cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21896133.v2
title: Virus-host dynamics in archaeal groundwater biofilms and the associated bacterial community composition: Main manuscript Figure 4: Bacterial community composition of MSI biofilm flocks

description: The .tsv file shows the community composition of individual MSI biofilm flocks with the relative abundance (%) of the bacterial taxa at the 16S genus level.
DOI: 10.6084/m9.figshare.21864405
title: Virus-host dynamics in archaeal groundwater biofilms and the associated bacterial community composition: Main manuscript Figure 4: Nanopore data of individual biofilm flocks from the MSI

description: Near full-length 16S rRNA gene amplicon sequencing of ten individual biofilm flocks from the MSI for investigating the bacteriome.
DOI: 10.6084/m9.figshare.21864405.v1
title: Main manuscript Figure 4: Nanopore data of individual biofilm flocks from the MSI

description: Near full-length 16S rRNA gene amplicon sequencing of ten individual biofilm flocks from the MSI for investigating the bacteriome.
DOI: 10.6084/m9.figshare.21864405.v2
title: Virus-host dynamics in archaeal groundwater biofilms and the associated bacterial community composition: Main manuscript Figure 4: Nanopore data of individual biofilm flocks from the MSI

description: Near full-length 16S rRNA gene amplicon sequencing of ten individual biofilm flocks from the MSI for investigating the bacteriome.
DOI: 10.5281/zenodo.7763679
title: AIRSEAL Project - Data sample

description: The attached files contain test data (main parameters measured during rotating labyrinth seals testing).<br> The .csv data is structured as follows: column1 = Time [s]<br> column2 = Tut [deg C]<br> column3 = PR [non dimentional]<br> column4 = N [rpm]<br> column5 = CLR [mm]<br> column6 = Qma [kg/s]<br> column7 = Swirler_angle [deg]<br> <br> The "11" in file names is related to the first configuration (which has been tested in the project): METCO casing and axial flow (0 deg swirl).<br> The "1" to "30" file indexes are related to test number (stabilized regime: pressure ratio, rotor speed and temperature).
DOI: 10.5281/zenodo.7763678
title: AIRSEAL Project - Data sample

description: The attached files contain test data (main parameters measured during rotating labyrinth seals testing).<br> The .csv data is structured as follows: column1 = Time [s]<br> column2 = Tut [deg C]<br> column3 = PR [non dimentional]<br> column4 = N [rpm]<br> column5 = CLR [mm]<br> column6 = Qma [kg/s]<br> column7 = Swirler_angle [deg]<br> <br> The "11" in file names is related to the first configuration (which has been tested in the project): METCO casing and axial flow (0 deg swirl).<br> The "1" to "30" file indexes are related to test number (stabilized regime: pressure ratio, rotor speed and temperature).
DOI: 10.25573/serc.21947627.v1
title: Dataset: Methane fluxes from automated chambers, Global Change Research Wetland, Maryland

description: These datasets contain CH<sub>4 </sub>and CO<sub>2</sub> fluxes and related environmental data measured from an automated chamber system in the Smithsonian's Global Change Research Wetland (GCReW), a brackish, microtidal wetland side on a subestuary of the Chesapeake Bay. Fluxes were measured in 12 chambers, from May 2021 through December 2022.
DOI: 10.5683/sp2/k03kwc
title: LAPOP: Honduras 2004

description: The Latin American Public Opinion Project (LAPOP) is an academic institution hosted by Vanderbilt University that began with the study of democratic values in Costa Rica, but today carries out surveys in much of Latin America, Canada, the United States, and the Caribbean. In 2004, LAPOP established the AmericasBarometer as multi-country, regularly conducted survey of democratic values and behaviors in the Americas, and the first round included voting-age respondents from 11 countries. LAPOP collected opinions on major problems facing the country and community, threats to security, attitudes towards the country's political system, and trust in government institutions. Also measured were respondents' participation in community organizations, attitudes towards government critics, voting behavior, confidence in local government, and the prevalence of crime. Demographic information collected includes nationality, ethnicity, native language, age, gender, education level, occupation, religion, and whether the respondent lives in an urban or rural area.
DOI: 10.5281/zenodo.7477560
title: Aerial Landing Pad Dataset

description: <strong>Landing Pad Dataset:</strong> The Landing Pad Image Dataset is a collection of images containing a landing pad, along with object annotations for the landing pad found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for autonomous landing of Unmanned Aerial Vehicles (UAVs). This dataset is intended for use in research and development of UAV landing systems, such as those used in industrial applications. The inclusion of the landing pad annotations allows for the detection and localization of the landing pad in real-time, enabling the UAV to safely and accurately land on the designated landing surface. Noted that some of the images are also taken from a simulation. The dataset consists of the following images and detection objects (Landing Pad and Kios Logo): Subset Images Landing Pad Kios Logo Training 961 818 349 Validation 480 425 153 Testing 480 412 167 It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing). <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong> Rafael Makrigiorgis, Panayiotis Kolios, Christos Kyrkou, & Charalambos Soteriou. (2022). Aerial Landing Pad Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.747756
DOI: 10.5281/zenodo.7477559
title: Aerial Landing Pad Dataset

description: <strong>Landing Pad Dataset:</strong> The Landing Pad Image Dataset is a collection of images containing a landing pad, along with object annotations for the landing pad found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for autonomous landing of Unmanned Aerial Vehicles (UAVs). This dataset is intended for use in research and development of UAV landing systems, such as those used in industrial applications. The inclusion of the landing pad annotations allows for the detection and localization of the landing pad in real-time, enabling the UAV to safely and accurately land on the designated landing surface. Noted that some of the images are also taken from a simulation. The dataset consists of the following images and detection objects (Landing Pad and Kios Logo): Subset Images Landing Pad Kios Logo Training 961 818 349 Validation 480 425 153 Testing 480 412 167 It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing). <strong>**NOTE** If you use this dataset in your research/publication please cite us using the following </strong> Rafael Makrigiorgis, Panayiotis Kolios, Christos Kyrkou, & Charalambos Soteriou. (2022). Aerial Landing Pad Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.747756
DOI: 10.6084/m9.figshare.22324900
title: sexual minority in Kosovo study database

description: The data set is for the study Kosovo Sexual Minorities: A Mental Health Crisis
DOI: 10.6084/m9.figshare.22324900.v1
title: sexual minority in Kosovo study database

description: The data set is for the study Kosovo Sexual Minorities: A Mental Health Crisis
DOI: 10.5281/zenodo.7755545
title: Macrozooplankton at the Barents Sea Polar Front in May 2022

description: # Macrozooplankton across the Barents Sea Polar Front in May 2022 ## Methods ### Study area This dataset is the result from sampling at 6 stations across the Polar Front in the western part of the Barents Sea. ### Time coverage The samples were collected between 19 May 2022 and 25 May 2022. ### Sampling 11 macrozooplankton samples were collected with a Tucker trawl (1 m<sup>2</sup> opening and 1500 μm mesh size) and towed for 10 minutes at ca. 2 knots. The targeted depth at each station was determined from the sound scattering layer identified in the echogram from the vessel's echosounder. ### Sample analysis Relative abundance from the Tucker trawl samples were analyzed per station, and taxa were counted and dried at 60 C° in pre-weighted recipients for dry weight measurements. The obtained abundances were standardized and converted to catch per unit effort, expressed in milligrams per cubic meter.
DOI: 10.5281/zenodo.7763682
title: Macrozooplankton at the Barents Sea Polar Front in May 2022

description: # Macrozooplankton across the Barents Sea Polar Front in May 2022 ## Methods ### Study area This dataset is the result from sampling at 6 stations across the Polar Front in the western part of the Barents Sea. ### Time coverage The samples were collected between 19 May 2022 and 25 May 2022. ### Sampling 11 macrozooplankton samples were collected with a Tucker trawl (1 m<sup>2</sup> opening and 1500 μm mesh size) and towed for 10 minutes at ca. 2 knots. The targeted depth at each station was determined from the sound scattering layer identified in the echogram from the vessel's echosounder. ### Sample analysis Relative abundance from the Tucker trawl samples were analyzed per station, and taxa were counted and dried at 60 C° in pre-weighted recipients for dry weight measurements. The obtained abundances were standardized and converted to catch per unit effort, expressed in milligrams per cubic meter.
DOI: 10.35078/vwpmhe
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Uberaba| Município: Gurinhatã

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5281/zenodo.7591280
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.0-alpha release candidate</strong>: 18761 annotations for 3706 documents (train-validation) <pre>train 14937 val 3827</pre> <pre>extractive 9843 abstractive 8921</pre>
description: Binaries are hosted elsewhere for now.
DOI: 10.5281/zenodo.7600128
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.1-alpha release candidate</strong>: 18764 annotations for 3702 documents (train-validation) <pre>train 14947 val 3827 </pre> <pre>extractive 13608 abstractive 5166 </pre>
description: Binaries are hosted elsewhere for now.
DOI: 10.5281/zenodo.7600505
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.1-alpha release candidate</strong>: 18759 annotations for 3702 documents (train-validation) <pre>train 14938 val 3821 </pre>
description: Binaries are hosted elsewhere for now.
DOI: 10.5281/zenodo.7652563
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.3 release candidate</strong>: 30102 annotations for 3702 documents (train-validation) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23774 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6328 }) }) </pre>
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.5281/zenodo.7662385
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.4 release candidate</strong>: 30102 annotations for 3702 documents (train-validation) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23774 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6328 }) }) </pre>
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.5281/zenodo.7662488
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.4 release candidate</strong>: 30102 annotations for 3702 documents (train-validation) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23774 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6328 }) }) ++update on answer_type</pre>
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.5281/zenodo.7667761
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.5 release candidate</strong>: 30102 annotations for 3702 documents (train-validation) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23774 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6328 }) }) ++update on answer_type +++formatting change to answers_variants </pre>
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.5281/zenodo.7680589
title: DUDE competition train and validation splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>1.0.6 release candidate</strong>: 30102 annotations for 3702 documents (train-validation) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23774 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6328 }) }) ++update on answer_type +++formatting change to answers_variants ++++stricter check on answer_variants </pre>
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.5281/zenodo.7680617
title: DUDE competition train - validation - test splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>V1.0.7 release</strong>: 41454 annotations for 4974 documents (train-validation-test) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23728 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6315 }) test: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 11411 }) }) ++update on answer_type +++formatting change to answers_variants ++++stricter check on answer_variants & rename annotations file <strong>+ blind test set (no ground truth answers provided)</strong> </pre>
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.5281/zenodo.7591279
title: DUDE competition train - validation - test splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>V1.0.7 release</strong>: 41454 annotations for 4974 documents (train-validation-test) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23728 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6315 }) test: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 11402 }) }) ++update on answer_type +++formatting change to answers_variants ++++stricter check on answer_variants & rename annotations file <strong>+ blind test set (no ground truth answers provided) </strong>++ removed duplicates from test set: </pre> "92bd5c758bda9bdceb5f67c17009207b_ac6964cbdf483e765b6668e27b3d0bc4", "6ee71a16d4e4d1dbd7c1f569a92d4e08_549f2a163f8ff3e9f0293cf59fdd98bc", "e6f3855472231a7ca6aada2f8e85fe5a_827c03a72f2552c722f2c872fd7f74c3", "e3eecd7cca5de11f1d17cd94ae6a8d77_6300df64e4cf6ba0600ac81278f68de2", "107b4037df8127a92ee4b6ae9b5df8fb_d7a60e7a9fc0b27487ea39cd7f56f98e", "300cc3900080064d308983f958141232_6a7cf1aad908d58a75ab8e02ddc856f4", "fdd3308efacddb88d4aa6e2073f481d4_138cb868ecc804a63cc7a4502c0009b2", "1f7de256ff1743d329a8402ba0d132e7_95b6e8758533a9817b9f20a958e7b776", "4f399b8c526ffb6a2fd585a18d4ed5ec_51097231bc327c26c59a4fd8d3ff3069",
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.5281/zenodo.7763635
title: DUDE competition train - validation - test splits ground truth

description: This JSON file contains the ground truth annotations for the train and validation set of the DUDE competition (https://rrc.cvc.uab.es/?ch=23&com=tasks) of ICDAR 2023 (https://icdar2023.org/). <strong>V1.0.7 release</strong>: 41454 annotations for 4974 documents (train-validation-test) <pre>DatasetDict({ train: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 23728 }) val: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 6315 }) test: Dataset({ features: ['docId', 'questionId', 'question', 'answers', 'answers_page_bounding_boxes', 'answers_variants', 'answer_type', 'data_split', 'document', 'OCR'], num_rows: 11402 }) }) ++update on answer_type +++formatting change to answers_variants ++++stricter check on answer_variants & rename annotations file <strong>+ blind test set (no ground truth answers provided) </strong>++ removed duplicates from test set: </pre> "92bd5c758bda9bdceb5f67c17009207b_ac6964cbdf483e765b6668e27b3d0bc4", "6ee71a16d4e4d1dbd7c1f569a92d4e08_549f2a163f8ff3e9f0293cf59fdd98bc", "e6f3855472231a7ca6aada2f8e85fe5a_827c03a72f2552c722f2c872fd7f74c3", "e3eecd7cca5de11f1d17cd94ae6a8d77_6300df64e4cf6ba0600ac81278f68de2", "107b4037df8127a92ee4b6ae9b5df8fb_d7a60e7a9fc0b27487ea39cd7f56f98e", "300cc3900080064d308983f958141232_6a7cf1aad908d58a75ab8e02ddc856f4", "fdd3308efacddb88d4aa6e2073f481d4_138cb868ecc804a63cc7a4502c0009b2", "1f7de256ff1743d329a8402ba0d132e7_95b6e8758533a9817b9f20a958e7b776", "4f399b8c526ffb6a2fd585a18d4ed5ec_51097231bc327c26c59a4fd8d3ff3069",
description: Binaries are hosted elsewhere for now, see https://huggingface.co/datasets/jordyvl/DUDE_loader/tree/main/data
DOI: 10.34740/kaggle/dsv/5221132
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.6848195
title: TrypTag: plate U5332 (replicate dated 20160527)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate U5332, replicate dated 20160527.
description: Fix missing _thr.tif files.