cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/81ps-bx03
title: N-BGP (Noninvasive Blood Group Prediction Dataset)

description: This Dataset used a non-invasive blood group prediction approach using deep learning. Rapid and meticulous prediction of blood type is a major step during medical emergency before supervising the red blood cell, platelet, and plasma transfusion. Any small mistake during transfer of blood can cause death. In conventional pathological assessment, the blood test is conducted using automated blood analyser; however, it results into time taking process. In typical pathological blood test, when blood sample is collected by pricking the skin, it may originate bleeding, lead to fainting and can cause skin laceration at certain part of body. The proposed deep learning approach is a non-invasive without perforating the skin that automatically predict the human blood type by applying deep learning algorithms to the captured images of superficial blood vessels present on finger. As laser light passes, the optical image of blood vessels hidden on the finger skin surface area is captured, which incorporates specific antigen shapes such as antigen ‘A’ and antigen ‘B’ present on the surface. Captured shapes of different antigen further used to predict the blood group of humans. The system requires high-definition camera to capture the antigen pattern from the red blood cells surface for classification of blood type without piercing the skin of patient. The proposed solution is easy to implement, straightforward and significant to identify ABO blood group instantly. It provides cost effective solution for identifying blood group in case of medical emergencies, battleground and more useful for infants.
DOI: 10.5281/zenodo.8089417
title: Dataset for Evaluation of a novel microfluidic chip-like device for purifying bovine frozen-thawed semen for in vitro fertilization

description: VetCount<sup>TM</sup> Harvester (MotilityCount ApS, Copenhagen, Denmark) is a novel sperm<br> purification device. It consists of two chambers separated by a 10 μM microporous<br> membrane. Untreated semen is applied in one chamber, sperm collection medium<br> in the other. Motile sperm cells are selected by actively swimming through the<br> membrane pores into the medium containing chamber. After 30 min incubation,<br> the sperm collection medium can be aspirated and the purified sperm is ready<br> for further use.<br> In a first experiment we assessed sperm quality and recovery of frozen-thawed semen<br> from six different bulls (n = 6) prior to and after purification with the<br> VetCount<sup>TM</sup> Harvester or BoviPure<sup>TM</sup> gradient centrifugation, a commercial available standard technique. In a second approach, a competitive fertilization assay was performed. Ten straws per bull were pooled, split<br> in two subsamples, and simultaneously purified either with the VetCount<sup>TM</sup> Harvester<br> or BoviPure<sup>TM</sup> gradient centrifugation. Following purification, sperm cells<br> from each treatment group were fluorescently labeled with either MitoTracker<sup>TM</sup><br> Red FM or MitoTracker<sup>TM</sup> Green FM. <em>In vitro</em> matured oocytes were inseminated with<br> equal numbers of red and green labeled sperm. Eighteen hours after fertilization,<br> fluorescence microscopy was used to determine the origin of the fertilizing spermatozoon.
DOI: 10.5281/zenodo.8089418
title: Dataset for Evaluation of a novel microfluidic chip-like device for purifying bovine frozen-thawed semen for in vitro fertilization

description: VetCount<sup>TM</sup> Harvester (MotilityCount ApS, Copenhagen, Denmark) is a novel sperm<br> purification device. It consists of two chambers separated by a 10 μM microporous<br> membrane. Untreated semen is applied in one chamber, sperm collection medium<br> in the other. Motile sperm cells are selected by actively swimming through the<br> membrane pores into the medium containing chamber. After 30 min incubation,<br> the sperm collection medium can be aspirated and the purified sperm is ready<br> for further use.<br> In a first experiment we assessed sperm quality and recovery of frozen-thawed semen<br> from six different bulls (n = 6) prior to and after purification with the<br> VetCount<sup>TM</sup> Harvester or BoviPure<sup>TM</sup> gradient centrifugation, a commercial available standard technique. In a second approach, a competitive fertilization assay was performed. Ten straws per bull were pooled, split<br> in two subsamples, and simultaneously purified either with the VetCount<sup>TM</sup> Harvester<br> or BoviPure<sup>TM</sup> gradient centrifugation. Following purification, sperm cells<br> from each treatment group were fluorescently labeled with either MitoTracker<sup>TM</sup><br> Red FM or MitoTracker<sup>TM</sup> Green FM. <em>In vitro</em> matured oocytes were inseminated with<br> equal numbers of red and green labeled sperm. Eighteen hours after fertilization,<br> fluorescence microscopy was used to determine the origin of the fertilizing spermatozoon.
DOI: 10.5281/zenodo.7892951
title: SMART_BARN

description: The dataset is collected in conjunction with the publication named smart-barn. The data is in two main parts. One is the case studies for reproducing results and another case is sample data to run the code (see below) to fulfill some functionalities or post-processing. Relevant Code: https://zenodo.org/record/7890239 - Audio processing https://zenodo.org/record/7890386 - Head eye calibration for pigeon Gaze https://zenodo.org/record/7890292 - Mocap data processing (annotation, keypoint, 3D data manipulation etc.)
DOI: 10.5281/zenodo.7892950
title: SMART_BARN

description: The dataset is collected in conjunction with the publication named smart-barn. The data is in two main parts. One is the case studies for reproducing results and another case is sample data to run the code (see below) to fulfill some functionalities or post-processing. Relevant Code: https://zenodo.org/record/7890239 - Audio processing https://zenodo.org/record/7890386 - Head eye calibration for pigeon Gaze https://zenodo.org/record/7890292 - Mocap data processing (annotation, keypoint, 3D data manipulation etc.)
DOI: 10.6084/m9.figshare.23607345.v2
title: Testing the algorithm under different cases for IFAJSS-DRC

description: These data are used to compare the performance of the algorithms on different problems
DOI: 10.17031/6481acb1ef340
title: 1996 Scottish Environment Protection Agency (SEPA) Dipple Shore Survey
title: 1996 SEPA Dipple Shore Survey

description: Dipple shore survey conducted in February 1996 in a survey of 14 sites and September 1996 in a survey of 15 sites.
DOI: 10.5281/zenodo.8116241
title: Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves

description: Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves
DOI: 10.5281/zenodo.8116240
title: Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves

description: Single-cell RNA-seq dataset to determine cell-type specific response to fungal pathogen infection in plant leaves
DOI: 10.21252/vhwf-ta02
title: Dataset for Everett, J., Brooks, J., Lermyte, F., Tjhin, V.T., Hands-Portman, I., Hill, E., Collingwood, J.F. & Telling, N.D. 2023, "Illuminating the brain: Revealing brain biochemistry with synchrotron X-ray spectromicroscopy", Journal of Electron Spectroscopy and Related Phenomena, vol. 266, pp. 147355.
DOI: 10.5281/zenodo.3757094
title: Chinese transcriptions of Indic terms in the translations of Ān Shìgāo 安世高 and Lokakṣema 支婁迦讖

description: This dataset includes the Chinese transcriptions of Indic terms in the translations of Ān Shìgāo 安世高 and Lokakṣema 支婁迦讖. The coverage of of Ān Shìgāo complete, except for the two Kongōji manuscripts not included in the Taishō canon. The coverage of Lokakṣema is less systematic, including what is indexed by Karashima (2010) from the <em><em>Aṣṭasāhasrikā Prajñāpāramitā</em></em> and what was included in Coblin's (1983) study. Coblin, W. South (1983). <em>A handbook of Eastern Han sound glosses</em>. Hong Kong: Chinese University Press. Karashima, Seishi (2010). <em>A glossary of Lokakṣema's translation of the Aṣṭasāhasrikā Prajñāpāramit</em>ā. (Bibliotheca philologica et philosophica buddhica 11). Tokyo : International Research Institute for Advanced Buddhology, Soka University, 2010.
description: {"references": ["Coblin, W. South (1983). A handbook of Eastern Han sound glosses. Hong Kong: Chinese University Press.", "Karashima, Seishi (2010). A glossary of Lokak\u1e63ema's translation of the A\u1e63\u1e6das\u0101hasrik\u0101 Praj\u00f1\u0101p\u0101ramit\u0101. (Bibliotheca philologica et philosophica buddhica 11). Tokyo : International Research Institute for Advanced Buddhology, Soka University, 2010."]}
DOI: 10.5281/zenodo.3757095
title: Chinese transcriptions of Indic terms in the translations of Ān Shìgāo 安世高 and Lokakṣema 支婁迦讖

description: This dataset includes the Chinese transcriptions of Indic terms in the translations of Ān Shìgāo 安世高 and Lokakṣema 支婁迦讖. The coverage of of Ān Shìgāo complete, except for the two Kongōji manuscripts not included in the Taishō canon. The coverage of Lokakṣema is less systematic, including what is indexed by Karashima (2010) from the <em><em>Aṣṭasāhasrikā Prajñāpāramitā</em></em> and what was included in Coblin's (1983) study. Coblin, W. South (1983). <em>A handbook of Eastern Han sound glosses</em>. Hong Kong: Chinese University Press. Karashima, Seishi (2010). <em>A glossary of Lokakṣema's translation of the Aṣṭasāhasrikā Prajñāpāramit</em>ā. (Bibliotheca philologica et philosophica buddhica 11). Tokyo : International Research Institute for Advanced Buddhology, Soka University, 2010.
description: {"references": ["Coblin, W. South (1983). A handbook of Eastern Han sound glosses. Hong Kong: Chinese University Press.", "Karashima, Seishi (2010). A glossary of Lokak\u1e63ema's translation of the A\u1e63\u1e6das\u0101hasrik\u0101 Praj\u00f1\u0101p\u0101ramit\u0101. (Bibliotheca philologica et philosophica buddhica 11). Tokyo : International Research Institute for Advanced Buddhology, Soka University, 2010."]}
DOI: 10.57745/6plxhu
title: TGA_OL_Pine_2018.csv

description: This file contains the TGA thermogram in format .CSV of the OL_Pine_2018 lignin sample.
DOI: 10.57745/tbuqw9
title: TGA_OL_Switchgrass_2018.csv

description: This file contains the TGA thermogram in format .CSV of the OL_Switchgrass_2018 lignin sample.
DOI: 10.57745/y72y5w
title: TGA_OL_Wheat_2016.csv

description: This file contains the TGA thermogram in format .CSV of the OL_Wheat_2016 lignin sample.
DOI: 10.57745/xngtlg
title: TGA_OL_Miscanthus_2016.csv

description: This file contains the TGA thermogram in format .CSV of the OL_Miscanthus_2016 lignin sample.
DOI: 10.57745/6vdqyz
title: TGA_OL_Flax_2020-01.csv

description: This file contains the TGA thermogram in format .CSV of the OL_Flax_2020-01 lignin sample.
DOI: 10.57745/jhcmjj
title: TGA_OL_Method.pdf

description: This file describes the method used to determine the thermal behaviour of the lab-extracted organosolv lignin using thermogravimetric analysis.
DOI: 10.57745/gmjmvq
title: TGA_OL_Flax_2020-02.csv

description: This file contains the TGA thermogram in format .CSV of the OL_Flax_2020-02 lignin sample.
DOI: 10.57745/7xonqy
title: TGA_OL_Results.csv

description: This file contains the temperatures corresponding to specific weight loss percentages (at 5%, 10%, 50% after water content correction) for all lab-extracted organosolv lignins.
DOI: 10.57745/2j2ddz
title: TGA_OL_Poplar_2016.csv

description: This file contains the TGA thermogram in format .CSV of the OL_Poplar_2016 lignin sample.
DOI: 10.23708/xggbea
title: South Asian maps from: LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa

description: The dataset contains forest aboveground biomass prediction maps derived from field inventory plot and UAV or airborne LiDAR data over five sites in South Asia, together with prediction uncertainty maps. Maps are provided at 100 x 100 m and 40 x 40 m spatial resolution. The dataset is associated to the following publication, where details on underlying data and map generation process can be found: Suraj R. Rodda et al., LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa. Submitted to Scientific Data.
DOI: 10.5517/ccdc.csd.cc2fglj1
title: CCDC 2248353: Experimental Crystal Structure Determination

description: Related Article: li cheng|2023|CSD Communication|||
description: Related Article: Cheng Li, Bin Zhao, Guojiang Mao, Guo-Jun Deng|2023|Chem.Commun.|59|7044|doi:10.1039/D3CC01940C
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: 10.5517/ccdc.csd.cc2fglwd
title: CCDC 2248364: Experimental Crystal Structure Determination

description: Related Article: Eleanor Dodd, Simon J. Coles, Terry L. Threlfall|2023|CSD Communication|||
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: 10.5517/ccdc.csd.cc2fglzh
title: CCDC 2248367: Experimental Crystal Structure Determination

description: Related Article: Eleanor Dodd, Simon J. Coles, Terry L. Threlfall, Philip Adler|2023|CSD Communication|||
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: 10.5517/ccdc.csd.cc2fgwxq
title: CCDC 2248644: Experimental Crystal Structure Determination

description: Related Article: Eleanor Dodd, Simon J. Coles, Terry L. Threlfall, Philip Adler|2023|CSD Communication|||
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: 10.5281/zenodo.7002917
title: Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based cathodes used in CO2 electrolysers"

description: Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based CO2 electrolysers", published in Journal of Materials Chemistry A 2023 11:5083–5094, doi: 10.1039/D2TA06965B Folder names describe the type of data content.
DOI: 10.5281/zenodo.7002916
title: Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based cathodes used in CO2 electrolysers"

description: Raw data for the article "Effective perspiration is essential to uphold the stability of zero-gap MEA-based CO2 electrolysers", published in Journal of Materials Chemistry A 2023 11:5083–5094, doi: 10.1039/D2TA06965B Folder names describe the type of data content.
DOI: 10.4119/unibi/2704060
title: Supplementary Material for "Sonification for EEG Data Analysis"

description: This paper presents techniques to render acoustic representations for EEG data. In our case, data are obtained from psycholinguistic experiments where subjects are exposed to three different conditions based on different auditory stimuli. The goal of this research is to uncover elements of neural processing correlated with high-level cognitive activity. Three sonifications are presented within this paper: spectral mapping sonification which offers a quite direct inspection of the recorded data, distance matrix sonification which allows to detect nonlinear long range correlations at high time resolution, and differential sonification which summarizes the comparison of EEG measurements under different conditions for each subject. This paper describes the techniques and presents sonification examples for experimental data. #### Sound Demonstrations for the presented EEG data sonification techniques * Table 1: <br> Audification of EEG data (2 electrodes, 3 subjects and all 3 conditions). Besides the dominating noise, pitched spikes can be heard File/Track: S1:: EEGr: Fp1, T5, Fp1/T5, speech: Fp1, T5, Fp1/T5 , pseudo: Fp1/T5<br> S2:: EEGr: Fp1, T5, Fp1/T5, speech: Fp1, T5, Fp1/T5 , pseudo: Fp1/T5<br> S3:: EEGr: Fp1, T5, Fp1/T5, speech: Fp1, T5, Fp1/T5 , pseudo: Fp1/T5 Description: Audification of EEG Data for two electrodes (Fp1 and T5) for subjects S1, S2 and S3 Duration: 1 s * Table 2: <br> Sound examples for Spectral Mapping Sonification of EEG data, for pseudo speech and speech condition in a sequence with a short gap in between of 2.5 s in real-time. File/Track: delta=0.4, [0-30Hz], left: T3,T5, right: T4,T6 <br> S1 S2 S3 S4 S5 S6 <br> delta=0, [0-30Hz], left: T3,T5, right: T4,T6 <br> S1 S2 S3 S4 S5 S6<br> delta=0, [0-30Hz], left: F7,F3,T3,C3,T5,P3, right: F4,F8,C4,T4,T6,P4<br> S1 S2 S3 S4 S5 S6 Description: Spectral Mapping Sonification of EEG data Duration: about 4 s * Table 3: <br> Sound examples for Distance Matrix Sonification of spectral vectors. High-pitched loud tones indicate long-range frequency selective couplings File/Track: frequency range 8-20 Hz, subjects: S1 S2 S3 S4 S5 S6 Description: Distance Matrix Sonification for concatenated datasets for the pseudospeech and speech condition. A noise burst separates the two parts Duration: 5 s * Sound Example: [Ex-DiffSon-1](https://pub.uni-bielefeld.de/download/2704060/2704108), Differential Sonification for Example shown as spectrogram <br> * Table 4: <br> Sound examples for Differential Sonifications File/Track: pseudospeech-speech (1s): <br> S1, S2, S3, S4, S5, S6 <br> same with higher threshold (1s): <br> S1, S2, S3, S4, S5, S6 <br> speech - EEGr (1s): <br> S1, S2, S3, S4, S5, S6 <br> pseudospeech-speech (4s): <br> S1, S2, S3, S4, S4, S6 <br> Description: Differential Sonification of EEG data Duration: 1 s and 4 s
DOI: 10.5281/zenodo.7117080
title: Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison''

description: Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison'', published in Applied Catalysis B: Environmental 2023 335:122885, doi: 10.1016/j.apcatb.2023.122885 Folder names describe the type of data content.
DOI: 10.5281/zenodo.7117079
title: Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison''

description: Raw data for the article "The role of ionomers in the electrolyte management of zero-gap MEA-based CO2 electrolysers: A Fumion vs. Nafion comparison'', published in Applied Catalysis B: Environmental 2023 335:122885, doi: 10.1016/j.apcatb.2023.122885 Folder names describe the type of data content.
DOI: 10.5281/zenodo.5578921
title: MASCDB: a database of images, descriptors and1microphysical properties of individual snowflakes in free fall

description: <strong>Dataset overview</strong> This dataset provides data and images of snowflakes in free fall collected with a Multi-Angle Snowflake Camera (MASC) The dataset includes, for each recorded snowflakes: A triplet of gray-scale images corresponding to the three cameras of the MASC A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement. The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example: Textural and geometrical descriptors as in <em>Praz et al 2017</em> Hydrometeor classification, riming degree estimation, melting identification, as in <em>Praz et al 2017</em> Blowing snow identification, as in <em>Schaer et al 2020 </em> Mass, volume, gyration estimation<em>, as in Leinonen et al 2021</em> <strong>Data format and structure</strong> The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available here. <strong>Supporting code</strong> A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on GitHub. See also the code documentation on ReadTheDocs. <strong>Download notes</strong> All files available here for download should be stored in the same folder, if the python-based API is used <em>MASCdb.zarr.zip</em> must be unzipped after download
DOI: 10.5281/zenodo.5709832
title: MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall

description: <strong>Dataset overview</strong> This dataset provides data and images of snowflakes in free fall collected with a Multi-Angle Snowflake Camera (MASC) The dataset includes, for each recorded snowflakes: A triplet of gray-scale images corresponding to the three cameras of the MASC A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement. The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example: Textural and geometrical descriptors as in <em>Praz et al 2017</em> Hydrometeor classification, riming degree estimation, melting identification, as in <em>Praz et al 2017</em> Blowing snow identification, as in <em>Schaer et al 2020 </em> Mass, volume, gyration estimation<em>, as in Leinonen et al 2021</em> <strong>Data format and structure</strong> The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available here. <strong>Supporting code</strong> A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on GitHub. See also the code documentation on ReadTheDocs. <strong>Download notes</strong> All files available here for download should be stored in the same folder, if the python-based API is used <em>MASCdb.zarr.zip</em> must be unzipped after download