cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5683/sp2/dszxkl
title: Data for: Insights on narrative enactments in signed and spoken discours in Quebec Sign Language, American Sign Language and Quebec French

description: These dataset in video format is to illustrate examples of the data directly which are cited in the chapter of the same title published in the book Signed Language and Gesture Research in Cognitive Linguistics (https://doi.org/10.1515/9783110703788-010). Those videos are used to present a portrait of the use of enactment in sign and spoken discourses for LSQ (L1 and L2), ASL (L1), and French (L1) discourses and are drawn from the Projet Marqspat corpus under the auspices of Groupe de recherche sur la LSQ et le bilinguisme sourd, at Université du Québec à Montréal (UQAM). The data used in this chapter were collected through a SSHRC project (regular grant) from 2011 to 2015, entitled " Étude de la variation de forme et de sens dans la réalisation du marquage spatial pour trois langues des signes". Projet Marqspat is a corpus of spoken/signed data produced in four languages (LSQ, ASL, English and French) where all participants were exposed to the same elicitation conditions,including a four-hour test aimed at the production of specific morphosyntactic, semantic and discursive structures within an elicitation setting. All participants were exposed to forty-four video sketches without use of language presenting different scenarios produced by actors. These were calibrated to elicit the linguistic expression of different concepts (quantity, point of view, etc.) and of different types of events such as descriptive or narrative events (Parisot et al. 2008; Parisot and Saunders 2019). The discourse data were collected from five groups of signers/speakers, with three participants in each group: Deaf signers using LSQ as L1 Deaf signers using LSQ L2 but as have ASL as L1 Deaf signers using LSQ as L2 but use spoken French as L1 Deaf signers using ASL as L1 Non-deaf Quebec French speakers as L1
DOI: 10.25318/2710038801-eng
title: Adoption of advanced material handling, supply chain and logistics technologies, by industry and enterprise size

description: Survey of advanced technology, adoption of advanced material handling, supply chain and logistics technologies, by type of advanced technologies, North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2022.
DOI: 10.25318/2710038801-fra
title: Adoption des technologies de pointe de manutention du matériel, de chaîne d'approvisionnement et de logistique, selon l'industrie et la taille de l'entreprise

description: Enquête sur les technologies de pointe, adoption des technologies de pointe de manutention du matériel, de chaîne d'approvisionnement et de logistique, selon le type de technologies de pointe, le système de classification des industries de l'Amérique du Nord (SCIAN) et la taille de l'entreprise pour le Canada et certaines provinces, en 2022.
DOI: 10.5281/zenodo.8356471
title: Molecular Gas Phase Conformational Ensembles

description: Accurately determining molecular structures' lowest energy states is crucial for fields like drug design and materials science. Conformational search engines, like Auto3D and RDKit, are valuable tools for this. We evaluate their effectiveness alongside CREST and Balloon, using collisional cross section data to validate. Our findings enhance molecular structure determination, aiding fields dependent on precise structures.
DOI: 10.5281/zenodo.8247754
title: Molecular Gas Phase Conformational Ensembles

description: Accurately determining molecular structures' lowest energy states is crucial for fields like drug design and materials science. Conformational search engines, like Auto3D and RDKit, are valuable tools for this. We evaluate their effectiveness alongside CREST and Balloon, using collisional cross section data to validate. Our findings enhance molecular structure determination, aiding fields dependent on precise structures.
DOI: 10.5281/zenodo.8356598
title: Combinatorial_lib_raw

description: Raw deep sequencing data of combinatorial libraries
DOI: 10.5281/zenodo.8356597
title: Combinatorial_lib_raw

description: Raw deep sequencing data of combinatorial libraries
DOI: 10.5281/zenodo.8354867
title: The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges

description: Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"
DOI: 10.5281/zenodo.8355483
title: The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges

description: Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"
DOI: 10.5281/zenodo.8354866
title: The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges

description: Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"
DOI: 10.5281/zenodo.8356633
title: The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges

description: Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"
DOI: 10.5281/zenodo.8356725
title: The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges

description: Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"
DOI: 10.5281/zenodo.8356756
title: The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges

description: Dataset of the paper "The Use of VR/AR End-User Development Tools in Education: Lessons Learned and Future Challenges"
DOI: 10.25318/2710038901-eng
title: Adoption of advanced business intelligence technologies, by industry and enterprise size

description: Survey of advanced technology, adoption of advanced business intelligence technologies, by type of advanced technologies, North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2022.
DOI: 10.25318/2710038901-fra
title: Adoption des technologies de pointe d'informatique décisionnelle, selon l'industrie et la taille de l'entreprise

description: Enquête sur les technologies de pointe, adoption des technologies de pointe d'informatique décisionnelle, selon le type de technologies de pointe, le système de classification des industries de l'Amérique du Nord (SCIAN) et la taille de l'entreprise pour le Canada et certaines provinces, en 2022.
DOI: 10.25318/2710039001-eng
title: Adoption of advanced design and information control technologies, by industry and enterprise size

description: Survey of advanced technology, adoption of advanced design and information control technologies, by type of advanced technologies, North American Industry Classification System (NAICS) and enterprise size for Canada and certain provinces, in 2022.
DOI: 10.25318/2710039001-fra
title: Adoption des technologies de pointe de conception et de contrôle d'information, selon l'industrie et la taille de l'entreprise

description: Enquête sur les technologies de pointe, adoption des technologies de pointe de conception et de contrôle d'information, selon le type de technologies de pointe, le système de classification des industries de l'Amérique du Nord (SCIAN) et la taille de l'entreprise pour le Canada et certaines provinces, en 2022.
DOI: 10.6084/m9.figshare.23875644.v1
title: COIL_Frequencyresponse.m

description: Frequency response of the cascaded optically injection-locked semiconductor laser
DOI: 10.6084/m9.figshare.23875665.v1
title: COIL_polezerodiagram.m

description: Pole-zero diagram of the cascaded optically injection-locked semiconductor laser
DOI: 10.6084/m9.figshare.23875674.v1
title: COIL_Transferfunction.m

description: Transfer function of the cascaded optically injection-locked semiconductor laser
DOI: 10.5517/ccdc.csd.cc2f66f9
title: CCDC 2240290: Experimental Crystal Structure Determination

description: Related Article: Oleh V. Shablykin, Danylo O. Merzhyievskyi, Volodymyr S. Brovarets, Svitlana V. Shishkina|2023|Chemistry of Heterocyclic Compounds|59|521|doi:10.1007/s10593-023-03226-7
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5683/sp2/zl91ox
title: Rockcliffe Pavillion and Lookout

description: This is a dataset of information compiled for Courses CIVE3207 (ARCN4100), CIVE4601 (ARCN4200), CIVE 5603 Course on Advanced Building Characterization, Conservation and Rehabilitation, or any other for students of the Architectural Conservation and Sustainability Program (Engineers and architects) at Carleton University. Students of the Architectural Conservation and Sustainability Program (Engineers and architects) at Carleton University will use these data to produce a Heritage Recording Dossier of this important Site. This material can not be used for commercial purposes or use in any form without the permission of Dr. Mario Santana, the authors and site custodians.
DOI: 10.5281/zenodo.6473731
title: FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)

description: Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050. Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five <em>climate-aware</em> scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (<em>dynamic development</em>). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum.
DOI: 10.5281/zenodo.6473730
title: FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)

description: Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050. Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five <em>climate-aware</em> scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (<em>dynamic development</em>). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum. Additionally, we include <code>migration_matrix.csv</code> derived from IRS data, that contains probability values of moving from an origin county in the case study (row) to any other counties (columns).
DOI: 10.5281/zenodo.6607860
title: FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)

description: Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050. Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five <em>climate-aware</em> scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (<em>dynamic development</em>). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum. Additionally, we include <code>migration_matrix.csv</code> derived from IRS data, that contains probability values of moving from an origin county in the case study (row) to any other counties (columns).
DOI: 10.5281/zenodo.8356863
title: FUTURES land change forecasts in response to flooding in Charleston area, South Carolina (2019-2050)

description: Climate-aware scenarios of FUTURES land change projections in Charleston area (Cherleston, Dorchester, Berkeley) for 2019-2050. Zipped folder <code>results</code> contains FUTURES v3 simulation runs for 3 counties in South Carolina, USA (Charleston, Berkeley, Dorchester) from 2019 to 2050. Included are five <em>climate-aware</em> scenarios (reactive, managed retreat, resist, polarized population, trapped population) and one scenario which does not take future climate conditions into account (<em>dynamic development</em>). Each folder contains 50 monte-carlo simulation results with <code>developed_seed_X.tif</code> and <code>adapted_seed_X.tif</code> where <code>X</code> goes from 1 to 50. Values of <code>developed_seed_X.tif</code> range from -31 to 31, where the positive values represent simulation step when an undeveloped pixel was developed and the negative values represent simulation step when a developed pixel was abandoned. Zero stands for initial development. For example, value -11 means the particular pixel was abandoned in 2030. Note that values of raster files in <code>Dynamic development</code> folder range from -1 to 31, where -1 means undeveloped and the rest is the same as above. Values of <code>adapted_seed_X.tif</code> range from 0 to 100, where 0 means trapped (flooded but no adaptation), the other values represent return period for which the pixel is adapted (e.g. 2-, 5-, 10-, 20-, 50- and 100-year flood). The files' CRS is Albers Conic Equal Area, NAD83(2011) datum. Additionally, we include <code>migration_matrix.csv</code> derived from IRS data, that contains probability values of moving from an origin county in the case study (row) to any other counties (columns).
DOI: 10.25573/data.24158454
title: NMAAHC Freedmen's Bureau Digital Collection transcribed data

description: The Bureau of Refugees, Freedmen, and Abandoned Lands, often referred to as the Freedmen’s Bureau, was established on March 3, 1865 and was active until 1872. The duties of the Freedmen’s Bureau included supervision of all affairs relating to refugees, freedmen, and the custody of abandoned lands and property. The Freedmen's Bureau Digital Collection is comprised of digital surrogates previously available on the 1918 rolls of microfilm held by the National Archives and Records Administration. This dataset includes images and transcriptions for everything transcribed as of February, 2022, which includes 346,632 records. Dataset Card with more information available here: https://github.com/Smithsonian/dataset-cards/blob/main/NMAAHC-Freedmens-Bureau-Digital-Collection.md
DOI: 10.25573/data.24158454.v1
title: NMAAHC Freedmen's Bureau Digital Collection transcribed data

description: The Bureau of Refugees, Freedmen, and Abandoned Lands, often referred to as the Freedmen’s Bureau, was established on March 3, 1865 and was active until 1872. The duties of the Freedmen’s Bureau included supervision of all affairs relating to refugees, freedmen, and the custody of abandoned lands and property. The Freedmen's Bureau Digital Collection is comprised of digital surrogates previously available on the 1918 rolls of microfilm held by the National Archives and Records Administration. This dataset includes images and transcriptions for everything transcribed as of February, 2022, which includes 346,632 records. Dataset Card with more information available here: https://github.com/Smithsonian/dataset-cards/blob/main/NMAAHC-Freedmens-Bureau-Digital-Collection.md
DOI: 10.7298/qgyq-ym43
title: Digital Elevation Model Data from: Measuring Erosional and Depositional Patterns Across Comet 67P's Imhotep Region
DOI: 10.15139/s3/mioviz
title: PharmFIT Patient Survey

description: This dataset contains the PharmFIT Patient Survey instrument and results (n=1040). The purpose of the PharmFIT survey is to understand patient preferences for a program designed to distribute fecal immunochemical test kits for colorectal cancer screening from community pharmacies.
DOI: 10.5281/zenodo.8356980
title: PATL2: A Potential Contraceptive Target August 2023 Update

description: PATL2: A Potential Contraceptive Target August 2023 Update
DOI: 10.5281/zenodo.8356979
title: PATL2: A Potential Contraceptive Target August 2023 Update

description: PATL2: A Potential Contraceptive Target August 2023 Update
DOI: 10.5281/zenodo.8284809
title: L2LFlows: Generating High-Fidelity 3D Calorimeter Images

description: This upload contains the datasets used in arXiv:2302.11594. The file <em>g4-showers_950k_10x10_train_val_test.pt</em> contains the <strong>760k training</strong>, <strong>95k validation</strong> and <strong>95k test</strong> showers as well as their incident energies. It should be loaded as follows: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - <em>import torch </em> <em>list_tensors = torch.load(args.file_path)</em> <em>for (idx, tensor) in enumerate(list_tensors):</em> <em> [showers_train, showers_val, showers_test, inc_energies_train, inc_energies_val, inc_energies_test] = list_tensors</em> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - The file <em>g4-showers_665k_10x10_test.pt</em> contains 665k additional showers that were used for the classifier scaling studies, in addition to the 95k test showers from the file <em>g4-showers_950k_10x10_train_val_test.pt</em>. It should be loaded as follows: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - <em>import torch</em> <em>list_tensors = torch.load("g4-showers_950k_10x10_train_val_test.pt") </em> <em>[showers_geant, inc_energies_geant] = list_tensors</em> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - A detailed description of how the datasets were simulated can be found in the paper.
DOI: 10.34740/kaggle/dsv/6495410
title: Balochi Language to English Translation Dataset
DOI: 10.5281/zenodo.8357021
title: Trove of Gut Virus Genomes (TGVG)

description: <strong>TGVG_v1.1.genomes.all.fna</strong> Sequences from the Gut Virome Database, the Cenote Human Virome Database, the Metagenomic Gut Virus catalog, and the Gut Phage Database were downloaded and dereplicated at 95% average nucleotide identity (ANI) across 85% alignment fraction (AF) using anicalc.py and aniclust.py from the CheckV (version 0.9.0) package, in line with metagenomic virus sequence community standards. Exemplar sequences from each cluster/singleton from the input sequences were kept and ran through Cenote-Taker 2 (version 2.1.5) to predict virus hallmark genes within each sequence using the ‘virion’ hallmark gene database. Sequences were kept if they 1) encoded direct terminal repeats (signature of complete virus genome), one or more virus hallmark genes, and were over 1.5 kilobases or longer, or 2) encoded 2 or more virus hallmark genes and were over 12 kilobases. Sequences passing this threshold were run through CheckV to remove flanking host (bacterial) sequences and quantify the virus gene/bacteria gene ratio for each contig. Sequences with 3 or fewer virus genes and 3 or more bacterial genes after pruning/were discarded. Finally, sequences passing this threshold were dereplicated again with CheckV scripts at 95% ANI and 85% AF to yield the Trove of Gut Virus Genomes of 110,296 genomes/genome fragments each representing a viral SGB. <strong>TGVG_v1.1_metadata.tsv</strong> For each sequence in the Trove of Gut Virus Genomes CheckV was used to estimate completeness, ipHOP (version 1.1.0) was used to predict bacterial/archael host genus. Bacphlip (version 0.9.3) was run on each of the sequences predicted to be 90% or more complete to predict phage virulence.<br> vConTACT2 (version 0.11.3) was used to cluster viral SGBs from the Trove of Gut Virus Genomes into virus clusters. In addition to viral SGBs with vConTACT2 “Singleton” labels, viral SGBs with vConTACT2 labels “Unassigned”, “Outlier”, “Overlap”, “Clustered/Singleton” were also considered “Singletons” for downstream analysis. Genomad (version 1.5.2) taxonomy module was run on each sequence to obtain taxonomical assignment at the phylum, class, order, and family levels.<br>