cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3715246
title: Dataset related to article "Intramedullary spinal cord tumors: the value of intraoperative neurophysiological monitoring in a series of 57 cases from two Italian centres."

description: BACKGROUND: Intramedullary spinal cord tumors are rare lesions of the central nervous system. Anatomical, molecular and radiological features are well defined, but correct management is still matter of debate. Pertinent literature has reported conflicting opinions regarding the use of intraoperative electrophysiological monitoring (IONM) in the surgical treatment of this kind of lesions, recently. We report a retrospective study from two Italian centres, in order to highlight the usefulness of IONM in the management of intramedullary lesions. METHODS: We performed a retrospective review of patients with intramedullary spinal tumor who underwent surgical resection from February 2011 to February 2018 in two different institutions. Clinical and radiological data, lesion features, timing of symptom onset and IONM findings were record ed . The IONM includ ed somatosensory-evoked potentials(SSEP), motor-evoked potentials(MEP) and D-Wave whenever possible. We evaluated the outcome according to the Modified McCormick scale. We also evaluated the accuracy and relevance of surgical outcomes for each evoked potential(SSEP, MEP, D-Wave). RESULTS: A total of 57 patients were included. A gross total removal was achieved in 46 cases. Neurological follow-up was assessment at 3 days, and 3 and 6 months after surgery. Comparing the preoperative status and 6 months follow-up: the M-McCormick scale showed a neurological stability for 30 patients (52.63%), a worsening of neurological status for 7 patients (12.28%) and an improvement for 20 patients (35.08%). IONM presented high accuracy (sensibility of 100% and specificity of 95.65%) and significantly pred icted postoperative permanent motor d eficits(P&lt;0.0001; AUC=0.978). D-Wave appeared to have significant greater predictive value than MEP and especially SSEP alone(0.967 vs 0.722 vs 0.542; P = 0.044 and P&lt;0.001 respectively). CONCLUSIONS: The gold standard in the intramedullary lesion treatment is maximal safe resection with good neurological outcome, as shown in our patients. The use of IONM is helpful in intramedullary tumors resection in order to minimize postoperative neurological deficits and our analysis suggests that the use of D-Wave presents a statistically significant higher accuracy for predicting postoperative deficits than SSEP and MEP alone.
DOI: 10.21223/p3/nn00oc/m3biyc
title: PAGE 2_Bseline Survey VISTA_2015_Demographic of Members of Household who are less or equal to 60 months of Age (5 years).tab

description: :unav
DOI: 10.21223/p3/nn00oc/wee1up
title: Questionnaires for VISTA Modules of the Baseline Survey in Portuguese.xls

description: :unav
DOI: 10.21223/p3/nn00oc/7pjdcb
title: PAGE 17_Baseline Survey VISTA_2015_Women Knowledge on Vitamina A.tab

description: :unav
DOI: 10.21223/p3/nn00oc/bkmtpf
title: PAGE 4_Baseline Survey VISTA_2015 Crop production.tab

description: :unav
DOI: 10.21223/p3/nn00oc/wtebnk
title: PAGE 13_Baseline Survey VISTA_2015_Attitude, Knowledge, practices.tab

description: :unav
DOI: 10.21223/p3/nn00oc/8kpz8n
title: PAGE 11_Baseline Survey VISTA_2015_Seed System_Vine Dissemination.tab

description: :unav
DOI: 10.21223/p3/nn00oc/p7ql5m
title: PAGE 9_Baseline Survey VISTA_2015_Sweetpotato marketing and selling.tab

description: :unav
DOI: 10.21223/p3/nn00oc/qkxqqu
title: BaseLineSurvey_VISTA Baseline Survey Dictionary Nampula_Mozambique_2015

description: :unav
DOI: 10.21223/p3/nn00oc/w5nb0m
title: PAGE 5_Baseline Survey VISTA_2015_Crop production_Area and use of fertilizer.tab

description: :unav
DOI: 10.21223/p3/dp0oyk/d7bhwr
title: Data_Dictionary_CultivarRelease.xlsx

description: :unav
DOI: 10.21223/p3/nn00oc/accfc0
title: VISTA Nampula and Zambezia 2015 Baseline Report_Draft 20160406.pdf

description: :unav
DOI: 10.21223/p3/nn00oc/t4ve9m
title: PAGE 6_Baseline Survey VISTA_2015_Sweetpotato production.tab

description: :unav
DOI: 10.21223/p3/nn00oc/xe5zvw
title: PAG16_Baseline Survey VISTA_2015_Practices and Preferences.tab

description: :unav
DOI: 10.21223/p3/dp0oyk/notckq
title: 25_Purple_Fleshed_Clones.xlsx

description: :unav
DOI: 10.21223/p3/dp0oyk/ou6ydb
title: 20_Dual_Purpose_Clones.xlsx

description: :unav
DOI: 10.21223/p3/dp0oyk/ut91aw
title: 27_Clones_OFSP.xlsx

description: :unav
DOI: 10.21223/p3/nn00oc/c1qmup
title: Lista of FILES contained in the folder_DATASET Mozambique_VISTA BASELINE SURVEY 2015.tab

description: :unav
DOI: 10.21223/p3/nn00oc/iobjkk
title: PAG2B_Baseline Survey VISTA_2015_Household members with age above 60 Months.tab

description: :unav
DOI: 10.5281/zenodo.3715317
title: Jakmall Computer & Laptop Data Crawling

description: The data we take is product data about computers and laptops sold by an e-commerce named Jakmall. The dataset contains 7 columns and 60 product lines about the most popular computers and laptops. Data scrapped at February 18th 2020. The name column contains the name of the seller who sells the product. The Location column contains the city information of the seller's offline store location. The product column contains the product name. The product_price column contains information on the selling price of one product. The product_wishlistcount column contains numbers that show how many products are used as a wish list that potential buyers want to buy.
DOI: 10.21223/p3/nn00oc/9irp58
title: PAGE 1_Baseline Survey VISTA_2015_Household identification and statement of agreement.tab

description: :unav
DOI: 10.21223/p3/nn00oc/eqj6hb
title: PAGE 3_Baseline Survey VISTA_2015_Affiliation to ssociation or group.tab

description: :unav
DOI: 10.21223/p3/nn00oc/jxryff
title: PAGE 11_Baseline Survey VISTA_2015_Seed System_General.tab

description: :unav
DOI: 10.21223/p3/nn00oc/lwrhbs
title: PAGE 19_Baseline Survey VISTA_2015_Dietary Diversity for Women and Children.tab

description: :unav
DOI: 10.5281/zenodo.3715352
title: Keyword frequencies in popular tech media (01.2016-02.2020)

description: <strong>Sources with weights</strong> <pre> Arstechnica: 1/8, Euractiv: 1/8, Fastcompany: 1/8, The Register: 1/8, Techcrunch: 1/8, The Guardian: 1/8, Venturebeat: 1/8, The Verge: 1/8 </pre> <strong>Methodology</strong> Frequency of appearances for all unigrams and bigrams in the texts Frequency: number of appearances of every term divided by the number of published articles (for every month and source) This measure reveals how many times an expression has been mentioned on average per article Several media sources: a representative index is calculated with weighted average (weights as above) Average monthly change in the analised term's frequency is calculated by OLS regressions The dependent variable of the estimation is the frequency index, while the number of months since the beginning of the analysed period (January 2016) is the independent variable The regression coefficient (referred to as coef) shows by how much on average the analysed expression’s frequency changed with every observed month (marginal change of the frequency), revealing which keywords had the biggest monthly growth <strong>Columns</strong> freq_months (e.g. freq_2019-04): the average frequency of the term coef: the regression coefficient coef_norm: the regression coefficient divided by the mean frequency of the keyword coef_norm_max: the regression coefficient divided by the maximum frequency of the keyword
DOI: 10.5281/zenodo.3715353
title: Keyword frequencies in popular tech media (01.2016-02.2020)

description: <strong>Sources with weights</strong> <pre> Arstechnica: 1/8, Euractiv: 1/8, Fastcompany: 1/8, The Register: 1/8, Techcrunch: 1/8, The Guardian: 1/8, Venturebeat: 1/8, The Verge: 1/8 </pre> <strong>Methodology</strong> Frequency of appearances for all unigrams and bigrams in the texts Frequency: number of appearances of every term divided by the number of published articles (for every month and source) This measure reveals how many times an expression has been mentioned on average per article Several media sources: a representative index is calculated with weighted average (weights as above) Average monthly change in the analised term's frequency is calculated by OLS regressions The dependent variable of the estimation is the frequency index, while the number of months since the beginning of the analysed period (January 2016) is the independent variable The regression coefficient (referred to as coef) shows by how much on average the analysed expression’s frequency changed with every observed month (marginal change of the frequency), revealing which keywords had the biggest monthly growth <strong>Columns</strong> freq_months (e.g. freq_2019-04): the average frequency of the term coef: the regression coefficient coef_norm: the regression coefficient divided by the mean frequency of the keyword coef_norm_max: the regression coefficient divided by the maximum frequency of the keyword
DOI: 10.5281/zenodo.3715330
title: List Anime Scrapped from riie.net per February 18, 2020

description: The data we take is anime movie data taken by an online movie streaming site called www.riie.net. The dataset contains six columns and 199 lines of anime series and movies with various genres. The anime_nama column contains the anime title, followed by anime_url column for the anime information site address. The anime types are loaded in anime_tipe column, meanwhile the anime_total_episode column contains information regarding the series total episode. The anime_status column is stating the latest update of the anime. Next column named anime_tgl_rilis provides the release date of the anime series or movies. Anime producers or production team can be seen in the anime_produser column. Information about score/ratings of the anime can be obtained from the anime_skor column. Meanwhile you can find out the genres from the column named anime_genre1 to anime_genre4. The anime_durasi column provides information on the duration of the anime series per episode, or movie.
DOI: 10.3886/e117301v2
title: Analysis Code for "The Black-White Gap in Non-Cognitive Skills among Elementary School Children"

description: Using two nationally representative datasets, we find large differences between black and white children in teacher-reported measures of non-cognitive skills. We show that teacher reports understate true black-white skill gaps because of reference bias: teachers appear to rate children relative to others in the same school, and black students have lower-skilled classmates, on average, than do white students. We pursue three approaches to addressing these reference biases. Each approach nearly doubles the estimated black-white gaps in non-cognitive skills, to roughly 0.9 standard deviations in third grade.
DOI: 10.26300/26rn-mr75
title: Combined Motion Data Sets
DOI: 10.5281/zenodo.3715360
title: Co-occurrences of trending keywords in popular tech media (01.2016-02.2020)

description: <strong>Sources with weights</strong> <pre> Arstechnica: 1/8, Euractiv: 1/8, Fastcompany: 1/8, The Register: 1/8, Techcrunch: 1/8, The Guardian: 1/8, Venturebeat: 1/8, The Verge: 1/8</pre> <strong>Methodology</strong> Exploring the relationship between topics Pairs of terms which are mentioned together in media articles Most trending social issues and technologies have been selected (e.g. 'gdpr', '5G') The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)
DOI: 10.5281/zenodo.3715361
title: Co-occurrences of trending keywords in popular tech media (01.2016-02.2020)

description: <strong>Sources with weights</strong> <pre> Arstechnica: 1/8, Euractiv: 1/8, Fastcompany: 1/8, The Register: 1/8, Techcrunch: 1/8, The Guardian: 1/8, Venturebeat: 1/8, The Verge: 1/8</pre> <strong>Methodology</strong> Exploring the relationship between topics Pairs of terms which are mentioned together in media articles Most trending social issues and technologies have been selected (e.g. 'gdpr', '5G') The co-occurrence analysis is calculated for pairs consisting of emerging social issues and trending uni/bigrams The number of times the terms appear in articles together with a social issue is divided by the number of times the social issue is mentioned across all articles A single index is constructed for all word pairs by weighted average (taking into account the prevalence of the given source)
DOI: 10.5281/zenodo.3715342
title: CO Emissions inferred from Surface CO Observations over China in December 2013 and 2017

description: <strong>CO_obs.rar</strong> includes assimilation observations for 2013 and 2017, independent verification observations for 2014, 2017 and 2018. NCP, YRD, and PRD represent the North China Plain, the Yangtze River Delta, and the Pearl River Delta, respectively. <strong>emission_36km_2012.nc</strong> and <strong>emission_36km_2016.nc</strong> are prior emissions, <strong>emission_36km_2013.nc</strong> and <strong>emission_36km_2017.nc</strong> are posterior emissions inferred with default 40% uncertainty setting. <strong>emission_36km_20.nc</strong> and <strong>emission_36km_60.nc</strong> are posterior emissions inferred with 20% and 60% uncertainty setting, respectively, which are used for sensitivity test. <strong>emission_36km_nosuper.nc</strong> is posterior emissions inferred without ‘super observation’ method. These files have dimensions of 39 VAR×123 RAW×163 COL and the third variable is CO.
DOI: 10.5281/zenodo.3715343
title: CO Emissions inferred from Surface CO Observations over China in December 2013 and 2017

description: <strong>CO_obs.rar</strong> includes assimilation observations for 2013 and 2017, independent verification observations for 2014, 2017 and 2018. NCP, YRD, and PRD represent the North China Plain, the Yangtze River Delta, and the Pearl River Delta, respectively. <strong>emission_36km_2012.nc</strong> and <strong>emission_36km_2016.nc</strong> are prior emissions, <strong>emission_36km_2013.nc</strong> and <strong>emission_36km_2017.nc</strong> are posterior emissions inferred with default 40% uncertainty setting. <strong>emission_36km_20.nc</strong> and <strong>emission_36km_60.nc</strong> are posterior emissions inferred with 20% and 60% uncertainty setting, respectively, which are used for sensitivity test. <strong>emission_36km_nosuper.nc</strong> is posterior emissions inferred without ‘super observation’ method. These files have dimensions of 39 VAR×123 RAW×163 COL and the third variable is CO.
DOI: 10.5281/zenodo.3715394
title: OneDrive ZIP64 "total number of disks" equals 0 demo file

description: This is a test file that demonstrates a problem with faulty large ZIP files that are generated by the Microsoft OneDrive web client. The value of the “total number of disks” field in the “zip64 end of central dir locator” for this file is set to 0 (zero). Since most reader tools expect a value of 1 for this field, this leads to various interoperability problems. A detailed discussion of this problem van be found in the following blog post: Does Microsoft OneDrive export large ZIP files that are corrupt? The ZIP file contains five 1-GB files, each of which is made up of null-bytes only.
DOI: 10.7910/dvn/eqdkvy/8weyb6
title: liveStage-frame185.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/atwi46
title: liveStage-frame79.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/fontsb
title: liveStage-frame161.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/gcvz3y
title: liveStage-frame93.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/zfui4t
title: liveStage-frame178.mat

description: :unav
DOI: 10.5281/zenodo.3715393
title: OneDrive ZIP64 "total number of disks" equals 0 demo file

description: This is a test file that demonstrates a problem with faulty large ZIP files that are generated by the Microsoft OneDrive web client. The value of the “total number of disks” field in the “zip64 end of central dir locator” for this file is set to 0 (zero). Since most reader tools expect a value of 1 for this field, this leads to various interoperability problems. A detailed discussion of this problem van be found in the following blog post: Does Microsoft OneDrive export large ZIP files that are corrupt? The ZIP file contains five 1-GB files, each of which is made up of null-bytes only.
DOI: 10.7910/dvn/eqdkvy/d0xjfk
title: liveStage-frame68.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/gh4qpm
title: liveStage-frame124.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/lvn6yv
title: liveStage-frame76.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/zskakz
title: liveStage-frame196.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/icekjk
title: liveStage-frame8.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/ihjvv7
title: liveStage-frame33.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/jv6lcg
title: liveStage-frame111.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/lc5esy
title: liveStage-frame106.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/wogqqv
title: liveStage-frame108.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/ywkv51
title: liveStage-frame17.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/djaem6
title: liveStage-frame103.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/ex7ktg
title: liveStage-frame168.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/i9kilz
title: liveStage-frame180.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/sl9fba
title: liveStage-frame105.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/w6cwod
title: liveStage-frame91.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/4uut4s
title: liveStage-frame60.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/dx4yhr
title: liveStage-frame10.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/qus2sz
title: liveStage-frame67.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/y3rhsq
title: liveStage-frame109.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/37nyt9
title: liveStage-frame99.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/dmbaj7
title: liveStage-frame119.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/hlzq84
title: liveStage-frame75.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/p5jwda
title: liveStage-frame55.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/x7idsb
title: liveStage-frame146.mat

description: :unav
DOI: 10.15490/fairdomhub.1.datafile.3405.1
title: /reports/SupplementaryTableS5

description: Supplementary Table S5 - Rywal biological evidence filtering results. Output of 1st filtering step by biological evidence for cv. Rywal. Layer _p_stRT/_I_STRT/_S_03_stCuSTr/_A_03.1_filtering/reports/
DOI: 10.7910/dvn/eqdkvy/8rgdyp
title: liveStage-frame111.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/aucjak
title: liveStage-frame85.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/dzsfmt
title: liveStage-frame156.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/j62lwp
title: liveStage-frame65.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/jxn7zo
title: liveStage-frame49.mat

description: :unav
DOI: 10.3886/e110643v1
title: Data and Code for "Energy Cost Pass-Through in U.S. Manufacturing: Estimates and Implications for Carbon Taxes"

description: We study how changes in energy input costs for U.S. manufacturers affect the relative welfare of manufacturing producers and consumers (i.e., incidence). We also develop a methodology to estimate the incidence of input taxes which accounts for incomplete pass-through, imperfect competition, and substitution amongst inputs. For the several industries we study, 70 percent of energy price-driven changes in input costs get passed through to consumers in the short- to medium-run. The share of the welfare cost that consumers bear is 25-75 percent smaller (and the share producers bear is larger) than models featuring complete pass-through and perfect competition would suggest.
description: Manufacturing industry<br>.
DOI: 10.7910/dvn/eqdkvy/2qzqxy
title: liveStage-frame163.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/5p9jx4
title: liveStage-frame107.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/do2kyi
title: liveStage-frame151.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/ecumta
title: liveStage-frame12.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/ic5oi6
title: liveStage-frame83.mat

description: :unav
DOI: 10.21223/p3/cyd0pi/4mk5oj
title: Data_Dictionary_NASPOT12O_NASPOT13O-Uganda.xlsx

description: :unav
DOI: 10.21223/p3/cyd0pi/ivmkty
title: 90.14 Biofort OFSP 2012 data_2.tab

description: :unav
DOI: 10.21223/p3/cyd0pi/z3unkh
title: 90.13 Biofort NaCRRI 0FSP 2011-2012_2.tab

description: :unav
DOI: 10.3886/e110643
title: Data and Code for "Energy Cost Pass-Through in U.S. Manufacturing: Estimates and Implications for Carbon Taxes"

description: We study how changes in energy input costs for U.S. manufacturers affect the relative welfare of manufacturing producers and consumers (i.e., incidence). We also develop a methodology to estimate the incidence of input taxes which accounts for incomplete pass-through, imperfect competition, and substitution amongst inputs. For the several industries we study, 70 percent of energy price-driven changes in input costs get passed through to consumers in the short- to medium-run. The share of the welfare cost that consumers bear is 25-75 percent smaller (and the share producers bear is larger) than models featuring complete pass-through and perfect competition would suggest.
description: Manufacturing industry<br>.
DOI: 10.7910/dvn/eqdkvy/a6s7pf
title: liveStage-frame188.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/gtxsez
title: liveStage-frame39.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/k97c7u
title: liveStage-frame87.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/qqxiju
title: liveStage-frame62.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/xu2mg0
title: liveStage-frame71.mat

description: :unav
DOI: 10.21223/p3/cyd0pi/ztwtgi
title: 90.15 On farm data final-2012A Biofort_2.tab

description: :unav
DOI: 10.25346/s6/lrv0es
title: UCLA Registrar Enrollment Data, Winter 1999 - Spring 2020

description: Enrollment data scraped from the UCLA Registrar's publicly available Schedule of Classes. Details about the data and scraper are linked below: Exploring Enrollment: Methodology Scraping Enrollment Data From the UCLA Registrar, Part One Scraping Enrollment Data From the UCLA Registrar, Part Two
DOI: 10.25346/s6/lrv0es/ubnlba
title: ucla_registrar_data.dump
DOI: 10.7910/dvn/eqdkvy/6b7bwa
title: liveStage-frame177.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/fmzjzy
title: liveStage-frame144.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/otj6rs
title: liveStage-frame3.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/sjx2ag
title: liveStage-frame181.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/ubihd5
title: liveStage-frame93.mat

description: :unav
DOI: 10.15490/fairdomhub.1.datafile.3407.1
title: /reports/SupplementaryTableS3

description: Supplementary Table S3 - Désirée biological evidence filtering results. Output of 1st filtering step by biological evidence for cv. Désirée. Layer _p_stRT/_I_STRT/_S_03_stCuSTr/_A_03.1_filtering/reports/
DOI: 10.7910/dvn/eqdkvy/9it1tc
title: liveStage-frame15.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/dussiq
title: liveStage-frame101.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/t4owuz
title: liveStage-frame29.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/yuydpi
title: liveStage-frame194.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/34cy1b
title: liveStage-frame45.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/g7eqel
title: liveStage-frame172.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/hrmpew
title: liveStage-frame171.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/japph9
title: liveStage-frame34.mat

description: :unav
DOI: 10.7910/dvn/ghcv1g/ggsija
title: ML100xZnS-Mn-cys_300ms_EM200-6bigMP-4.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/4ehvck
title: liveStage-frame197.mat

description: :unav
DOI: 10.7910/dvn/eqdkvy/5ezhlu
title: liveStage-frame100.mat

description: :unav