cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh2710061.08fu
title: SH2710061.08FU

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: 10.57745/5muh3e
title: 4d_91511-00-1.zip

description: 1H, 13C and 2D raw data relate to molecule 4d
DOI: 10.57745/ex5ycp
title: 6c_(S,E)-3-ethoxydec-4-enoic_acid.zip

description: 1H, 13C and 2D raw data relate to molecule 6c
DOI: 10.57745/msz4zm
title: 9b_19525-83-8.zip

description: 1H, 13C and 2D raw data relate to molecule 9b
DOI: 10.57745/qxrfg3
title: 9c_(R)-3-ethoxydecanoic_acid.zip

description: 1H, 13C and 2D raw data relate to molecule 9c
DOI: 10.57745/iyi7fq
title: 3b_91879-31-1.zip

description: 1H, 13C and 2D raw data relate to molecule 3b
DOI: 10.57745/8aurvf
title: 9a_19525-80-5.zip

description: 1H, 13C and 2D raw data relate to molecule 9a
DOI: 10.57745/dnc1jj
title: Iodo-Ac_78151-19-6.zip

description: 1H, 13C and 2D raw data relate to molecule Iodo-Ac
DOI: 10.57745/yj8foy
title: 2b_102719-10-8.zip

description: 1H, 13C and 2D raw data relate to molecule 2b
DOI: 10.57745/pjdsum
title: 6b_(S,E)-3-(benzyloxy)dec-4-enoic_acid.zip

description: 1H, 13C and 2D raw data relate to molecule 6b
DOI: 10.57745/rpqg7b
title: Iodo-Benzyl_(4S,5S)-4-(benzyloxy)-5-(iodomethyl)dihydrofuran-2(3H)-one.zip

description: 1H, 13C and 2D raw data relate to molecule Iodo-Benzyl
DOI: 10.57745/uxukw7
title: 8b_Benzyl-(S,E)-3-(benzyloxy)dec-4-enoate.zip

description: 1H, 13C and 2D raw data relate to molecule 8b
DOI: 10.57745/rga9x1
title: 3c_91879-39-9.zip

description: 1H, 13C and 2D raw data relate to molecule 3c
DOI: 10.57745/jdzdyf
title: 4e_(2R,3S)-3-(benzyloxy)-5-oxotetrahydrofuran-2-yl)methyl methanesulfonate.zip

description: 1H, 13C and 2D raw data relate to molecule 4e
DOI: 10.57745/e5owqv
title: 6d_(S,E)-3-acetoxydec-4-enoic_acid.zip

description: 1H, 13C and 2D raw data relate to molecule 6d
DOI: 10.57745/qhvive
title: Iodo-OH_82166-98-1.zip

description: 1H, 13C and 2D raw data relate to molecule Iodo-OH
DOI: 10.57745/kijhue
title: 3a_34371-14-7.zip

description: 1H, 13C and 2D raw data relate to molecule 3a
DOI: 10.57745/7zhrfz
title: 4a_91510-99-5.zip

description: 1H, 13C and 2D raw data relate to molecule 4a
DOI: 10.57745/otqez7
title: 4b_(2R,3S)-3-(benzyloxy)-5-oxotetrahydrofuran-2-yl)methyl 4-methylbenzenesulfonate.zip

description: 1H, 13C and 2D raw data relate to molecule 4b
DOI: 10.57745/gnhyqc
title: 7d_948911-57-7.zip

description: 1H, 13C and 2D raw data relate to molecule 7d
DOI: 10.57745/odpl9a
title: 2a_58238-45-2.zip

description: 1H, 13C and 2D raw data relate to molecule 2a
DOI: 10.57745/8cyuyn
title: 5a_38996-05-3.zip

description: 1H, 13C and 2D raw data relate to molecule 5a
DOI: 10.57745/lf6cer
title: Iodo-Ethyl_(4S,5S)-4-ethoxy-5-(iodomethyl)dihydrofuran-2(3H)-one.zip

description: 1H, 13C and 2D raw data relate to molecule Iodo-Ethyl
DOI: 10.6084/m9.figshare.24560476
title: SI_Data_03_genotypes.txt

description: Genotypes file from Albert et al. (2018)
DOI: 10.6084/m9.figshare.24560476.v1
title: SI_Data_03_genotypes.txt

description: Genotypes file from Albert et al. (2018)
DOI: 10.57745/znpvhu
title: 5b_1257872-89-1.zip

description: 1H, 13C and 2D raw data relate to molecule 5b
DOI: 10.57745/x6tunv
title: 5d_1648909-81-2.zip

description: 1H, 13C and 2D raw data relate to molecule 5d
DOI: 10.48544/e3b01dbb-b58d-4c4c-89f4-872538e1ecce
title: Why Answer this Question? Experts’ Behaviors on Educational Community Question-Answering Platforms
DOI: 10.48544/daae0844-c51d-4e7d-94a1-02e931b6e5bf
title: Perceived control decisions in preparation for a summative achievement test in higher education
DOI: 10.11583/dtu.24025596
title: Supplementary Dataset: "Polar ionospheric currents and high temporal resolution geomagnetic field models"

description: READMEData and model parameter files supplement the paper:<b>Title</b>: "Polar ionospheric currents and high temporal resolution geomagnetic field models"<b>Authors</b>: Kloss, C. and Finlay, C. and Laundal, K.M. and Olsen, N.<b>DOI</b>: 10.1093/gji/ggad325Supplementary software that investigates the divergence and curl of the estimated poloidal ionospheric field is available at https://doi.org/10.11583/DTU.24542374.Files DescriptionThe following files are available:Model-A_datafile.h5Reference.matModel-A.matModel-B.matreadme.txt<i>(1) Model-A_datafile.h5:</i>The file stores the processed magnetic data used to estimate our geomagnetic field model called Model-A. Data is organized as HDF-datasets within the HDF-group "vector". Each HDF-dataset comes with the attributes "UNITS", "DESCRIPTION" and "FORMAT", which provide additional information on the HDF-dataset.Python code example (using h5py Python package):&gt;&gt;&gt; import h5py as h5&gt;&gt;&gt; file = h5.File('Model-A_datafile.h5', 'r')&gt;&gt;&gt; print(list(file['vector'].keys())) # list all data variables&gt;&gt;&gt; print(file["vector/Time"].attrs["UNITS"].decode()) # units of "Time"&gt;&gt;&gt; file.close()References to the original databases:(i) Swarm and CryoSat2 data are available through ESA at https://swarm-diss.eo.esa.int/#.(ii) CHAMP data are available from GFZ Data Services at https://www.doi.org/10.5880/GFZ.2.3.2019.004.(iii) Ground magnetic observatory data from INTERMAGNET are available via the virtual research platform VirES https://vires.services/.(iv) RC index is available at http://www.spacecenter.dk/files/magnetic-models/RC/.(v) SML index is available at https://supermag.jhuapl.edu/info/.(vi) Solar wind speed, interplanetary magnetic field and Kp index are available through the GSFC/SPDF OMNIWeb interface at https://omniweb.gsfc.nasa.gov/ow.html.(vii) CHAOS-7 model and its updates are available at http://www.spacecenter.dk/files/magnetic-models/CHAOS-7/.<i>(2-4) Reference.mat (similarly for Model-A.mat, Model-B.mat):</i>Model parameter file of our estimated model in mat-format, following the style of the CHAOS model releases.Chaosmagpy (documentation, doi: https://doi.org/10.5281/zenodo.3352398 ) can be used to load the mat-formatted file and evaluate the non-ionospheric parts of the model. The ionospheric part of our model is unfortunately not supported at the moment. However, we have plans to make this possible in a future version of chaosmagpy.Python code example (using ChaosMagPy Python package):&gt;&gt;&gt; import chaosmagpy as cp&gt;&gt;&gt; model_a = cp.CHAOS.from_mat('Model-A.mat) # load model file&gt;&gt;&gt; B_int = model_a.synth_values_tdep(...) # evaluate time-dependent internal field<i>(5) Readme.txt:</i>Copy of this document.Changelog2023-11-10 Added link to supplementary software.2023-08-16 Initial preparation of this document and the files by Clemens Kloss.<br>
DOI: 10.11583/dtu.24025596.v1
title: Supplementary Material: "Polar ionospheric currents and high temporal resolution geomagnetic field models"

description: READMEData and model parameter files supplement the paper:<b>Title</b>: "Polar ionospheric currents and high temporal resolution geomagnetic field models"<b>Authors</b>: Kloss, C. and Finlay, C. and Laundal, K.M. and Olsen, N.<b>Doi</b>: 10.1093/gji/ggad325Files DescriptionThe following files are available:Model-A_datafile.h5Reference.matModel-A.matModel-B.matreadme.txt<i>(1) Model-A_datafile.h5:</i>The file stores the processed magnetic data used to estimate our geomagnetic field model called Model-A. Data is organized as HDF-datasets within the HDF-group "vector". Each HDF-dataset comes with the attributes "UNITS", "DESCRIPTION" and "FORMAT", which provide additional information on the HDF-dataset.Python code example (using h5py Python package):&gt;&gt;&gt; import h5py as h5&gt;&gt;&gt; file = h5.File('Model-A_datafile.h5', 'r')&gt;&gt;&gt; print(list(file['vector'].keys())) # list all data variables&gt;&gt;&gt; print(file["vector/Time"].attrs["UNITS"].decode()) # units of "Time"&gt;&gt;&gt; file.close()References to the original databases:(i) Swarm and CryoSat2 data are available through ESA at https://swarm-diss.eo.esa.int/#.(ii) CHAMP data are available from GFZ Data Services at https://www.doi.org/10.5880/GFZ.2.3.2019.004.(iii) Ground magnetic observatory data from INTERMAGNET are available via the virtual research platform VirES https://vires.services/.(iv) RC index is available at http://www.spacecenter.dk/files/magnetic-models/RC/.(v) SML index is available at https://supermag.jhuapl.edu/info/.(vi) Solar wind speed, interplanetary magnetic field and Kp index are available through the GSFC/SPDF OMNIWeb interface at https://omniweb.gsfc.nasa.gov/ow.html.(vii) CHAOS-7 model and its updates are available at http://www.spacecenter.dk/files/magnetic-models/CHAOS-7/.<i>(2-4) Reference.mat (similarly for Model-A.mat, Model-B.mat):</i>Model parameter file of our estimated model in mat-format, following the style of the CHAOS model releases.Chaosmagpy (documentation, doi: https://doi.org/10.5281/zenodo.3352398 ) can be used to load the mat-formatted file and evaluate the non-ionospheric parts of the model. The ionospheric part of our model is unfortunately not supported at the moment. However, we have plans to make this possible in a future version of chaosmagpy.Python code example (using ChaosMagPy Python package):&gt;&gt;&gt; import chaosmagpy as cp&gt;&gt;&gt; model_a = cp.CHAOS.from_mat('Model-A.mat) # load model file&gt;&gt;&gt; B_int = model_a.synth_values_tdep(...) # evaluate time-dependent internal field<i>(5) Readme.txt:</i>Copy of this document.Changelog2023-08-16 Initial preparation of this document and the files by Clemens Kloss.<br>
DOI: 10.11583/dtu.24025596.v2
title: Supplementary Dataset: "Polar ionospheric currents and high temporal resolution geomagnetic field models"

description: READMEData and model parameter files supplement the paper:<b>Title</b>: "Polar ionospheric currents and high temporal resolution geomagnetic field models"<b>Authors</b>: Kloss, C. and Finlay, C. and Laundal, K.M. and Olsen, N.<b>DOI</b>: 10.1093/gji/ggad325Supplementary software that investigates the divergence and curl of the estimated poloidal ionospheric field is available at https://doi.org/10.11583/DTU.24542374.Files DescriptionThe following files are available:Model-A_datafile.h5Reference.matModel-A.matModel-B.matreadme.txt<i>(1) Model-A_datafile.h5:</i>The file stores the processed magnetic data used to estimate our geomagnetic field model called Model-A. Data is organized as HDF-datasets within the HDF-group "vector". Each HDF-dataset comes with the attributes "UNITS", "DESCRIPTION" and "FORMAT", which provide additional information on the HDF-dataset.Python code example (using h5py Python package):&gt;&gt;&gt; import h5py as h5&gt;&gt;&gt; file = h5.File('Model-A_datafile.h5', 'r')&gt;&gt;&gt; print(list(file['vector'].keys())) # list all data variables&gt;&gt;&gt; print(file["vector/Time"].attrs["UNITS"].decode()) # units of "Time"&gt;&gt;&gt; file.close()References to the original databases:(i) Swarm and CryoSat2 data are available through ESA at https://swarm-diss.eo.esa.int/#.(ii) CHAMP data are available from GFZ Data Services at https://www.doi.org/10.5880/GFZ.2.3.2019.004.(iii) Ground magnetic observatory data from INTERMAGNET are available via the virtual research platform VirES https://vires.services/.(iv) RC index is available at http://www.spacecenter.dk/files/magnetic-models/RC/.(v) SML index is available at https://supermag.jhuapl.edu/info/.(vi) Solar wind speed, interplanetary magnetic field and Kp index are available through the GSFC/SPDF OMNIWeb interface at https://omniweb.gsfc.nasa.gov/ow.html.(vii) CHAOS-7 model and its updates are available at http://www.spacecenter.dk/files/magnetic-models/CHAOS-7/.<i>(2-4) Reference.mat (similarly for Model-A.mat, Model-B.mat):</i>Model parameter file of our estimated model in mat-format, following the style of the CHAOS model releases.Chaosmagpy (documentation, doi: https://doi.org/10.5281/zenodo.3352398 ) can be used to load the mat-formatted file and evaluate the non-ionospheric parts of the model. The ionospheric part of our model is unfortunately not supported at the moment. However, we have plans to make this possible in a future version of chaosmagpy.Python code example (using ChaosMagPy Python package):&gt;&gt;&gt; import chaosmagpy as cp&gt;&gt;&gt; model_a = cp.CHAOS.from_mat('Model-A.mat) # load model file&gt;&gt;&gt; B_int = model_a.synth_values_tdep(...) # evaluate time-dependent internal field<i>(5) Readme.txt:</i>Copy of this document.Changelog2023-11-10 Added link to supplementary software.2023-08-16 Initial preparation of this document and the files by Clemens Kloss.<br>
DOI: 10.57754/fdat.d8bav-5sk43
title: Tübingen Dependency-Parsed German Wikipedia Treebank

description: TüBa-D/W is a large treebank of modern written german, that follows common annotations standards and is freely available under a permissive license. The treebank is based on Wikipedia text and consists of 36.1 million sentences (615 million tokens) in CONLL-X format. The annotation layers are: part-of-speech tags, morphology, lemmas, and dependency structure.
DOI: 10.5281/zenodo.10142228
title: Data used in Hidalgo et a. 2024 GEB

description: This dataset includes all stability metrics, diversity indicators and covariates used in the manuscript entitled  "Diversity, stability and ecosystem functioning relationships across marine fish communities facing contrasting environmental and anthropogenic pressures"
DOI: 10.5281/zenodo.10142166
title: Highly heterogeneous mycobiomes shape fungal diversity in two globally distributed lichens

description: Premise — Lichens are multi-kingdom symbioses in which fungi, algae and bacteria interact to develop a stable selection unit. In addition to the mycobiont forming the symbiosis, fungal communities associated with lichens represent the lichen mycobiome, but their taxonomic and functional diversity across the range of their host lichens is still largely unknown. We aim to characterize the diversity of the lichen mycobiome in two cosmopolitan lichens, i.e. Rhizoplaca melanophthalma and Tephromela atra as study models.


Methods — The mycobiomes were investigated across a broad range of their distribution using both a culture-dependent approach and environmental DNA.


Key results — The variation of the mycobiomes associated with the two lichen species is extremely high, and a stable species-specific core mycobiome is hardly identifiable. Most of the mycobiome taxa are present in a low fraction of the samples. No taxon is ubiquitously present in neither lichen species. The mycobiomes of R. melanophthalma and T. atra are thus composed of heterogenous lichenicolous fungi. A fraction of these mycobiomes is also generalist, as it associates with both lichens. A fraction of the two lichen mycobiomes is detectable only by the culture-dependent approach and escapes PCR amplification.


Conclusions — Lichens rather are reservoirs of other fungi that choose the thalli as own ecological niche to thrive in more extreme habitats..
DOI: 10.5281/zenodo.7852084
title: Highly heterogeneous mycobiomes shape fungal diversity in two globally distributed lichens

description: Premise — Lichens are multi-kingdom symbioses in which fungi, algae and bacteria interact to develop a stable selection unit. In addition to the mycobiont forming the symbiosis, fungal communities associated with lichens represent the lichen mycobiome, but their taxonomic and functional diversity across the range of their host lichens is still largely unknown. We aim to characterize the diversity of the lichen mycobiome in two cosmopolitan lichens, i.e. Rhizoplaca melanophthalma and Tephromela atra as study models.


Methods — The mycobiomes were investigated across a broad range of their distribution using both a culture-dependent approach and environmental DNA.


Key results — The variation of the mycobiomes associated with the two lichen species is extremely high, and a stable species-specific core mycobiome is hardly identifiable. Most of the mycobiome taxa are present in a low fraction of the samples. No taxon is ubiquitously present in neither lichen species. The mycobiomes of R. melanophthalma and T. atra are thus composed of heterogenous lichenicolous fungi. A fraction of these mycobiomes is also generalist, as it associates with both lichens. A fraction of the two lichen mycobiomes is detectable only by the culture-dependent approach and escapes PCR amplification.


Conclusions — Lichens rather are reservoirs of other fungi that choose the thalli as own ecological niche to thrive in more extreme habitats..
DOI: 10.5281/zenodo.7852085
title: Highly heterogeneous mycobiomes shape fungal diversity in two globally distributed lichens

description: <strong>Premise</strong> <em>— </em>Lichens are multi-kingdom symbioses in which fungi, algae and bacteria interact to develop a stable selection unit. In addition to the mycobiont forming the symbiosis, fungal communities associated with lichens represent the lichen mycobiome, but their taxonomic and functional diversity across the range of their host lichens is still largely unknown. We aim to characterize the diversity of the lichen mycobiome in two cosmopolitan lichens, i.e. <em>Rhizoplaca melanophthalma</em> and <em>Tephromela atra</em> as study models. <strong>Methods</strong> <em>—</em> The mycobiomes were investigated across a broad range of their distribution using both a culture-dependent approach and environmental DNA. <strong>Key results</strong> <em>— </em>The variation of the mycobiomes associated with the two lichen species is extremely high, and a stable species-specific core mycobiome is hardly identifiable. Most of the mycobiome taxa are present in a low fraction of the samples. No taxon is ubiquitously present in neither lichen species. The mycobiomes of <em>R. melanophthalma</em> and <em>T. atra</em> are thus composed of heterogenous lichenicolous fungi. A fraction of these mycobiomes is also generalist, as it associates with both lichens. A fraction of the two lichen mycobiomes is detectable only by the culture-dependent approach and escapes PCR amplification. <strong>Conclusions</strong> <em>— </em>Lichens rather are reservoirs of other fungi that choose the thalli as own ecological niche to thrive in more extreme habitats..
DOI: 10.5281/zenodo.8042795
title: Highly heterogeneous mycobiomes shape fungal diversity in two globally distributed lichens

description: <strong>Premise</strong> <em>— </em>Lichens are multi-kingdom symbioses in which fungi, algae and bacteria interact to develop a stable selection unit. In addition to the mycobiont forming the symbiosis, fungal communities associated with lichens represent the lichen mycobiome, but their taxonomic and functional diversity across the range of their host lichens is still largely unknown. We aim to characterize the diversity of the lichen mycobiome in two cosmopolitan lichens, i.e. <em>Rhizoplaca melanophthalma</em> and <em>Tephromela atra</em> as study models. <strong>Methods</strong> <em>—</em> The mycobiomes were investigated across a broad range of their distribution using both a culture-dependent approach and environmental DNA. <strong>Key results</strong> <em>— </em>The variation of the mycobiomes associated with the two lichen species is extremely high, and a stable species-specific core mycobiome is hardly identifiable. Most of the mycobiome taxa are present in a low fraction of the samples. No taxon is ubiquitously present in neither lichen species. The mycobiomes of <em>R. melanophthalma</em> and <em>T. atra</em> are thus composed of heterogenous lichenicolous fungi. A fraction of these mycobiomes is also generalist, as it associates with both lichens. A fraction of the two lichen mycobiomes is detectable only by the culture-dependent approach and escapes PCR amplification. <strong>Conclusions</strong> <em>— </em>Lichens rather are reservoirs of other fungi that choose the thalli as own ecological niche to thrive in more extreme habitats..
DOI: 10.5281/zenodo.10142227
title: Data used in Hidalgo et a. 2024 GEB

description: This dataset includes all stability metrics, diversity indicators and covariates used in the manuscript entitled  "Diversity, stability and ecosystem functioning relationships across marine fish communities facing contrasting environmental and anthropogenic pressures"
DOI: 10.57754/fdat.akjnj-nc030
title: TüBa-DDC - Tübinger Baumbank des Deutschen - Diachrones Corpus
title: TüBa-DDC - Tübingen Treebank of Written German - Diachronic Corpus

description: Creation tools: OpenNLP Tokenizer, TCF 0.3 Sentence Border Detecter (in-house tool for detecting sentence borders), Part-of-speech tagger and lemmatiser, Berkeley Parser (constituent parser), and German Named Entity Recognizer.
Source: Gutenberg-DE Edition 11 DVD-ROM
DOI: 10.5281/zenodo.8200396
title: Correlation between LDH/PDH activities ratio and tissue pH in the perfused mouse heart – a potential non-invasive indicator of cardiac pH provided by hyperpolarized magnetic resonance

description: Primary data for DOI: 10.1002/nbm.4444


NMR in Biomedicine. 2021;34:e4444.


Title: Correlation between LDH/PDH activities ratio and tissue pH in the perfused mouse heart – a potential non-invasive indicator of cardiac pH provided by hyperpolarized magnetic resonance


Authors: David Shaul, Assad Azar, Gal Sapir, Sivaranjan Uppala, Atara Nardi-Schreiber, Ayelet Gamliel, Jacob Sosna, J. Moshe Gomori, and Rachel Katz-Brull


 


These primary datasets contain data presented in the above publication and consist of:


1. 31P-NMR spectra


2. Hyperpolarized 13C-NMR spectra


Please consult the Archive Guide.
DOI: 10.5281/zenodo.8200397
title: Correlation between LDH/PDH activities ratio and tissue pH in the perfused mouse heart – a potential non-invasive indicator of cardiac pH provided by hyperpolarized magnetic resonance

description: Primary data for DOI: 10.1002/nbm.4444


NMR in Biomedicine. 2021;34:e4444.


Title: Correlation between LDH/PDH activities ratio and tissue pH in the perfused mouse heart – a potential non-invasive indicator of cardiac pH provided by hyperpolarized magnetic resonance


Authors: David Shaul, Assad Azar, Gal Sapir, Sivaranjan Uppala, Atara Nardi-Schreiber, Ayelet Gamliel, Jacob Sosna, J. Moshe Gomori, and Rachel Katz-Brull


 


These primary datasets contain data presented in the above publication and consist of:


1. 31P-NMR spectra


2. Hyperpolarized 13C-NMR spectra


Please consult the Archive Guide.
DOI: 10.5281/zenodo.7436678
title: Dataset for the publication "Impact of DC Transient Disturbances on Harmonic Performance of Voltage Transformers for AC Railway Applications"

description: This is dataset for paper published:


Letizia, P.S.; Signorino, D.; Crotti, G. Impact of DC Transient Disturbances on Harmonic Performance of Voltage Transformers for AC Railway Applications. Sensors 2022, 22, 2270. https://doi.org/10.3390/s22062270
description: "This project 19NRM05 IT4PQ has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme". Funder name: European Metrology Programme for Innovation and Research (EMPIR) Funder ID: 10.13039/100014132