron, Kendyl Bond, Graham Brumley, Kennedi Bruns, Leah Burger, Hannah Busche, Morgan Butrum-Griffith, Zoe Catlin, Angeli Cauley, Nathalya Chavez Medrano, Mia Cooper, Suhani Desai, Isabella Flick, Samantha Garcez, Isabella Grady, Macy Hutchinson, Sarah Kirkman, Ella Leitner, Elle Marquardt, Madison Moss, Ethan Nixdorf, Reya Patel, Mickey Racenstein, Kennedy Rehklau, Grace Roggeman, Jack Rossell, Madeline Rubin, Fernando Sanchez, Hayden Sawyer, Diego Scheker, Lily Schwecke, Brooke Scott, Megan Scott, Samantha Secchi, Jolie Segal, Katherine Smith, Constantine Stefanidis, Cami Stetler, Madisyn West, Alivia Yusefzadeh, Tayssir Aminou, Karen Fecht, Luciana Orrego-Hoyos, Hannah Pickett, and Sophia Tracy. </p> </div> <div> <p>This work used Jetstream2 at Indiana University through allocation HUM200003 from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296. </p> </div> <div> <p> </p> </div>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2023
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2023.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Atlas of the Latin Church in the Polish-Lithuanian Commonwealth in the Eighteenth Century
<h2>Atlas of the Latin Church in the Polish-Lithuanian Commonwealth in the Eighteenth Century (v1)</h2> <p><strong><a href="https://geo-ecclesiae.kul.pl/apps/latin-church-1772" rel="nofollow">The application "Atlas of the Latin Church in the Polish-Lithuanian Commonwealth in the Eighteenth Century"</a></strong> was prepared by the Institute for the Historical Geography of the Church in Poland on the basis of data published in the work of S. Litak with the same title (TNKUL, Lublin 2006). The original database prepared by B. Szady was created in 1993-1996 prior to the publication of the previous edition of Litak's study ("The Latin Church in the Polish-Lithuanian Commonwealth around 1772: Administrative Structures", Institute of East-Central Europe, Lublin 1996, Religious and Ethnic Communities in the Polish-Lithuanian Commonwealth in the Second Half of the 18th Century, 1). Work carried out over the next decade resulted in the development of the source base, additions and corrections. At the same time, a map was prepared and editorial changes were made regarding the system of source and bibliographic abbreviations.</p> <p>The information presented in the application and in the downloadable files has been grouped into 4 sections: 1) churches, 2) monasteries and convents, 3) ecclesiastical boundaries, 4) secular boundaries. The whole resource has been provided with appropriate metadata and list of abbreviations.</p>
The macroeconomic determinants of trade openness in Latin American countries: A panel data analysis
<p><strong><span>Background:</span></strong><span> Trade openness shows a positive impact on economic growth, supported by economic theory, and export diversification and economic complexity show a positive dynamic in trade openness in the world; however, a specificity is generated in South American countries. Therefore, the objective of the research is to analyse the macroeconomic determinants of trade openness in Latin American countries.</span></p> <p><strong><span>Methods: </span></strong><span>The research approach was quantitative and explanatory using panel data methodology from the databases of the World Bank, Harvard University and the Economic Commission for Latin America and the Caribbean for the period 2000-2020.</span></p> <p><strong><span>Results: </span></strong><span>The fixed effects panel data model showed that the variables that had a negative impact on trade openness were GDP, the economic complexity index and the logistic performance index, while the variables that had a positive impact were exports of high-tech products (a proxy for innovation), exports, imports, research and development expenditure and interregional trade in goods.</span></p> <p><strong><span>Conclusions: </span></strong><span>Therefore, during the analysis period of 2000-2020 in South America, based on the panel data analysis under fixed effects, a total of 8 countries had a negative impact on trade openness, and only the economies of Chile, French Guiana, and Brazil had a positive impact on trade openness; these economies are characterized by their better performance in the economic complexity index, their higher percentage of budget for research and development expenses, and their trade policies oriented towards the industrialization of their value-added products.</span></p>
Information literacy in the area of Library and Information Science. A bibliometric analysis in Latin America, from the Lens database (2001-2020).
<p>The results of scientific production on ALFIN (2001-2020) in the areas of Library and Information Science are shown. All BIC journals were identified from Latindex. Then it was verified whether these journals were contained in the following databases: Web of Science (Core Collection and Scielo Citation Index), Scopus, Lens and Dimensions. The Lens database was chosen for retrieving records on ALFIN and performing the bibliometric analysis, as it has the highest coverage of BIC journals in Latindex. The trend and growth of scientific production were evaluated according to authors and year of publication; the productivity of authors was analyzed using Lotka's Law and the dispersion of the literature according to Bradford's Law. The degree, index and coefficient of collaboration were determined and collaboration networks were identified according to authors. The results show that scientific production on ALFIN in Latin America, reached a peak between 2017 and 2018, presenting a decrease from 2019 onwards. It was also observed that the production, collaboration between authors and the number of journals is predominantly Brazilian.</p>
Latin American and Caribbean journals indexed in Google Scholar Metrics
<p>Dataset from a study aiming to analyze the coverage of Latin American and Caribbean journals in Google Scholar Metrics (GSM). Data from 8,205 journals from 24 countries of the region were downloaded from Latindex database. A Python script was used for automated title search and data extraction (titles, h5-index, h5-median, URLs) in GSM. For the journals not found, a manual search was carried out, with attempts by variations of the title. It was found 3,070 journals indexed in GSM, which corresponds to 37.42% of the Latindex list. The search was performed on the 2021 edition of GSM, which considers articles published between 2016 and 2020 and citations registered until July 2021. The number of all types of documents published (productivity) in the h5-index period (2016-2020) in Scopus, Journal Citation Reports, and SciELO of 1,314 journals was also identified. </p> <p>The present dataset is the result of this study, which is under peer-review in a scientific journal. </p> <p>The dataset comprises titles, h5-index; h5-median, URLs of 3,070 publications from Latin America and the Caribbean identified in Google Scholar Metrics, and the respective editorial information of the publications was extracted from Latindex</p> <p>The original language of the content was kept, mainly Spanish in the case of editorial data from Latindex. The columns descriptors are also shown in English.</p> <p>The productivity data refer to the number of all types of documents published by the journals in the period 2016-2020. Data were extracted from the InCities Journal Citation Reports, Scopus, and SciELO Citation Index (Web of Science database).</p> <p>In this version 2, only the productivity data were changed, covering a larger number of journals (1,314) and including all types of documents. Other data are the same as in the first version (https://doi.org/10.5281/zenodo.5572873).</p> <p> </p> <p> </p> <p> </p> <pre> </pre> <p> </p>
Tiempo de publicación en revistas académicas latinoamericanas. © / Time delay in Latin American academic journals. An international comparative analysis
<p>Cuadros comparativos sobre tiempos de aceptación y de publicación de revistas académicas latinoamericanas (Argentina, Brasil, Chile, Colombia y México) incluidas en Scielo.</p> <p>Comparative tables on acceptance and publication times of Latin American academic journals (Argentina, Brazil, Chile, Colombia and Mexico) included in Scielo.</p>
Visualizing linguistic variation in a network of Latin documents and scribes
<p>Gephi project files (for Gephi 0.8.x) and Sigma.JS -visualizations.</p>
Birth of the Marimacho: Modernismo's Trans* Cultural Productions in Latin America
<p><em>Birth of the Marimacho: Modernismo’s Trans* Cultural Productions in Latin America </em>is a Horizon Marie Skłodowska Curie Action 2022-funded project under grant number 101103095.<em> </em>This research analyzes the representations of a radical gender embodiment in early-twentieth-century Latin American literature, medical science, and visual culture: the ‹‹marimacho››. In the Hispanic tradition, <em>marimacho </em>is the umbrella notion that may refer to a sexist slur as much as a modern form of non-binary gender, a transmasculine identification, or a lesbian sexual identity. This project examines the aesthetic construction of this hybrid, counter-cultural, and gender-dissident figure in an extensive cultural repertoire that includes novels, plays, silver films, leaflets, medical studies, prison and mental hospital records, poems, newspaper, and tabloid articles from the period 1880-1930.</p> <p>Data collection aims to assemble a corpus of published and unpublished sources held in print and digital archives and special collections in Latin America and Europe. These sources are copyright-free and are available through open access in university digital repositories, national archives, and research institutes in Germany, France, Mexico, Spain, and Argentina. The main purpose of the data collected and presented in this critical reference data base is to analyze its content through the methodologies of literary studies and gender theory concerning the main research questions of the project outlined in the original proposal. Some of the research questions that inform this project are:</p> <p>1) What gendered formats, systems of racial classification, and mechanisms of sexual exclusion did modernista & avant-garde writers use to construct the <em>marimacho</em> (butch women/ early forms of transmasculinities/lesbian populations) but also the <em>maricón</em>(the fairy) as public enemies of national morals?</p> <p>2) How and why did writers engage with tropes of world literary traditions—notions of Greek love, vampirism, primitivism, orientalism, teratology, and sexual inversion— to define the <em>marimachos</em> and <em>maricones</em> as dangers to bourgeois society?</p> <p>3) What aesthetic practices such as clothing, hair, makeup, fashion, fragrance, tattooing, sports, dancing, poetics, and musical performances did queer women, non-binary peoples, and trans* men use to contest naturalized notions of femininity and masculinity?</p> <p>This research seeks to increase the understanding of the region’s politics of gender and sexuality during the so-called positivist era (1880-1930), that is, before the solidification of LGBTQ activism, pride literature, and minority social movements of the Global 1960s. In the period of modernization that this project covers, there was a reaction against new gender dynamics led by cosmopolitan networks of feminism and the discreet homosexual liberation movements of the turn of the century. In response, the keepers of gender normativity created the <em>marimacho</em> as a monster version of conventional masculinity. Today, examining this response of the late 19<sup>th</sup> and early 20<sup>th</sup> centuries is crucial to understand the current anti-LGBT hatred in socio-political discourse.</p> <p><strong> </strong></p> <p><strong> </strong></p> <p><strong> </strong></p>
Dataset: First Trust Latin America AlphaDEX Fund (FLN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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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.