cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4783271
title: Original data for: Microplastic concentrations, size distribution, and polymer types in the surface waters of a northern European lake

description: <strong>Description of the data and files</strong>: The data was created in 2018 and opened in 2021. Dataset is an Excel table file <em>MpKallavesiMIF_rawdata.xlsx</em>. The first tab “Sampling” contains coordinates, where the samples were collected and volumes of water per samples. The second tab “Manta FTIR Results” contains light microscopy and FTIR microscopy measurement data from all particles found from the Manta net samples. Similarly, the third tab “Pump FTIR results” contains light microscopy and FTIR microscopy measurement data for particles from pumped samples. The fourth tab “Controls” contains light microscopy and FTIR microscopy measurement data for control samples. The fifth tab “Materials” contains statistics for the shapes (fibers/fragments) and polymer types of the microplastics. Please see the related publication for more details about the methods and results. <strong>Creation and processing of the data</strong>: Data is created by examining the appearance and chemical composition of the suspected microplastic particles by light microscopy and FTIR microscopy. Correlation coefficients have been calculated between the measured FTIR spectra and a reference library of plastics. In addition to the raw data, the file contains statistics calculated from the raw numbers. <strong>Purpose and use of the data</strong>: Data is published under Creative Commons CC-BY 4.0 license. Data may be useful for comparisons between datasets and meta-analyses of microplastic concentrations in various environments.
DOI: 10.5281/zenodo.4783270
title: Original data for: Microplastic concentrations, size distribution, and polymer types in the surface waters of a northern European lake

description: <strong>Description of the data and files</strong>: The data was created in 2018 and opened in 2021. Dataset is an Excel table file <em>MpKallavesiMIF_rawdata.xlsx</em>. The first tab “Sampling” contains coordinates, where the samples were collected and volumes of water per samples. The second tab “Manta FTIR Results” contains light microscopy and FTIR microscopy measurement data from all particles found from the Manta net samples. Similarly, the third tab “Pump FTIR results” contains light microscopy and FTIR microscopy measurement data for particles from pumped samples. The fourth tab “Controls” contains light microscopy and FTIR microscopy measurement data for control samples. The fifth tab “Materials” contains statistics for the shapes (fibers/fragments) and polymer types of the microplastics. Please see the related publication for more details about the methods and results. <strong>Creation and processing of the data</strong>: Data is created by examining the appearance and chemical composition of the suspected microplastic particles by light microscopy and FTIR microscopy. Correlation coefficients have been calculated between the measured FTIR spectra and a reference library of plastics. In addition to the raw data, the file contains statistics calculated from the raw numbers. <strong>Purpose and use of the data</strong>: Data is published under Creative Commons CC-BY 4.0 license. Data may be useful for comparisons between datasets and meta-analyses of microplastic concentrations in various environments.
DOI: 10.6084/m9.figshare.14680638
title: Non-AMP ID table

description: Original and custom ID codes for the used non-AMP amino acid sequences.
DOI: 10.6084/m9.figshare.14680638.v1
title: Non-AMP ID table

description: Original and custom ID codes for the used non-AMP amino acid sequences.
DOI: 10.15468/vvtcss
title: Two new species of Neolindus from Peru and Venezuela (Coleoptera: Staphylinidae: Paederinae: Cylindroxystina)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Volker, A. (2012): Two new species of Neolindus from Peru and Venezuela (Coleoptera: Staphylinidae: Paederinae: Cylindroxystina). Beiträge Zur Entomologie = Contributions to Entomology 62 (6): 291-297, DOI: 10.21248/contrib.entomol.62.2.291-297, URL: http://dx.doi.org/10.21248/contrib.entomol.62.2.291-297
DOI: 10.5281/zenodo.4772532
title: QReCC - Question Rewriting in Conversational Context

description: QReCC contains 14K conversations with 81K question-answer pairs and a collection of 54M passages. See the README for more information.
DOI: 10.25345/c55v5k
title: MassIVE MSV000087520 - ATG9A interactome using BioID proximity labeling
title: Quantitative LC/MS/MS was performed on 2 uL of each sample, using a nanoAcquity UPLC system (Waters Corp) coupled to a Thermo Orbitrap Fusion Lumos high resolution accurate mass tandem mass spectrometer (Thermo) equipied with a FAIMS device via a nanoelectrospray ionization source. Briefly, the sample was first trapped on a Symmetry C18 20 mm x 180 um trapping column (5 ul/min at 99.9/0.1 v/v water/ac etonitrile), after which the analytical separation was performed using a 1.8 um Acquity HSS T3 C18 75 um x 250 mm column (Waters Corp.) with a 90-min linear gradient of 5 to 30% acetonitrile with 0.1% formic acid at a flow rate of 400 nanoliters/minute (nL/min) with a column temperature of 55C. Data collection on the Fusion Lumos mass spectrometer was performed for three difference compensation voltages (-40, 60, 80). Within each CV, a data-dependent acquisition (DDA) mode of acquisition with a r=120,000 (@ m/z 200) full MS scan from m/z 375-1500 with a target AGC value of 4e5 ions was performed. MS/MS scans were acquired at Rapid scan rate (Ion Trap) with an AGC target of 1e4 ions and a max injection time of 100 ms. The total cycle time for each CV was 0.66s, with total cycle times of 2 sec between like MS scans. A 20s dynamic exclusion was employed to increase depth of coverage. The total analysis cycle time for each sample injection was approximately 2 hours.
DOI: 10.6084/m9.figshare.14680641
title: AMP prediction results

description: Datatable containing all AMP-prediction results from the prediction tool assessments.<br>
DOI: 10.6084/m9.figshare.14680641.v1
title: AMP prediction results

description: Datatable containing all AMP-prediction results from the prediction tool assessments.<br>
DOI: 10.6084/m9.figshare.4534118.v4
title: Teacher pedagogical content knowledge, practice, and student achievement<sup>†</sup>

description: In this exploratory study, we attempted to measure potential changes in teacher knowledge and practice as a result of an intervention, as well as trace such changes through a theoretical path of influence that could inform a model of teacher professional knowledge. We created an instrument to measure pedagogical content knowledge (PCK), studied the impact of a two-year professional development intervention, explored the relationships among teacher variables to attempt to validate a model of teacher professional knowledge, and examined the relationship of teacher professional knowledge and classroom practice on student achievement. Teacher professional knowledge and skill was measured in terms of academic content knowledge (ACK), general pedagogical knowledge (GenPK), PCK and teacher practice. Our PCK instrument identified two factors within PCK: PCK-content knowledge and PCK-pedagogical knowledge. Teacher gains existed for all variables. Only GenPK had a significant relationship to teacher practice. ACK was the only variable that explained a substantial portion of student achievement. Our findings provide empirical evidence that we interpret through the lens of the model of teacher professional knowledge and skill, including PCK [Gess-Newsome, J. (2015). A model of teacher professional knowledge and skill including PCK: Results of the thinking from the PCK summit. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), <i>Re-examining pedagogical content knowledge in science education</i> (pp. 28–42). London: Routledge Press], highlighting the complexity of measuring teacher professional knowledge and skill.
DOI: 10.5281/zenodo.4812868
title: Lake Mendota under-ice light and water column profiles 2018 and 2019 (The Lake Ice Continuum Concept)

description: This dataset complements the case study of Lake Mendota presented in <em>The Lake Ice Continuum Concept: Influence of Winter Conditions on Ecosystem Dynamics</em> published in the Journal of Geophysical Research - Biogeosciences.
DOI: 10.5281/zenodo.4812867
title: Lake Mendota under-ice light and water column profiles 2018 and 2019 (The Lake Ice Continuum Concept)

description: This dataset complements the case study of Lake Mendota presented in <em>The Lake Ice Continuum Concept: Influence of Winter Conditions on Ecosystem Dynamics</em> published in the Journal of Geophysical Research - Biogeosciences.
DOI: 10.6084/m9.figshare.14671971
title: an2017

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.5281/zenodo.4743890
title: B-Hall Sample Areas for Lighting Analysis

description: The data set contains 3D luminance point clouds for lighting analysis. Colums: X Y Z (L) Study Area: B-Hall, a lecture hall at Aalto University, Espoo, Finland.
DOI: 10.5281/zenodo.4743889
title: B-Hall Sample Areas for Lighting Analysis

description: The data set contains 3D luminance point clouds for lighting analysis. Colums: X Y Z (L) Study Area: B-Hall, a lecture hall at Aalto University, Espoo, Finland.
DOI: 10.15468/xgk22s
title: The Neotropical species of the genera Pseudespeson L, 1994 and Espeson S, 1882 (Coleoptera: Staphylinidae: Osoriinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Irmler, U. (2012): The Neotropical species of the genera Pseudespeson L, 1994 and Espeson S, 1882 (Coleoptera: Staphylinidae: Osoriinae). Beiträge Zur Entomologie = Contributions to Entomology 62 (2): 331-360, DOI: 10.21248/contrib.entomol.62.2.331-360
DOI: 10.6084/m9.figshare.14671980
title: an2019

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.6084/m9.figshare.14671986
title: asd2017

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.15468/4zr547
title: Agdistis marionae sp. n., a new Pterophoridae from St. Helena (Lepidoptera)

description: This dataset contains the digitized treatments in Plazi based on the original journal article M. (2012): Agdistis marionae sp. n., a new Pterophoridae from St. Helena (Lepidoptera). Beiträge Zur Entomologie = Contributions to Entomology 62 (6): 447-457, DOI: 10.21248/contrib.entomol.62.2.447-457
DOI: 10.6084/m9.figshare.14671989
title: asd2019

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.5281/zenodo.4812948
title: Corona data

description: The data of the study: "Social capital during the Covid-19 pandemic threat: The Russian experience"
DOI: 10.5281/zenodo.4812947
title: Corona data

description: The data of the study: "Social capital during the Covid-19 pandemic threat: The Russian experience"
DOI: 10.6084/m9.figshare.14671995
title: bip2011

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.6084/m9.figshare.14671998
title: bip2019

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.24376/rd.sgul.11830764
title: Facilitators to Adoption qualitative study dataset

description: This dataset is associated with the pre-print publication, Pacho, A et al. Facilitators and barriers for clinical implementation of a 30-minute point-of-care test for Neisseria Gonorrhoeae and Chlamydia Trachomatis into rountine care. SocArXiv. May 26. doi:10.31235/osf.io/z2c5u.<br>Qualitative interviews with healthcare professionals and commissioners in England about the facilitators and barriers to adoption of new technologies (generally) and point-of-care tests (POCTs) for STIs (specifically) into sexual health clinics. Interviews were conducted before and after POCTs were implemented into clinical settings. <br><br>Please contact the authors for further details on this dataset.<br>
DOI: 10.24376/rd.sgul.11830764.v1
title: Facilitators to Adoption qualitative study dataset

description: This dataset is associated with the pre-print publication, Pacho, A et al. Facilitators and barriers for clinical implementation of a 30-minute point-of-care test for Neisseria Gonorrhoeae and Chlamydia Trachomatis into rountine care. SocArXiv. May 26. doi:10.31235/osf.io/z2c5u.<br>Qualitative interviews with healthcare professionals and commissioners in England about the facilitators and barriers to adoption of new technologies (generally) and point-of-care tests (POCTs) for STIs (specifically) into sexual health clinics. Interviews were conducted before and after POCTs were implemented into clinical settings. <br><br>Please contact the authors for further details on this dataset.<br>
DOI: 10.6084/m9.figshare.14102594
title: bip2021

description: PGC3 bipolar disorder GWAS summary statistics<br><br>Three sets of GWAS summary statistics are provided: <br>1. bipolar disorder (all cases): pgc-bip2021-all.vcf.tsv.gz2. bipolar I disorder: pgc-bip2021-BDI.vcf.tsv.gz3. bipolar II disorder: pgc-bip2021-BDII.vcf.tsv.gz<br>Please refer to original publication for detailed study info and phenotype definitions.
DOI: 10.6084/m9.figshare.12404483
title: Embedding Learning

description: Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of <i>U</i>-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12404483.v4
title: Embedding Learning

description: Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of <i>U</i>-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12404483.v3
title: Embedding Learning

description: Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of <i>U</i>-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12126618
title: Prediction of the secretomes of endophytic and nonendophytic fungi reveals similarities in host plant infection and colonization strategies

description: Endophytic fungi are microorganisms that inhabit internal plant tissues without causing apparent damage. During the infection process, both endophytic and phytopathogenic fungi secrete proteins to resist or supplant the plant’s defense mechanisms. This study analyzed the predicted secretomes of six species of endophytic fungi and compared them with predicted secretomes of eight fungal species with different lifestyles: saprophytic, necrotrophic, hemibiotrophic, and biotrophic. The sizes of the predicted secretomes varied from 260 to 1640 proteins, and the predicted secretomes have a wide diversity of CAZymes, proteases, and conserved domains. Regarding the CAZymes in the secretomes of the analyzed fungi, the most abundant CAZyme families were glycosyl hydrolase and serine proteases. Several predicted proteins have characteristics similar to those found in small, secreted proteins with effector characteristics (SSPEC). The most abundant conserved domains, besides those found in the SSPEC, have oxidation activities, indicating that these proteins can protect the fungus against oxidative stress, against domains with protease activity, which may be involved in the mechanisms of nutrition, or against lytic enzymes secreted by the host plant. This study demonstrates that secretomes of endophytic and nonendophytic fungi share an arsenal of proteins important in the process of infection and colonization of host plants.
DOI: 10.6084/m9.figshare.12126618.v1
title: Prediction of the secretomes of endophytic and nonendophytic fungi reveals similarities in host plant infection and colonization strategies

description: Endophytic fungi are microorganisms that inhabit internal plant tissues without causing apparent damage. During the infection process, both endophytic and phytopathogenic fungi secrete proteins to resist or supplant the plant’s defense mechanisms. This study analyzed the predicted secretomes of six species of endophytic fungi and compared them with predicted secretomes of eight fungal species with different lifestyles: saprophytic, necrotrophic, hemibiotrophic, and biotrophic. The sizes of the predicted secretomes varied from 260 to 1640 proteins, and the predicted secretomes have a wide diversity of CAZymes, proteases, and conserved domains. Regarding the CAZymes in the secretomes of the analyzed fungi, the most abundant CAZyme families were glycosyl hydrolase and serine proteases. Several predicted proteins have characteristics similar to those found in small, secreted proteins with effector characteristics (SSPEC). The most abundant conserved domains, besides those found in the SSPEC, have oxidation activities, indicating that these proteins can protect the fungus against oxidative stress, against domains with protease activity, which may be involved in the mechanisms of nutrition, or against lytic enzymes secreted by the host plant. This study demonstrates that secretomes of endophytic and nonendophytic fungi share an arsenal of proteins important in the process of infection and colonization of host plants.
DOI: 10.6084/m9.figshare.12312101
title: <i>Commelinaceomyces</i>, gen. nov., for four clavicipitaceous species misplaced in <i>Ustilago</i> that infect Commelinaceae

description: A fungus causing false smut in the flowers of <i>Murdannia keisak</i> (Commelinaceae, Commelinales, Monocots) in Japan was morphologically identical to <i>Ustilago aneilematis</i>. The fungus infected ovaries of most flowers of host plants. Infected flowers were filled with yellow to orange thick-walled conidia that became olivaceous green at maturity. However, multilocus phylogenetic analysis of DNA sequences (18S, 28S, translation elongation factor 1α [<i>TEF</i>], the largest [<i>RPB1</i>] and the second largest [<i>RPB2</i>] subunit of RNA polymerase II) showed that the fungus belonged to the tribe Ustilaginoideae (Clavicipitaceae, Hypocreales, Ascomycota). Microscopic examination showed that the fungus developed conidia at the apex of conidiogenous cells, in contrast to other species in the Ustilaginoideae that develop conidia pleurogenously. A new genus, <i>Commelinaceomyces</i>, is formally proposed in the Ustilaginoideae to accommodate this fungus. Four species previously misplaced in <i>Ustilago</i> (Ustilaginales, Basidiomycota) are transferred to <i>Commelinaceomyces</i>, including the type of the genus, <i>C. aneilematis</i>, on <i>Murdannia keisak</i>. This is the first report of a clavicipitaceous species infecting host plants in the Commelinaceae.