cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17876/plate/dr.3/plates/101_6217
title: Digital object identifier (DOI) for the plate 6217 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6218
title: Digital object identifier (DOI) for the plate 6218 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6219
title: Digital object identifier (DOI) for the plate 6219 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_621
title: Digital object identifier (DOI) for the plate 621 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6220
title: Digital object identifier (DOI) for the plate 6220 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6221
title: Digital object identifier (DOI) for the plate 6221 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6222
title: Digital object identifier (DOI) for the plate 6222 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6223
title: Digital object identifier (DOI) for the plate 6223 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6224
title: Digital object identifier (DOI) for the plate 6224 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6225
title: Digital object identifier (DOI) for the plate 6225 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6226
title: Digital object identifier (DOI) for the plate 6226 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6227
title: Digital object identifier (DOI) for the plate 6227 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6228
title: Digital object identifier (DOI) for the plate 6228 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6229
title: Digital object identifier (DOI) for the plate 6229 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_622
title: Digital object identifier (DOI) for the plate 622 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6230
title: Digital object identifier (DOI) for the plate 6230 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6231
title: Digital object identifier (DOI) for the plate 6231 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6232
title: Digital object identifier (DOI) for the plate 6232 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6233
title: Digital object identifier (DOI) for the plate 6233 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6234
title: Digital object identifier (DOI) for the plate 6234 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6235
title: Digital object identifier (DOI) for the plate 6235 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6236
title: Digital object identifier (DOI) for the plate 6236 in archive 101 from the APPLAUSE DR3
DOI: 10.5066/p9qkjr3g
title: Updates to the Everglades Vulnerability Analysis (EVA) vegetation module

description: The Everglades Vulnerability Analysis (EVA) is a series of connected, modular Bayesian networks that predict the response of several Everglades indicators of ecosystem health to changes in hydrology, salinity, and the landscape. This release provides the code to update the vegetation module of EVA, validate the updated module, and provides the process and outputs of a sensitivity analysis of the module. Key updates include expanding the number of vegetation classes predicted from 6 to 11 classes, simplifying the inputs to the module, and increasing the number of vegetation observations used to parameterize the network. The validation of the module includes the process to calculate receiver operating characteristic curves and their associated area under the curve values, multi-class Brier scores, and classification error loss from a 10-fold cross-validation on the network. The sensitivity analyses explore the period of record under scenarios of altered hydrology or salinity and determine the most likely vegetation outcome given the proportion of states within the period of record being explored.
DOI: 10.17876/plate/dr.3/plates/101_6237
title: Digital object identifier (DOI) for the plate 6237 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6238
title: Digital object identifier (DOI) for the plate 6238 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6239
title: Digital object identifier (DOI) for the plate 6239 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_623
title: Digital object identifier (DOI) for the plate 623 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6240
title: Digital object identifier (DOI) for the plate 6240 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6241
title: Digital object identifier (DOI) for the plate 6241 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6242
title: Digital object identifier (DOI) for the plate 6242 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6243
title: Digital object identifier (DOI) for the plate 6243 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6244
title: Digital object identifier (DOI) for the plate 6244 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6245
title: Digital object identifier (DOI) for the plate 6245 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6246
title: Digital object identifier (DOI) for the plate 6246 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6247
title: Digital object identifier (DOI) for the plate 6247 in archive 101 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/101_6248
title: Digital object identifier (DOI) for the plate 6248 in archive 101 from the APPLAUSE DR3
DOI: 10.5281/zenodo.7557410
title: INSPIRE-seq simultaneously selects nanobodies for immune epitopes in the complex tumor microenvironment

description: scRNAseq of CD45 magnetic microbeads enriched cells were isolated form Py8119 bearing mice (three mice per pool/group) two hours after injection of either PBS, insertless phage display, CD45, DCs, or CD8 specific VHHs phage display libraries.
DOI: 10.5281/zenodo.7557409
title: INSPIRE-seq simultaneously selects nanobodies for immune epitopes in the complex tumor microenvironment

description: scRNAseq of CD45 magnetic microbeads enriched cells were isolated form Py8119 bearing mice (three mice per pool/group) two hours after injection of either PBS, insertless phage display, CD45, DCs, or CD8 specific VHHs phage display libraries.
DOI: 10.6084/m9.figshare.3761562.v2465
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.5517/ccdc.csd.cc286b72
title: CCDC 2091453: Experimental Crystal Structure Determination

description: Related Article: Sevde Demir, Fatma Yuksel|2023|Inorg.Chim.Acta|548|121373|doi:10.1016/j.ica.2022.121373
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: 10.6073/pasta/8f146c12218336f3447e4a7e79080fd2
title: Desert Fertilization Experiment: investigation of Sonoran desert ecosystem response to atmospheric deposition and experimental nutrient addition, ongoing since 2006

description: Launched in 2006 with support from the National Science Foundation (NSF) and leveraged by the CAP LTER, the Carbon and Nitrogen deposition (CNdep) project sought to answer the fundamental question of whether elemental cycles in urban ecosystems are qualitatively different from those in non-urban ecosystems. Ecosystem scientists, atmospheric chemists, and biogeochemists tested the hypothesis that distinct biogeochemical pathways result from elevated inorganic nitrogen and organic carbon deposition from the atmosphere to the land. To test the hypothesis, scientists examined the responsiveness of Sonoran desert ecosystems to nutrient enrichment by capitalizing on a gradient of atmospheric deposition in and around the greater Phoenix metropolitan area. Fifteen desert study sites were established, with five locations each west and east of the urban core, and in the urban core in desert preserves. In addition to the gradient of atmospheric deposition in and around the urban core, select study plots at each of the fifteen desert locations receive amendments of nitrogen, phosphorus, or nitrogen + phosphorus fertilizer. Measured variables include soil properties, perennial and annual plant growth, and atmospheric deposition of nitrogen. At the close of the initial grant period, the CAP LTER assumed responsibility for the project, renamed the Desert Fertilization Experiment, which provides a remarkable platform to study the long-term effects of nutrient enrichment on desert ecosystem properties.
DOI: 10.5281/zenodo.7557402
title: Abeshu-etal_2023_GMD: Model Input and Output data

description: 1. Input data required to conduct the experiment 2. Model output data for reproducing the manuscript figures
DOI: 10.5281/zenodo.7139978
title: Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output

description: We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook, is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.
description: This work was conducted with the support of the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program.
DOI: 10.5281/zenodo.7139977
title: Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output

description: We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook (and replicated in a Python file), is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.
description: This work was conducted with the support of the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program.
DOI: 10.5281/zenodo.7557622
title: Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output

description: We produce a dataset that uses pattern scaling, a common method of emulating climate models. Our dataset is built on the Pangeo CMIP6 archive, which has the advantage that we don't need to actually download the climate model output. Here we demonstrate the utility of our dataset, called Pangeo-Enabled ESM Pattern Scaling (PEEPS). The dataset, which is encapsulated in a Jupyter notebook (and replicated in a Python file), is flexible and can be extended to multiple scenarios and multiple variables, as long as they are in the Pangeo-accessible archive.
description: This work was conducted with the support of the U.S. Department of Energy, Office of Science, as part of research in MultiSector Dynamics, Earth and Environmental System Modeling Program.
DOI: 10.6084/m9.figshare.21905463
title: Unsupervised learning reveals interpretable latent representations for translucency perception

description: <strong>Contents:</strong> 1. Experiment_Stimuli.zip:<br> - The image stimuli used in psychophysical experiments (Exp 1, 2 and 3). <br> 2. Psychophysics_Data.zip:<br> - The human data of psychophysical experiments (Exp 1, 2 and 3). <br> 3. Trained_TAG_Model.zip:<br> - stylegan2-ada: the trained StyleGAN2-ADA model used to generated the image stimuli used in psychophysical experiments. - psp-encoder: the trained pixel2style2pixel (pSp) encoder network. <br> 4. TID_res1024_8k.zip: - Translucent Image Dataset: containing 8085 high-resolution (1024 x 1024) images of soaps.
DOI: 10.5281/zenodo.7452707
title: FSD-FS

description: FSD-FS is a publicly-available database of human labelled sound events for few-shot learning. It spans across 143 classes obtained from the AudioSet Ontology and contains 43805 raw audio files collected from the FSD50K. FSD-FS is curated at the Centre for Digital Music, Queen Mary University of London. <strong>Citation</strong> If you use the FSD-FS dataset, please cite our paper and FSD50K. <pre><code>@article{liang2022learning, title={Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition}, author={Liang, Jinhua and Phan, Huy and Benetos, Emmanouil}, journal={arXiv preprint arXiv:2212.08952}, year={2022} } @ARTICLE{9645159, author={Fonseca, Eduardo and Favory, Xavier and Pons, Jordi and Font, Frederic and Serra, Xavier}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, title={FSD50K: An Open Dataset of Human-Labeled Sound Events}, year={2022}, volume={30}, number={}, pages={829-852}, doi={10.1109/TASLP.2021.3133208}}</code></pre> <strong>About FSD-FS</strong> FSD-FS is an open database for multi-label few-shot audio classification containing 143 classes drawn from the FSD50K. It also inherits the AudioSet Ontology. FSD-FS follows the ratio 7:2:1 to split classes into base, validation, and evaluation sets, so there are 98 classes in the base set, 30 classes in the validation set, and 15 classes in the evaluation set (More details can be found in our paper). <strong>LICENSE</strong> FSD-FS are released in Creative Commons (CC) licenses. Same as FSD50K, each clip has its own license as defined by the clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. For more details, ones can refer to the link. <strong>FILES</strong> FSD-FS are organised in the structure: <pre><code>root | └─── dev_base | └─── dev_val | └─── eval</code></pre> <strong>REFERENCES AND LINKS</strong> [1] Gemmeke, Jort F., et al. "Audio set: An ontology and human-labeled dataset for audio events." <em>2017 IEEE international conference on acoustics, speech and signal processing (ICASSP)</em>. IEEE, 2017. [paper] [link] [2] Fonseca, Eduardo, et al. "Fsd50k: an open dataset of human-labeled sound events." <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em> 30 (2021): 829-852. [paper] [code]
DOI: 10.5281/zenodo.7452708
title: FSD-FS

description: FSD-FS is a publicly-available database of human labelled sound events for few-shot learning. It spans across 143 classes obtained from the AudioSet Ontology and contains 43030 raw audio files collected from the FSD50K. FSD-FS is curated at the Centre for Digital Music, Queen Mary University of London. <strong>Citation</strong> If you use the FSD-FS dataset, please cite our paper and FSD50K. <pre><code>@article{liang2022learning, title={Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition}, author={Liang, Jinhua and Phan, Huy and Benetos, Emmanouil}, journal={arXiv preprint arXiv:2212.08952}, year={2022} } @ARTICLE{9645159, author={Fonseca, Eduardo and Favory, Xavier and Pons, Jordi and Font, Frederic and Serra, Xavier}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, title={FSD50K: An Open Dataset of Human-Labeled Sound Events}, year={2022}, volume={30}, number={}, pages={829-852}, doi={10.1109/TASLP.2021.3133208}}</code></pre> <strong>About FSD-FS</strong> FSD-FS is an open database for multi-label few-shot audio classification containing 143 classes drawn from the FSD50K. It also inherits the AudioSet Ontology. FSD-FS follows the ratio 7:2:1 to split classes into base, validation, and evaluation sets, so there are 98 classes in the base set, 30 classes in the validation set, and 15 classes in the evaluation set (More details can be found in our paper). <strong>LICENSE</strong> FSD-FS are released in Creative Commons (CC) licenses. Same as FSD50K, each clip has its own license as defined by the clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. For more details, ones can refer to the link. <strong>FILES</strong> FSD-FS are organised in the structure: <pre><code>root | └─── base | └─── val | └─── eval</code></pre> <strong>REFERENCES AND LINKS</strong> [1] Gemmeke, Jort F., et al. "Audio set: An ontology and human-labeled dataset for audio events." <em>2017 IEEE international conference on acoustics, speech and signal processing (ICASSP)</em>. IEEE, 2017. [paper] [link] [2] Fonseca, Eduardo, et al. "Fsd50k: an open dataset of human-labeled sound events." <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em> 30 (2021): 829-852. [paper] [code]
DOI: 10.5281/zenodo.7557107
title: FSD-FS

description: FSD-FS is a publicly-available database of human labelled sound events for few-shot learning. It spans across 143 classes obtained from the AudioSet Ontology and contains 43805 raw audio files collected from the FSD50K. FSD-FS is curated at the Centre for Digital Music, Queen Mary University of London. <strong>Citation</strong> If you use the FSD-FS dataset, please cite our paper and FSD50K. <pre><code>@article{liang2022learning, title={Learning from Taxonomy: Multi-label Few-Shot Classification for Everyday Sound Recognition}, author={Liang, Jinhua and Phan, Huy and Benetos, Emmanouil}, journal={arXiv preprint arXiv:2212.08952}, year={2022} } @ARTICLE{9645159, author={Fonseca, Eduardo and Favory, Xavier and Pons, Jordi and Font, Frederic and Serra, Xavier}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, title={FSD50K: An Open Dataset of Human-Labeled Sound Events}, year={2022}, volume={30}, number={}, pages={829-852}, doi={10.1109/TASLP.2021.3133208}}</code></pre> <strong>About FSD-FS</strong> FSD-FS is an open database for multi-label few-shot audio classification containing 143 classes drawn from the FSD50K. It also inherits the AudioSet Ontology. FSD-FS follows the ratio 7:2:1 to split classes into base, validation, and evaluation sets, so there are 98 classes in the base set, 30 classes in the validation set, and 15 classes in the evaluation set (More details can be found in our paper). <strong>LICENSE</strong> FSD-FS are released in Creative Commons (CC) licenses. Same as FSD50K, each clip has its own license as defined by the clip uploader in Freesound, some of them requiring attribution to their original authors and some forbidding further commercial reuse. For more details, ones can refer to the link. <strong>FILES</strong> FSD-FS are organised in the structure: <pre><code>root | └─── dev_base | └─── dev_val | └─── eval</code></pre> <strong>REFERENCES AND LINKS</strong> [1] Gemmeke, Jort F., et al. "Audio set: An ontology and human-labeled dataset for audio events." <em>2017 IEEE international conference on acoustics, speech and signal processing (ICASSP)</em>. IEEE, 2017. [paper] [link] [2] Fonseca, Eduardo, et al. "Fsd50k: an open dataset of human-labeled sound events." <em>IEEE/ACM Transactions on Audio, Speech, and Language Processing</em> 30 (2021): 829-852. [paper] [code]
DOI: 10.5281/zenodo.7557690
title: Supplementary materials of the active learning methodology for online engineering education

description: These are the supplementary materials for the active learning methodology for engineering education integrating online and mobile learning. The methodology is focused on teaching electronics, physical computing, basic robotics, and programming. The purpose of the methodology was to provide active learning, experimentation, and reflection in online classes to the students increasing their motivation and self-efficacy. The file contains the following elements: Rubric employed to evaluate the student-created videos and blogs. The survey's questions of the different courses that employ online and mobile learning modalities. A set of URLs with examples of the blogs and videos constructed by the students.
DOI: 10.5281/zenodo.7557689
title: Supplementary materials of the active learning methodology for online engineering education

description: These are the supplementary materials for the active learning methodology for engineering education integrating online and mobile learning. The methodology is focused on teaching electronics, physical computing, basic robotics, and programming. The purpose of the methodology was to provide active learning, experimentation, and reflection in online classes to the students increasing their motivation and self-efficacy. The file contains the following elements: Rubric employed to evaluate the student-created videos and blogs. The survey's questions of the different courses that employ online and mobile learning modalities. A set of URLs with examples of the blogs and videos constructed by the students.
DOI: 10.15156/bio/sh0828854.10fu
title: SH0828854.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0828855.10fu
title: SH0828855.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.