cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7479/3caa-gq23
title: Findbücher Konvolute im Archiv des Museums für Naturkunde Berlin

description: Die Bestände des Archivs des Museums für Naturkunde Berlin (HBSB, Historische Bild- und Schriftgutsammlungen) werden sukzessive für die inhaltliche Recherche erschlossen. Unsere Findbücher für Nachlässe, Konvolute und andere Teilbestände bieten eine Einleitung zu Bestandsbildner und Bestandsgeschichte, Benutzungshinweise und Zitiervorgaben. Angehängte Inventarlisten geben eine Übersicht über Umfang und Inhalte der Verzeichnungseinheiten (Akten / Bilder / Objekte). Konvolute umfassen Dokumente zu Expeditionen, aber auch Archive von Gesellschaften oder Schriftgutsammlungen, die weder reine Nachlässe, noch dem Verwaltungsschriftgut zuzuordnen sind. Ein Überblick über die Findmittel zu Nachlässen findet sich hier. The holdings of the Archive of the Museum für Naturkunde Berlin (HBSB, Historical Image and Document Collections) are successively being made accessible for research. Our finding aids for estates, collections and other partial holdings provide an introduction to the creator of the holdings and their history, instructions for use and citation guidelines. Attached inventory lists provide an overview of the scope and contents of the units of description (files/images/objects). Currently, these are only available in German. Convoluts comprise documents of expeditions, but als archives of societies or written document collections that are neither true estates or part of the general administrative written documents. An overview of the finding aids of estates can be found here.
DOI: 10.5281/zenodo.7281340
title: Ultrahigh-responsivity waveguide-coupled optical power monitor for Si photonic circuits operating at near-infrared wavelengths

description: The simulation code and script of Lumerical for the Simulation in the Supplementary figures: Fig. S4 and Fig. S5.
DOI: 10.5281/zenodo.7281339
title: Ultrahigh-responsivity waveguide-coupled optical power monitor for Si photonic circuits operating at near-infrared wavelengths

description: The simulation code and script of Lumerical for the Simulation in the Supplementary figures: Fig. S4 and Fig. S5.
DOI: 10.5281/zenodo.7333268
title: FEM-DEM bridging coupling for the modeling of gouge

description: <strong>Full Changelog</strong>: https://github.com/ManonVL/fem-dem-bridging-coupling/commits/v1.0.0<br> <br> This repository contains the data used in the paper "FEM-DEM bridging coupling for the modeling of gouge" (https://doi.org/10.1002/nme.7171).
DOI: 10.5281/zenodo.7333251
title: correctKin PLINK data sets

description: The PLINK data set of archaic mediveal family and the public 1KG individuals used in our manuscript: An optimized method to infer relatedness up to the 4th degree from low coverage ancient human genomes (BMC Genome Biology). The public sample IDs are equivalent with the 1KG phase III IDs. For archaic data: FTH (Father - tooth sample - high coverage); FPM (father - pars petrosa - medium coverage); FPL (father - parse petrosa - low coverage); CPH (Son - pars petrosa - high coverage); FPL (Son - pars petrosa - low coverage).
DOI: 10.5281/zenodo.7333250
title: correctKin PLINK data sets

description: The PLINK data set of archaic mediveal family and the public 1KG individuals used in our manuscript: An optimized method to infer relatedness up to the 4th degree from low coverage ancient human genomes (BMC Genome Biology). The public sample IDs are equivalent with the 1KG phase III IDs. For archaic data: FTH (Father - tooth sample - high coverage); FPM (father - pars petrosa - medium coverage); FPL (father - parse petrosa - low coverage); CPH (Son - pars petrosa - high coverage); FPL (Son - pars petrosa - low coverage).
DOI: 10.25377/sussex.21388851.v1
title: Research Data for paper: Explosive percolation yields highly conductive nanocomposites

description: Data for paper published in Nature Communications 11/11/2022 This dataset comprises all numerical results used in the plotted figures of the paper entitled "Explosive percolation yields highly conductive nanocomposites". The data are given in a single Excel workbook, with each figure panel supplied in a separate sheet: the sheets are labelled according to the figure panel in the main text of the paper. The data are in x-y format, with column headings giving the axis labels/quantities from the figure along with the units. <br> Abstract Explosive percolation is an experimentally-elusive phenomenon where network connectivity coincides with onset of an additional modification of the system; materials with correlated localisation of percolating particles and emergent conductive paths can realise sharp transitions and high conductivities characteristic of the explosively-grown network. Nanocomposites present a structurally- and chemically-varied playground to realise explosive percolation in practically-applicable systems but this is yet to be exploited by design. Herein, we demonstrate composites of graphene oxide and synthetic polymer latex which form segregated networks, leading to low percolation threshold and localisation of conductive pathways. In situ reduction of the graphene oxide at temperatures of &lt;150 °C drives chemical modification of the polymer matrix to produce species with phenolic groups, which are known crosslinking agents. This leads to conductivities exceeding those of dense-packed networks of reduced graphene oxide, illustrating the potential of explosive percolation by design to realise low-loading composites with dramatically-enhanced electrical transport properties. <br> <br>
DOI: 10.25377/sussex.21388851
title: Research Data for paper: Explosive percolation yields highly conductive nanocomposites

description: Data for paper published in Nature Communications 11/11/2022 This dataset comprises all numerical results used in the plotted figures of the paper entitled "Explosive percolation yields highly conductive nanocomposites". The data are given in a single Excel workbook, with each figure panel supplied in a separate sheet: the sheets are labelled according to the figure panel in the main text of the paper. The data are in x-y format, with column headings giving the axis labels/quantities from the figure along with the units. <br> Abstract Explosive percolation is an experimentally-elusive phenomenon where network connectivity coincides with onset of an additional modification of the system; materials with correlated localisation of percolating particles and emergent conductive paths can realise sharp transitions and high conductivities characteristic of the explosively-grown network. Nanocomposites present a structurally- and chemically-varied playground to realise explosive percolation in practically-applicable systems but this is yet to be exploited by design. Herein, we demonstrate composites of graphene oxide and synthetic polymer latex which form segregated networks, leading to low percolation threshold and localisation of conductive pathways. In situ reduction of the graphene oxide at temperatures of &lt;150 °C drives chemical modification of the polymer matrix to produce species with phenolic groups, which are known crosslinking agents. This leads to conductivities exceeding those of dense-packed networks of reduced graphene oxide, illustrating the potential of explosive percolation by design to realise low-loading composites with dramatically-enhanced electrical transport properties. <br> <br>
DOI: 10.5281/zenodo.7324980
title: Functional annotation of the reference transcriptome of Dinophysis ovum strain DoSS3195

description: Raw reads were pre-processed by removing the adaptors and low-quality reads using BBMap. The filtered reads were normalized for depth based on kmer counts using BBNorm function. De novo transcriptomes were generated using both Trinity and velvet-oases. CD-HIT-EST was used to merge the two de novo transcriptomes and reduce the transcript redundancy to 98% similarity and generate unique genes. Transcriptome assembly completeness was evaluated with BUSCO (Benchmarking Universal Single Copy Orthologs) database. Functional annotation was done using blastp function of ncbi-blast using the nr database with evalue 1E-20 and num_alignments 3.
description: Funded by NOAA/ECOHAB: NA19NOS4780182-722372-712683
DOI: 10.6084/m9.figshare.21581430
title: Grethlein et al - Phenotypic raw data

description: Raw data for https://doi.org/10.1186/s12302-022-00690-4
DOI: 10.6084/m9.figshare.21581430.v1
title: Grethlein et al - Phenotypic raw data

description: Raw data for https://doi.org/10.1186/s12302-022-00690-4
DOI: 10.5281/zenodo.7249508
title: Cooperative action of separate interaction domains promotes high-affinity DNA binding of Arabidopsis thaliana ARF transcription factors

description: The repository contains the smFRET and SAXS data presented in the preprint https://doi.org/10.1101/2022.11.16.516730 (BioRxiv)
DOI: 10.5281/zenodo.7249507
title: Cooperative action of separate interaction domains promotes high-affinity DNA binding of Arabidopsis thaliana ARF transcription factors

description: The repository contains the smFRET and SAXS data presented in the preprint https://doi.org/10.1101/2022.11.16.516730 (BioRxiv)
DOI: 10.26302/sshade/experiment_ik_20210602_001
title: Mo K edge XAS transmission of Na2MoO4 reference for energy resolution optimisation
DOI: 10.5281/zenodo.7137930
title: Spacecraft Thruster Firing Test Dataset

description: <strong>WARNING</strong> This version of the dataset is not recommended for anomaly detection use case. We discovered discrepancies in the anomalous sequences. A new version will be released. In the meantime, please ignore all sequence marked as anomalous. <strong>CONTEXT</strong> Testing hardware to qualify it for Spaceflight is critical to model and verify performances. Hot fire tests (also known as life-tests) are typically run during the qualification campaigns of satellite thrusters, but results remain proprietary data, hence making it difficult for the machine learning community to develop suitable data-driven predictive models. This synthetic dataset was generated partially based on the real-world physics of monopropellant chemical thrusters, to foster the development and benchmarking of new data-driven analytical methods (machine learning, deep-learning, etc.). The PDF document "STFT Dataset Description" describes in much details the structure, context, use cases and domain-knowledge about thruster in order for ML practitioners to use the dataset. <strong>PROPOSED TASKS</strong> <strong>Supervised</strong>: <strong>Performance Modelling:</strong> Prediction of the thruster performances (target can be thrust, mass flow rate, and/or the average specific impulse) <strong>Acceptance Test for Individualised Performance Model refinement: </strong>Taking into account the acceptance test of individual thruster might be helpful to generate individualised thruster predictive model <strong>Uncertainty Quantification</strong> <strong>for </strong><strong>Thruster-to-thruster reproducibility verification</strong>, i.e. to evaluate the prediction variability between several thrusters in order to construct uncertainty bounds around the prediction (predictive intervals) of the thrust and mass flow rate of future thrusters that may be used during an actual space mission <strong>Unsupervised / Anomaly Detection</strong> <strong>Anomaly Detection</strong>: Anomalies can be detected in an unsupervised setting (outlier detection) or in a semi-supervised setting (novelty detection). The dataset includes a total of 270 anomalies. A simple approach is to predict if a firing test sequence is anomalous or nominal. A more advanced approach is trying to predict which portion of a time series is anomalous. The dataset also provide a detailed information about each time point being anomalous or nominal. In case of an anomaly, a code is provided which allows to diagnosis the detection system performance on the different types of anomalies contained in the dataset.
DOI: 10.5281/zenodo.7137929
title: Spacecraft Thruster Firing Test Dataset

description: <strong>WARNING</strong> This version of the dataset is not recommended for anomaly detection use case. We discovered discrepancies in the anomalous sequences. A new version will be released. In the meantime, please ignore all sequence marked as anomalous. <strong>CONTEXT</strong> Testing hardware to qualify it for Spaceflight is critical to model and verify performances. Hot fire tests (also known as life-tests) are typically run during the qualification campaigns of satellite thrusters, but results remain proprietary data, hence making it difficult for the machine learning community to develop suitable data-driven predictive models. This synthetic dataset was generated partially based on the real-world physics of monopropellant chemical thrusters, to foster the development and benchmarking of new data-driven analytical methods (machine learning, deep-learning, etc.). The PDF document "STFT Dataset Description" describes in much details the structure, context, use cases and domain-knowledge about thruster in order for ML practitioners to use the dataset. <strong>PROPOSED TASKS</strong> <strong>Supervised</strong>: <strong>Performance Modelling:</strong> Prediction of the thruster performances (target can be thrust, mass flow rate, and/or the average specific impulse) <strong>Acceptance Test for Individualised Performance Model refinement: </strong>Taking into account the acceptance test of individual thruster might be helpful to generate individualised thruster predictive model <strong>Uncertainty Quantification</strong> <strong>for </strong><strong>Thruster-to-thruster reproducibility verification</strong>, i.e. to evaluate the prediction variability between several thrusters in order to construct uncertainty bounds around the prediction (predictive intervals) of the thrust and mass flow rate of future thrusters that may be used during an actual space mission <strong>Unsupervised / Anomaly Detection</strong> <strong>Anomaly Detection</strong>: Anomalies can be detected in an unsupervised setting (outlier detection) or in a semi-supervised setting (novelty detection). The dataset includes a total of 270 anomalies. A simple approach is to predict if a firing test sequence is anomalous or nominal. A more advanced approach is trying to predict which portion of a time series is anomalous. The dataset also provide a detailed information about each time point being anomalous or nominal. In case of an anomaly, a code is provided which allows to diagnosis the detection system performance on the different types of anomalies contained in the dataset.
DOI: 10.5281/zenodo.7333358
title: Dataset Literatur Review Digital Forensic AND Methods

description: Data tersebut didapatkan dengan menggunakan website lens.org dengan memilih Scholarly Works dan pencariannya berbasis kata kunci/keyword “Digital Forensic” “Methods” dan akan memunculkan sejumlah data yang didalamnya berbentuk dokumen. Yang selanjutnya di filter sebanyak 3 kali yang mana fitur filter pertama yaitu pada bagian Document Type “Journal Article”, selanjutnya menggunakan fitur filter date range yang mana menggunakan data rate 10 tahun sebelumnya yaitu tahun 2012 sampai dengan tahun 2022, yang terakhir menggunakan fitur filter Subject Matter pada Subject “Law”. Adapun pada setiap tahapan mulai awal hingga akhir semua data tersebut masing-masing di export dengan format CSV dan BIBTEX, dan diambil semua gambar yang muncul pada menu analisys di lens.org pada tahap terakhir.
DOI: 10.5281/zenodo.7333357
title: Dataset Literatur Review Digital Forensic AND Methods

description: Data tersebut didapatkan dengan menggunakan website lens.org dengan memilih Scholarly Works dan pencariannya berbasis kata kunci/keyword “Digital Forensic” “Methods” dan akan memunculkan sejumlah data yang didalamnya berbentuk dokumen. Yang selanjutnya di filter sebanyak 3 kali yang mana fitur filter pertama yaitu pada bagian Document Type “Journal Article”, selanjutnya menggunakan fitur filter date range yang mana menggunakan data rate 10 tahun sebelumnya yaitu tahun 2012 sampai dengan tahun 2022, yang terakhir menggunakan fitur filter Subject Matter pada Subject “Law”. Adapun pada setiap tahapan mulai awal hingga akhir semua data tersebut masing-masing di export dengan format CSV dan BIBTEX, dan diambil semua gambar yang muncul pada menu analisys di lens.org pada tahap terakhir.
DOI: 10.48425/f2cwz2xl
title: V_soil_temperature_snowbed_experimental

description: Soil temperature measurements in snowbeds with exclosures on the Varanger peninsula. The dataset includes four different types of files and all data files are saved as ;-separated txt-files (except the readme): * One data file per year (V_soil_temperature_snowbed_experimental_YEAR.txt) * One meatadata file per year with dates when the loggers were deployed and collected (V_soil_moisture_temperature_snowbed_experimental_metadata_YEAR.txt) * One coordinate file with coordinates of all sites (V_soil_moisture_temperature_snowbed_experimental_coordinates.txt) * One auxiliary file with information about which sites are included in the study design (V_soil_moisture_temperature_snowbed_experimental_aux.txt) * One readme file with additional information (_readme.pdf)
DOI: 10.6084/m9.figshare.20176004
title: Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes

description: Introduction: Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. Sparseness leads to maximum likelihood estimators (MLEs) of log-OR parameters that are biased away from their null value of zero and tests with inflated type 1 errors. Different penalized-likelihood methods have been developed to mitigate sparse-data bias. We study penalized logistic regression using a class of log-F priors indexed by a shrinkage parameter m to shrink the biased MLE towards zero. For a given m, log-F-penalized logistic regression may be easily implemented using data augmentation and standard software. Method: We propose a two-step approach to the analysis of a genetic association study: first, a set of variants that show evidence of association with the trait is used to estimate m; and second, the estimated m is used for log-F-penalized logistic regression analyses of all variants using data augmentation with standard software. Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. Results: We evaluate the statistical properties of our proposed two-step method and compared its performance to other shrinkage methods by a simulation study. Our simulation studies suggest that the proposed log-F-penalized approach has lower bias and mean squared error than other methods considered. We also illustrate the approach on data from a study of genetic associations with "super senior" cases and middle aged controls. Discussion/Conclusion: We have proposed a method for single rare variant analysis with binary phenotypes by logistic regression penalized by log-F priors. Our method has the advantage of being easily extended to correct for confounding due to population structure and genetic relatedness through a data augmentation approach.
DOI: 10.6084/m9.figshare.21581463
title: Non-Invasive Monitoring of Adrenocortical Activity in a Fossorial Amphisbaenian

description: We validated a technique to quantify GC levels of the North West African checkboard amphisbaenian <em>Trogonophis wiegmanni</em>. The main purpose was to optimize and validate the suitability of an enzyme immunoassay (EIA) for non-invasively monitoring adrenocortical activity, through measuring fecal corticosterone metabolites (FCM). We first made a controlled experimental stimulation of the hypothalamic-pituitary-adrenal (HPA) axis via a corticosterone supplementation and examined whether challenged individuals increased FCM levels more than control ones. Then, we used the validated assay for a preliminary exploration of seasonal, sex and size related differences in FCM levels in the field in an island population of this amphisbaenian.
DOI: 10.6084/m9.figshare.21581358.v1
title: Cenfind datasets

description: Datasets used to train and test cenfind with annotations for centriole markers and nuclei. <br> Dataset names: RPE1wt_CEP63+CETN2+PCNT_1 RPE1wt_CP110+GTU88+PCNT_2 RPE1wt_CEP152+GTU88+PCNT_1 RPE1p53+Cnone_CEP63+CETN2+PCNT_1 U2OS_CEP63+SAS6+PCNT_1 <br> The structure is as follows: projections/ annotations/ fields.txt test.txt train.txt
DOI: 10.5281/zenodo.7333586
title: Etiology, characteristics and occurrence of heart diseases in rural Lesotho (ECHO-Lesotho): A retrospective echocardiography cohort study

description: <strong>Background</strong> In 2019, 600’000 people in Africa died of heart failure and heart diseases will increase on the continent. It is crucial to understand the regional etiologies and risk factors for heart failure and underlying heart diseases. However, echocardiography data from rural Africa are scarce and from Lesotho non-existent. This study aims to examine the occurrence, characteristics and etiology of heart failure and heart diseases using echocardiography data from a referral hospital in rural Lesotho. <strong>Methods</strong> We conducted a retrospective cohort study at Seboche Mission Hospital, the only referral hospital in Butha-Buthe district (Lesotho) with an echocardiography department. We included data from all individuals referred to the department between January 2020 and May 2021. From non-hospitalized patients echocardiographic diagnosis, sex and age were available, from hospitalized patients additional sociodemographic and clinical data could be extracted. <strong>Results</strong> In the study period, a total of 352 echocardiograms were conducted; 213 had abnormal findings (among them 3 children). The majority of adult participants (130/210; 64%) were female and most frequent heart diseases were hypertensive (62/210, 30%), valvular (39/210, 19%) and chronic pulmonary (37/210, 18%). Heart failure represented 11% of hospitalizations in the same period. Among the 126 hospitalized heart failure patients, the most common etiology was chronic pulmonary heart disease (32/126; 25%). Former mine workers and people with a history of tuberculosis were more likely to have a chronic pulmonary heart disease. <strong>Conclusions</strong> The leading cause of heart disease in this setting is hypertension. However, in contrast to other African epidemiological studies, chronic pulmonary heart disease is unexpectedly common. There is an urgent need to improve awareness and knowledge about lung diseases, make diagnostic and therapeutic options available and increase prevention.