cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/rsv1vh/1q7saz
title: PerC21.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/5bttsb
title: PerC1.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/71bxnw
title: PerC1.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/c21sjd
title: PerC20.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/eip3gd
title: PerC20.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/i2zdbm
title: PerC21.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ozdiia
title: PerC20.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/sfqcrq
title: PerC21.A.C.high.cont.image.pbcor.fits
DOI: 10.25919/5da883478385b
title: Parkes pulsar observations with undefined project IDs for 2019_26

description: Parkes pulsar observations with undefined project IDs for 2019. This collection contains observations from project PX048.
DOI: 10.7910/dvn/rsv1vh/1mkhms
title: PerC23.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/2njomn
title: PerC24.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/6drcre
title: PerC24.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/brbptx
title: PerC23.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/kenaeo
title: PerC23.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/kibdx0
title: PerC22.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/lpzzal
title: PerC22.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/rb8xni
title: PerC22.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/2fsj6x
title: PerC27.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/2fwlfd
title: PerC25.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/bzrhau
title: PerC24.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/hd0abv
title: PerC25.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/iwl4kf
title: PerC26.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/lw13ru
title: PerC25.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/lyha6i
title: PerC26.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ni39ez
title: PerC26.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/5sat5d
title: PerC29.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/9eixo1
title: PerC2.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/g2stqh
title: PerC28.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ik7el3
title: PerC28.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/lcybns
title: PerC29.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/mu6mt5
title: PerC28.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ncqcby
title: PerC27.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/rypl80
title: PerC29.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/zu5go5
title: PerC27.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/9jdfhk
title: PerC31.A.C.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/h9tlfn
title: PerC2.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/hahf03
title: PerC2.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/hpt9km
title: PerC33.A.C.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/htmeqb
title: PerC32.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/pphku9
title: PerC32.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/wbzer7
title: PerC31.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/zc1dlb
title: PerC32.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/znpyvu
title: PerC31.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/1mpq1z
title: PerC35.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/7t01jy
title: PerC33.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/enh8fl
title: PerC33.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/n2hjbd
title: PerC35.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/nqbtr6
title: PerC34.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/oiabjr
title: PerC34.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/stq4oh
title: PerC34.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/xodnvq
title: PerC35.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/e4su5s
title: PerC38.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/faxadd
title: PerC37.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/hbdoaf
title: PerC37.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ihuuz6
title: PerC36.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/jugzsc
title: PerC38.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/majkfg
title: PerC36.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/qdpnmt
title: PerC36.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/qgkktt
title: PerC37.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/xbkonv
title: PerC38.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/2pjesk
title: PerC4.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/5japaj
title: PerC3.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ca8rzd
title: PerC39.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/gkb6ku
title: PerC3.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/kcghib
title: PerC4.A.C.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/kegdxh
title: PerC39.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/pvppyp
title: PerC3.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/xvhb4g
title: PerC4.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/y2nlho
title: PerC39.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/46uegr
title: PerC6.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/etfqsp
title: PerC7.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/gtxwr6
title: PerC5.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/ihbnfd
title: PerC6.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/itnufc
title: PerC7.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/p9aypq
title: PerC5.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/qbwn1v
title: PerC6.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/u5ey30
title: PerC7.A.C.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/vkd3ua
title: PerC5.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/fsgktd
title: PerC8.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/nrho1r
title: PerC9.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/piac0l
title: PerC9.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/poyyig
title: PerC15.A.C.cont.low.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/rsrbq2
title: PerC8.A.C.low.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/txkcty
title: PerC15.A.C.cont.high.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/uqnn3v
title: PerC9.A.C.cont.image.tt0.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/wjnei8
title: PerC8.A.C.high.cont.image.pbcor.fits
DOI: 10.7910/dvn/rsv1vh/uxtkfa
title: PerC15.A.C.cont.image.tt0.pbcor.fits
DOI: 10.5445/ir/1000098881
title: Benchmark Sets used in the Dissertation of Sebastian Schlag

description: This data set contains all benchmark sets used in the dissertation of Sebastian Schlag.
DOI: 10.5281/zenodo.3404016
title: Source Data file for Weiss et al., Nature Communications 10, 4772 (2019).

description: Source Data file for Weiss et al., "Controlled Creation of a Singular Spinor Vortex by Circumventing the Dirac Belt Trick", Nature Communications <strong>10</strong>, 4772 (2019); containing data for all relevant figures.
description: We acknowledge funding by the National Science Foundation (Grant Nos. PHY--1519174 and PHY--1806318), by the Emil Aaltonen Foundation, by the Kaupallisten ja teknillisten tieteiden tukis\"a\"ati\"o (KAUTE) foundation through its Researchers Abroad program, by the Academy of Finland through the Centre of Excellence Programme (grant no.\ 312300) and ERC Consolidator Grant QUESS (no. 681311), and by the EPSRC.
DOI: 10.5281/zenodo.3404017
title: Source Data file for Weiss et al., Nature Communications 10, 4772 (2019).

description: Source Data file for Weiss et al., "Controlled Creation of a Singular Spinor Vortex by Circumventing the Dirac Belt Trick", Nature Communications <strong>10</strong>, 4772 (2019); containing data for all relevant figures.
description: We acknowledge funding by the National Science Foundation (Grant Nos. PHY--1519174 and PHY--1806318), by the Emil Aaltonen Foundation, by the Kaupallisten ja teknillisten tieteiden tukis\"a\"ati\"o (KAUTE) foundation through its Researchers Abroad program, by the Academy of Finland through the Centre of Excellence Programme (grant no.\ 312300) and ERC Consolidator Grant QUESS (no. 681311), and by the EPSRC.
DOI: 10.5445/ir/1000098894
title: SEM, FIB, STEM images. XRD data. Raw Friction data
DOI: 10.25919/5da8881bbf566
title: Parkes observations for project P941 semester 2018OCTS_01

description: Transitional millisecond pulsars show rapid swings between accretion-powered (X-ray) and rotation-powered (radio) pulsar state. Their discovery opened a brand new window to study binary evolutionary scenarios and the interplay between the pulsar magnetosphere and accretion flows. MAXI J0911-655 is the first accreting millisecond pulsars discovered in the globular cluster NGC 2808. It is harboured in a close binary system (P_orb~40 min) and is in X-ray outburst for more than two years. We propose 4 x 4 hours NAPA observations of the source with the Parkes 64m telescope, to be performed within a few days from the end of the X-ray outburst, to catch the possible activation of a radio pulsar in the system expected as soon as accretion ceases. In case of a detection, the proposed observations will allow measuring the time needed for a radio pulsar to turn on after an accretion event, and to study state transitions in a binary system harbouring a very light donor star. This is the re-submission of prop. P941, not triggered as {\src} has not faded into X-ray quiescence yet.
DOI: 10.17632/9j35yskmj3.1
title: Data and supplementary files for: Rare earth element geochemistry of Australian Neoproterozoic carbonate: Constraints on the Neoproterozoic oxygenation events

description: Data and supplementary files for "Rare earth element geochemistry of Australian Neoproterozoic carbonate: Constraints on the Neoproterozoic oxygenation events"
DOI: 10.21335/nmdc-1826273218
title: Data for: An efficient protocol and data set for automated otolith image analysis

description: The northeast Arctic cod (Gadus Morhua) is the world's largest cod stock and it supports a large fishery in addition to being an important component of the Barent Sea ecosystem. Management of this stock uses an age-based analytical assessment model, and an accurate estimation of the age structure of the fish populations is essential. The age of individual cod is determined by manually examining the layered structure of otoliths. Image-based methods have been tried to age otoliths with varying results, but recent developments in automatic image analysis techniques have been shown to be effective in similar situations. The objective of this paper is to describe a method to efficiently image a manually broken otolith (avoiding the cross-sectioning and molding process) and to describe the organization and acquisition of imaged broken otolith images with associated metadata for a collection of northeast Arctic cod otoliths. A single lens reflex camera was used for capturing photos of the broken otoliths. A total of six images were taken for each subject, consisting of three images in the first position with three different light exposures and three images in the second position with three different light exposures. This results in a simple and efficient procedure for capturing clear, satisfactory, and reproducible images of broken fish otoliths, and a more straightforward and less labor intensive alternative to the commonly used methods that involve cross-sectioning and molding of the otolith.
DOI: 10.5281/zenodo.3497156
title: ECC-POPC-curved-bilayer-negative

description: A curved POPC bilayer, restrained in the *positive* regions - negatively curved regions are free to move. 150 mmol/l CaCl2
DOI: 10.5281/zenodo.3497157
title: ECC-POPC-curved-bilayer-negative

description: A curved POPC bilayer, restrained in the *positive* regions - negatively curved regions are free to move. 150 mmol/l CaCl2
DOI: 10.7910/dvn/yn03bj/bb2wv2
title: dynes-martin_replication_AMOS2012-1.tab

description: Stata data set of variables from 2012 American Municipal Officials Survey
DOI: 10.7910/dvn/yn03bj/k7x4ai
title: 00CODE_replication_AMOS2012.do

description: Stata do file to analyze data from 2012 American Municipal Officials Survey
DOI: 10.7910/dvn/yn03bj/l1x756
title: dynes-martin_replication_AMOS2014.tab

description: CSV file of variables from 2014 American Municipal Officials Survey
DOI: 10.7910/dvn/yn03bj/l4cnw0
title: 00CODE_replication_AMOS2014.do

description: Stata do file to analyze data from 2014 American Municipal Officials Survey
DOI: 10.7910/dvn/yn03bj/rtqiow
title: dynes-martin_replication_AMOS2012.tab

description: CSV file of variables from 2012 American Municipal Officials Survey
DOI: 10.7910/dvn/yn03bj/ujl0j2
title: dynes-martin_replication_AMOS2014-1.tab

description: Stata data set of variables from 2014 American Municipal Officials Survey
DOI: 10.17632/dxv5bfjz8s.1
title: Data for: The PERK/Nrf2 pathway mediates endoplasmic reticulum stress-induced injury by upregulating endoplasmic reticulophagy in H9c2 cardiomyoblasts

description: ERS caused ER-phagy with a decrease in cell viability and an increase in cell death in H9c2 cardiomyoblasts.(A) Live/Dead staining of H9c2 cardiomyoblasts was performed using a LIVE/DEAD® Viability/Cytotoxicity Kit (bar=40 μm, n=3). TG- or TM-treated H9c2 cardiomyoblasts were incubated with calcein-AM (green) and EthD-1 (red) for 10 min, and the fluorescence was visualized using a confocal microscope. Green fluorescence indicates live cells, while red fluorescence indicates dead cells. (E) Ultrastructural lesions in the ER caused by TG or TM and the occurrence of ER-phagy were observed via transmission electron microscopy (bar=500 nm). (F) Representative confocal microscopy images corresponding to analysis of ER-phagy for TG- or TM-treated H9c2 cardiomyoblasts and showing colocalization of autophagosomes with ER-fragments (bar=15 μm; n=3). LC3B was labeled with Texas Red, while calreticulin was labeled with Alexa Fluor 488.
DOI: 10.5281/zenodo.3497065
title: QLKNN10D training set

description: This dataset contain a large-scale run of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is used in many Fusion integrated modelling suites within Europe, and is openly available at qualikiz.com. This dataset was generated with v2.4.0 'Big dataset edition' of QuaLiKiz, see https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/v2.4.0. Note that GyroBohm fluxes were rescaled to match the normalizations of fluxes with a instead of R0, as is the case for v2.4.1 and later. The dataset was used to train neural network versions of QuaLiKiz, which when integrated in modelling suites allow for near-real time tokamak core transport simulations. Please check our proof-of-principle and application in RAPTOR, short paper for the EPS 2019 conference, and published in the Physics of Plasmas ICDDPS2 Special Issue https://aip.scitation.org/doi/10.1063/1.5134126. This paper is also available on arXiv https://arxiv.org/abs/1911.05617 and zenodo 10.5281/zenodo.3595558. Related repositories: Training, plotting, filtering, and auxiliary tools https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop QuaLiKiz related tools https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools FORTRAN QLKNN implementation with wrapper for Python and MATLAB https://gitlab.com/qualikiz-group/QLKNN-fortran Weights and biases of 'hyperrectangle style' QLKNN https://gitlab.com/qualikiz-group/qlknn-hyper
description: This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 and 2019-2020 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.
description: {"references": ["J. Citrin et al. (2015). Real-time capable first principle based modelling of tokamak turbulent transport (doi.org/10.1088/0029-5515/55/9/092001)", "F. Felici et al. (2018). Real-time-capable prediction of temperature and density profiles in a tokamak using RAPTOR and a first-principle-based transport model (doi.org/10.1088/0029-5515/55/9/092001)"]}
DOI: 10.5257/oecd/na/2019q3
title: National Account Statistics (2019Q3 Edition)

description: The OECD National Accounts Statistics database includes annual and quarterly data of a wide range of areas from 1955, such as gross domestic product (GDP) with its three approaches: expenditure based, output based and income based; gross domestics product (GDP) per capita; disposable income; population and employment; PPPs and exchange rates; general government accounts; financial accounts flows and stocks and central government debt for OECD countries and non-member economies. Data are internationally comparable by following the System of National Accounts 2008 (SNA 2008), except for Chile, Japan and Turkey who report under SNA 1993.
DOI: 10.5255/ukda-sn-853954
title: A feminist, participatory and assets-based exploration of women's experiences of ageing with HIV in London 2017
DOI: 10.21227/qk5a-c787
title: REVIEW ON FRUIT DISEASE DETECTION USING IMAGE PROCESSING

description: Fruit disease causes lot of economic loss and major loss in agricultural production all over the world. The crops are affected by uneven climatic conditions. Because of that diseases on plant is increased and agriculture yield is decreased[1]. Now days, the conditions become worst because of bacterial diseases. Increasing population burdens farmers to increase yield.Modern agriculture will overcome this type of effect and improves agriculture[1][4]. So,the image processing technique is used to identify the disease during its growing stage .At that time we may make some prevention to the other fruits in the field. Several methods in image processing can be used to overcome the disease[3].K-means clustering technique which is used for segmentation of image, some sort of segmented image will get extracted,some image will be finalized by using Multi-class Support vector machine[2][3]. The image which is enhanced where the segmentation is done using k-means and thresholding based segmentation where the extraction of disease in done. Discrete Wavelet Transform method extracts a set of visual features of diseased portions. Based on the extracted featuresset, a SVM classifier identifies the affected disease type and the another system which uses two database, one is for already stored image and another for query of image[6]. Web based system which is used to identify the disease of fruit for non experts, based on picture which will be represented by the symptoms of fruit[5].
DOI: 10.5281/zenodo.3497385
title: M. tuberculosis bioinformatics training data

description: Inferred ancestral reference genome of <em>M. tuberculosis</em> combined with NCBI NC_000962.3 annotation in Genbank format.
DOI: 10.5281/zenodo.3497504
title: weecology/portalPredictions 2019-10-17

description: Using various models to forecast abundances at Portal
DOI: 10.5281/zenodo.3497505
title: weecology/forecasts 2019-10-17

description: Archived forecasts for future analysis
DOI: 10.5281/zenodo.3497393
title: scRNA-seq dataset prefiltered from Paul et al. (2015), Cell

description: Preprocessed dataset from Paul et al. (2015), Cell, to use for tradeSeq vignette.
DOI: 10.5281/zenodo.3497394
title: scRNA-seq dataset prefiltered from Paul et al. (2015), Cell

description: Preprocessed dataset from Paul et al. (2015), Cell, to use for tradeSeq vignette.
DOI: 10.5281/zenodo.3497481
title: Image database of giant petrels preying on sperm whales

description: All image data of giant petrels preying on sperm whales as supplement to submitted paper by Towers and Gasco.
description: Metadata times are in UTC
DOI: 10.5281/zenodo.3497482
title: Image database of giant petrels preying on sperm whales

description: All image data of giant petrels preying on sperm whales as supplement to submitted paper by Towers and Gasco.
description: Metadata times are in UTC
DOI: 10.17632/js86f4nzyn.1
title: Data, R code, and sample recordings for: The relevance of auditory feedback for consonant production: the case of fricatives

description: This data set contains the data of 19 native Russian speakers for whom the spectrum of the target fricative [sj] was auditorily perturbed in two opposite directions. The main goal of the study was to investigate whether speakers were able to develop two different compensatory strategies to successfully produce the target sound. In the accompanying R code, we present a multi-parameter analysis where we examine the relevance of a series of acoustic measures for describing the adaptation process. By applying a supervised classification algorithm (Random Forest; RF), we aimed to identify those acoustic parameters which were systematically affected during the adaptation process. Consequently, to break down the adaptation process into individual compensatory strategies, we traced specific acoustic changes that occurred over the course of the experiment by means of generalized additive mixed models (GAMM).
DOI: 10.5281/zenodo.3497307
title: zhzhch88/Zhengchen-Zhang: The programs of the determination of LCI

description: The programs of the paper "The Definition of the Lithology Combination Index and Quantitative Prediction of Fractures in Shale ".
DOI: 10.5281/zenodo.3497308
title: zhzhch88/Zhengchen-Zhang: The programs of the determination of LCI

description: The programs of the paper "The Definition of the Lithology Combination Index and Quantitative Prediction of Fractures in Shale ".
DOI: 10.7910/dvn/sccqyd/whs1sp
title: CrowdTangle Codebook.pdf

description: :unav
DOI: 10.5287/bodleian:5rbk00g9e
title: Azetidinium as Cation in Lead Mixed Halide Perovskite Nanocrystals of Optoelectronic Quality
DOI: 10.21227/afeg-sw30
title: A case study with SymbiHand: an sEMG-controlled electrohydraulic hand orthosis for individuals with Duchenne muscular dystrophy

description: The dataset, includes raw data, observations and biometric data from our case study with an individual with DMD, controlling for the first time an active hand orthosis.
DOI: 10.21227/36zn-2m65
title: High Resulution Images Of Air-Suspended Single-Wall Carbon Nanotubes

description: The dataset contains high-resolution microscopy images and confocal spectra of semiconducting single-wall carbon nanotubes. Carbon nanotubes allow down-scaling of electronic components to the nano-scale. There is initial evidence from Monte Carlo simulations that microscopy images with high digital resolution show energy information in the Bessel wave pattern that is visible in these images. In this dataset, images from Silicon and InGaAs cameras, as well as spectra, give valuable insights into the spectroscopic properties of these single-photon emitters. The source data is connected to a GitLab pipeline running on docker containers for data processing to various formats that are valid input to machine learning algorithms to further prove that there is information about the excitonic emission wavelength contained in the microscopic images.
DOI: 10.21227/p8x3-q316
title: A case study with SymbiHand: an sEMG-controlled electrohydraulic hand orthosis for individuals with Duchenne muscular dystrophy

description: The dataset, includes raw data, observations and biometric data from our case study with an individual with DMD, controlling for the first time an active hand orthosis.