cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3539366
title: 2D STORM dataset U2OS Nup96-SNAP A647

description: 2D STORM dataset sample: U2OS Nup96-SNAP cells labelled with A647 imaging: STORM, COT buffer, pxlsize 106 nm Cells from: Thevathasan, J.V., Kahnwald, M., Cieśliński, K. <em>et al.</em> Nuclear pores as versatile reference standards for quantitative superresolution microscopy. <em>Nat Methods</em> <strong>16, </strong>1045–1053 (2019) doi:10.1038/s41592-019-0574-9
DOI: 10.5281/zenodo.3539367
title: 2D STORM dataset U2OS Nup96-SNAP A647

description: 2D STORM dataset sample: U2OS Nup96-SNAP cells labelled with A647 imaging: STORM, COT buffer, pxlsize 106 nm Cells from: Thevathasan, J.V., Kahnwald, M., Cieśliński, K. <em>et al.</em> Nuclear pores as versatile reference standards for quantitative superresolution microscopy. <em>Nat Methods</em> <strong>16, </strong>1045–1053 (2019) doi:10.1038/s41592-019-0574-9
DOI: 10.5281/zenodo.3539510
title: Data used for the paper Andres Parra-Puerto, Kai Ling NG, Kieran Fahy, Angela E Goode, Mary P. Ryan, and Anthony Kucernak Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER and Corrosion Resistance in Acid and Alkaline Electrolytes ACS Catalysis, 2019 DOI: 10.1021/acscatal.9b03359

description: The data in this spreadsheet was used to produce the figures in the paper Andres Parra-Puerto, Kai Ling NG, Kieran Fahy, Angela E Goode, Mary P. Ryan, and Anthony Kucernak Supported Transition Metal Phosphides: Activity Survey for HER, ORR, OER and Corrosion Resistance in Acid and Alkaline Electrolytes ACS Catalysis, 2019 DOI: 10.1021/acscatal.9b03359Please cite the above reference if you wish to use this data
DOI: 10.17632/k784xkjg9k.2
title: HMCES Alt-EJ manuscript
DOI: 10.25345/c5kq2s
title: MassIVE MSV000084573 - ExpeAissatouMetabo-C18Neg pool MS/MS
title: serum humains extraction metabolomique C18 Neg Pool MS/MS
DOI: 10.25919/5dcb57548daae
title: Parkes observations for project P958 semester 2019OCTS_24

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last two year searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 31 bursts, five of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.5281/zenodo.3538222
title: The Consonant Challenge Corpus

description: The Consonant Challenge Corpus provides a dataset to support human-machine comparisons of consonant recognition in quiet and noise. Twelve female and 12 male native English talkers contributed to the corpus. All speakers produced each of the 24 English consonants / b, d, g, p, t, k, s, ʃ, f, v, ð, θ, ʧ, z, ʒ, h, ʤ, m, n, ŋ, w, r, j, l / in nine vowel contexts consisting of all possible combinations of the three vowels / iː / (as in “beat”), / uː / (as in “boot”), and / æ / (as in “bat”). Each VCV was produced using both front and end stress (e.g. / ‘æ b æ / vs / æ b ‘æ /) giving a total of 24 (speakers) * 24 (consonants) * 2 (stress types) * 9 (vowel contexts) = 10368 tokens. Tokens are distributed into training, development and test sets for the purposes of automatic speech recognition experiments. The Consonant Challenge is described in this article: Cooke, M., Scharenborg, O. (2008), “The Interspeech 2008 Consonant Challenge”, Proceedings of Interspeech, Brisbane, Australia, September 2008. The distribution consists of the following elements: Technical description: <em>readme</em> Speech/noise waveforms <em>train.zip</em> contains noise-free training data <em>test.zip</em> contains the 7 test sets as well as practice items for perceptual tests, and MATLAB format files containing offsets identifying the time location of the speech token within the mixture <em>test_binaural.zip</em> contains 2-channel wavs with the speech and noise on separate channels (left=noise, right=speech), for test sets 2-7 (test set 1 is noise-free) <em>dev.zip</em> development set <em>dev_binaural.zip</em> is the 2-channel version of the development set Phoneme segmentation data <em>handsegm.91.mlf.txt:</em> 91 hand-segmented VCVs in HTK format. This set consists of at least three items per consonant in a context in which the first and the second vowel were identical, added to that were 19 randomly selected VCVs. <em>segmentation_training.mlf.txt:</em> automatically generated phoneme segmentation of the clean training material in HTK format <em>segmentation_testsets.zip</em>: zip file containing automatically generated phoneme segmentations of each test set in HTK format Automatic speech recognition <em>asr.zip</em> contais scripts and models
DOI: 10.5281/zenodo.3538223
title: The Consonant Challenge Corpus

description: The Consonant Challenge Corpus provides a dataset to support human-machine comparisons of consonant recognition in quiet and noise. Twelve female and 12 male native English talkers contributed to the corpus. All speakers produced each of the 24 English consonants / b, d, g, p, t, k, s, ʃ, f, v, ð, θ, ʧ, z, ʒ, h, ʤ, m, n, ŋ, w, r, j, l / in nine vowel contexts consisting of all possible combinations of the three vowels / iː / (as in “beat”), / uː / (as in “boot”), and / æ / (as in “bat”). Each VCV was produced using both front and end stress (e.g. / ‘æ b æ / vs / æ b ‘æ /) giving a total of 24 (speakers) * 24 (consonants) * 2 (stress types) * 9 (vowel contexts) = 10368 tokens. Tokens are distributed into training, development and test sets for the purposes of automatic speech recognition experiments. The Consonant Challenge is described in this article: Cooke, M., Scharenborg, O. (2008), “The Interspeech 2008 Consonant Challenge”, Proceedings of Interspeech, Brisbane, Australia, September 2008. The distribution consists of the following elements: Technical description: <em>readme</em> Speech/noise waveforms <em>train.zip</em> contains noise-free training data <em>test.zip</em> contains the 7 test sets as well as practice items for perceptual tests, and MATLAB format files containing offsets identifying the time location of the speech token within the mixture <em>test_binaural.zip</em> contains 2-channel wavs with the speech and noise on separate channels (left=noise, right=speech), for test sets 2-7 (test set 1 is noise-free) <em>dev.zip</em> development set <em>dev_binaural.zip</em> is the 2-channel version of the development set Phoneme segmentation data <em>handsegm.91.mlf.txt:</em> 91 hand-segmented VCVs in HTK format. This set consists of at least three items per consonant in a context in which the first and the second vowel were identical, added to that were 19 randomly selected VCVs. <em>segmentation_training.mlf.txt:</em> automatically generated phoneme segmentation of the clean training material in HTK format <em>segmentation_testsets.zip</em>: zip file containing automatically generated phoneme segmentations of each test set in HTK format Automatic speech recognition <em>asr.zip</em> contais scripts and models
DOI: 10.5281/zenodo.3539332
title: Replication Kit: "Skill Models for Programming Language Concepts"

description: <strong>Structure</strong> <strong>data</strong>: contains the data we used for our case study <strong>skillmodels</strong>: data sets generated from the raw data in the database <strong>raw</strong>: raw data collected in SmartAPE [1] containing the source code of the students as well as the assessment results of the system <strong>results</strong>: contains the complete results of our case study Results of AUC and RMSE for each meta-parametrization and each skill model in .csv and .Rda format boxplots of our AUC and RMSE distributions for each skill model and each meta-parameter in .pdf format calculation scripts: <strong>sm_trainer.R</strong>: script to fit different models for different meta-parametrizations and test them using different performance metrics. Uses <em>data/skillmodels </em>as input <strong>comparison.R</strong>: script that performs statistical tests to compare meta-parameters. Uses <em>results//results_pfa.Rda, results//results_afm.Rda, and results/results_prop.Rda</em> as input <strong>References</strong> [1] Albrecht, Ella et al. “Experiences in Introducing Blended Learning in an Introductory Programming Course.” <em>ECSEE</em> (2018).
DOI: 10.6084/m9.figshare.10292771.v2
title: CGPS, borehole strain and tilt data - 24 December 2018 eruptive intrusion Etna volcano

description: On the early morning of 24 December 2018, after two months of Strombolian activity, Mt Etna was suddenly penetrated by a violent dyke intrusion that opened an eruptive fissure and continued to propagate southward for more than ten hours.<br>In this collection are included raw CGPS data from 24 to 25 December 2018 (Zip archive). Moreover, borehole strain data are included for the 24 December (positive variation = compression; negative variation = dilatation). Finally, borehole tilt data (biaxial) for the 24 December are also included (negative variation = uplift).
DOI: 10.7910/dvn/h9x2ab/f7p5hx
title: 1percent_figure4.pdf
DOI: 10.7910/dvn/h9x2ab/sltpwu
title: yg_pamrp_poststrat_sample.csv.zip
DOI: 10.17632/gyn4brwjd7.1
title: Data for: A family-level floristic inventory and analysis of medicinal plants used in Traditional African Medicine

description: Data matrices used in regression analysis, Bayesian analysis and IDM analysis of the African flora and African medicinal flora
DOI: 10.5281/zenodo.3539563
title: Computational History of Philosophy of Science (Comp HOPOS) Dataset

description: The Computational History of Philosophy of Science (Comp HOPOS) aims to be a comprehensive set of article and (when available) book chapter metadata for philosophy of science. The dataset covers the full run of over 40 journals and 3 major book series in the field. An automated author disambiguation script is used to construct canonical names for each author, and a combination of gender attribution methods is used to attribute the gender of each author. The full code used to generate the dataset is available at https://github.com/dhicks/comp-HOPOS. See the file data_dictionary.txt for data dictionary and additional information.
DOI: 10.5281/zenodo.3539577
title: Preliminary Area Estimates of Forest Disturbances in the Country of Georgia by Region

description: Preliminary area estimates of forest disturbances in the country of Georgia by region from 1991 to 2018 using a modified version of the algorithm developed by (Bullock, Woodcock, & Olofsson, 2018). Unit is square kilometers. Reference: Bullock, E. L., Woodcock, C. E., & Olofsson, P. (2018). Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis. <em>Remote Sensing of Environment</em>, 110968.
DOI: 10.5281/zenodo.3539711
title: Pathway Figure OCR GMT

description: Sample Pathway Figure OCR results from 17 December 2018, condensed by PMCID into GMT format for use in gene set analysis. Columns are PMCID, URL, Entrez Gene IDs
DOI: 10.7910/dvn/h9x2ab/9rhgar
title: 1percent_figure3.pdf
DOI: 10.7910/dvn/h9x2ab/bv87jw
title: 1percent_figure1.pdf
DOI: 10.7910/dvn/h9x2ab/g5tjbb
title: 1percent_figure2.pdf
DOI: 10.7910/dvn/h9x2ab/mvqa9n
title: README.txt
DOI: 10.7910/dvn/h9x2ab/setye3
title: co99_d00.shx
DOI: 10.7910/dvn/h9x2ab/ve0lcg
title: 00_run_scripts.sh
DOI: 10.7910/dvn/h9x2ab/windov
title: 1percent_figure7.pdf
DOI: 10.7910/dvn/h9x2ab/xyucud
title: cmuss2.afm
DOI: 10.5281/zenodo.3539710
title: Pathway Figure OCR GMT

description: Sample Pathway Figure OCR results from 17 December 2018, condensed by PMCID into GMT format for use in gene set analysis. Columns are PMCID, URL, Entrez Gene IDs
DOI: 10.7910/dvn/bq9m2u/1v6jdh
title: blame_officials_dataset.tab

description: Data from survey with government officials
DOI: 10.7910/dvn/bq9m2u/gnhwqq
title: blame_citizens_dataset.tab

description: Data from survey with citizens
DOI: 10.7910/dvn/h9x2ab/8iq16b
title: 05_figures_5_to_7.R
DOI: 10.7910/dvn/h9x2ab/gljwz6
title: st99_d00.shx
DOI: 10.7910/dvn/h9x2ab/gsbzsp
title: 1percent_figure5.pdf
DOI: 10.7910/dvn/h9x2ab/mjqhvs
title: table1.pdf
DOI: 10.7910/dvn/h9x2ab/uqqqvo
title: postgresql_installation_instructions.txt
DOI: 10.7910/dvn/h9x2ab/vy2kmw
title: yg_pamrp_state2012results.txt
DOI: 10.7910/dvn/h9x2ab/w2s3dl
title: co99_d00.shp
DOI: 10.5281/zenodo.3539239
title: BackFlow: Backward Context-sensitive Flow Reconstruction of Taint Analysis Results

description: This archive contains the details of the experimental results reported in the VMCAI 2020 paper, and instruction on how to reproduce these results.
DOI: 10.5281/zenodo.3539240
title: BackFlow: Backward Context-sensitive Flow Reconstruction of Taint Analysis Results

description: This archive contains the details of the experimental results reported in the VMCAI 2020 paper, and instruction on how to reproduce these results.
DOI: 10.7910/dvn/bq9m2u/1pyxpb
title: 1_Prep.do

description: Code preparing the main dataset (blame_analysis.dta)
DOI: 10.7910/dvn/bq9m2u/4po47m
title: blame_pilotdata.tab

description: Data from pilot survey with citizens
DOI: 10.7910/dvn/bq9m2u/6nnlrx
title: Codebook.pdf

description: Codebook
DOI: 10.7910/dvn/bq9m2u/kqfbfe
title: blame_analysis.tab

description: Main dataset
DOI: 10.7910/dvn/bq9m2u/y5ukdj
title: 2_Analysis.do

description: Code for analysis
DOI: 10.7910/dvn/h9x2ab/jk61tw
title: yg_pamrp_surveydata.txt
DOI: 10.7910/dvn/h9x2ab/sao51g
title: yg_pamrp_fips2012results.txt
DOI: 10.7910/dvn/h9x2ab/tnf8kj
title: cmuss4.afm
DOI: 10.7910/dvn/m046sb/1wjuqa
title: chk_lin_2007_sumvis_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/8wyvxt
title: chk_lin_2007_wkvis_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/iwva6l
title: combined.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/omsrq7
title: MEplot_panel_permret_pan_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/thr6v4
title: MEplot_2007_allret_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/wnlrg4
title: MEplot_2007_wkvis_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/x7zvm1
title: chk_lin_2007_allret_bw.pdf

description: :unav
DOI: 10.7910/dvn/bq9m2u/qugaqi
title: readme.txt

description: Readme file describing the replication material
DOI: 10.7910/dvn/h9x2ab/4wqxjh
title: 04_figures_1_to_4.R
DOI: 10.7910/dvn/h9x2ab/focxve
title: 1percent_figure6.pdf
DOI: 10.7910/dvn/h9x2ab/h5qmne
title: co99_d00.dbf
DOI: 10.7910/dvn/h9x2ab/ije5nk
title: 02_score_in_database.R
DOI: 10.7910/dvn/h9x2ab/lnokly
title: cmuss1.afm
DOI: 10.7910/dvn/h9x2ab/y4rupw
title: 03_county_intercept_correction.R
DOI: 10.7910/dvn/m046sb/13v6u7
title: hist_sumvis_2007_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/2ygyq9
title: loess_pan_permret_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/7b52pt
title: MEplot_2007_sumvis_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/eykp7c
title: loess_2007_wkvis_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/gvssdo
title: loess_2007_sumvis_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/qirvlu
title: chk_lin_pan_permret_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/usnkz7
title: loess_2007_allret_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/yddt6n
title: hist_wkvis_2007_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/yjt9lp
title: hist_allret_2007_bw.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/znpuyi
title: hist_permret_2007_bw.pdf

description: :unav
DOI: 10.17632/94xy39t47x.1
title: Data for: Metabolic expenditure and feeding performance in hatchling yellow pond turtles (Mauremys mutica) from different incubation temperatures

description: Body mass, metabolic rate, food intake and digestive efficiency of Asian yellow pond turtles in the first two months after hatching
DOI: 10.17632/r992j24765.1
title: Data for: C/D ratio in long term renal function.

description: The datas use for C/D ratio in long term renal function
DOI: 10.5281/zenodo.3539128
title: Simulations of a DPPC bilayer at different temperatures

description: Simulations aimed to estimate the melting point of pure DPPC membranes in the Slipids force field. Simulations performed at 300, 305, and 310 K. The simulations were 500 ns (300 K & 305 K) or 300 ns (310 K) long. Full details of simulations available in the related manuscript, where they are also carefully analyzed: Nanoscale Membrane Domain Formation Driven by Cholesterol by Matti Javanainen, Hector Martinez-Seara, and Ilpo Vattulainen Scientific Reports 7: 1143 (2017)
DOI: 10.5281/zenodo.3539129
title: Simulations of a DPPC bilayer at different temperatures

description: Simulations aimed to estimate the melting point of pure DPPC membranes in the Slipids force field. Simulations performed at 300, 305, and 310 K. The simulations were 500 ns (300 K & 305 K) or 300 ns (310 K) long. Full details of simulations available in the related manuscript, where they are also carefully analyzed: Nanoscale Membrane Domain Formation Driven by Cholesterol by Matti Javanainen, Hector Martinez-Seara, and Ilpo Vattulainen Scientific Reports 7: 1143 (2017)
DOI: 10.7910/dvn/h9x2ab/czzfoq
title: st99_d00.shp
DOI: 10.7910/dvn/h9x2ab/o6wwap
title: 01_stan_model.R
DOI: 10.7910/dvn/h9x2ab/s6jbgq
title: cmuss3.afm
DOI: 10.7910/dvn/h9x2ab/tzlvke
title: table2.pdf
DOI: 10.7910/dvn/h9x2ab/wawske
title: 2012_exit_polls.tab
DOI: 10.7910/dvn/h9x2ab/xrhhfh
title: st99_d00.dbf
DOI: 10.7910/dvn/m046sb/2w6kwu
title: tableA4.tex

description: :unav
DOI: 10.7910/dvn/m046sb/4hmoxl
title: tableA3.tex

description: :unav
DOI: 10.7910/dvn/m046sb/8cngmu
title: tableA7.tex

description: :unav
DOI: 10.7910/dvn/m046sb/ppbn1e
title: tableA6.tex

description: :unav
DOI: 10.7910/dvn/m046sb/qdm88p
title: tableA5.tex

description: :unav
DOI: 10.7910/dvn/m046sb/sdd9q0
title: table3.tex

description: :unav
DOI: 10.7910/dvn/m046sb/shco6y
title: tableA2.tex

description: :unav
DOI: 10.7910/dvn/m046sb/vdpjv7
title: summary_stats.log

description: :unav
DOI: 10.7910/dvn/m046sb/3jyovx
title: lebanon-major-cities.tab

description: :unav
DOI: 10.7910/dvn/m046sb/gntngt
title: lebanon-panel.tab

description: :unav
DOI: 10.7910/dvn/m046sb/ngz1tw
title: lebanon-return.tab

description: :unav
DOI: 10.7910/dvn/m046sb/qafe14
title: lebanon-geocoded-all.tab

description: :unav
DOI: 10.7910/dvn/m046sb/qdee2o
title: codebook.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/quzmou
title: lebanon-mapdata.tab

description: :unav
DOI: 10.7910/dvn/m046sb/qwwr9c
title: table_violence.tex

description: :unav
DOI: 10.7910/dvn/m046sb/rlgy14
title: main-analysis.do

description: :unav
DOI: 10.7910/dvn/m046sb/xdmsaq
title: Camarena_Hagerdal_Excerpts_2017.pdf

description: :unav
DOI: 10.7910/dvn/ff3dqm/elprdr
title: prediction_code_COM.R

description: Uses trained machine learning classifiers to produce predicted delegation and constraint ratios for the EU Commission.
DOI: 10.7910/dvn/ff3dqm/f1rnuk
title: ratios_delegation_constraint_FINAL_MS.R

description: Code producing predicted EU Member State delegation/constraint/discretion ratios for EU regulations and directives.
DOI: 10.7910/dvn/ff3dqm/gpup9g
title: predicted-v-actual-ratios_COM.R

description: Produces plots for predicted vs. actual delegation/constraint/discretion ratios for EU Commission estimates.
DOI: 10.7910/dvn/ff3dqm/ojpdyv
title: ratios_delegation_constraint_COM.R

description: Code producing predicted EU Commission delegation/constraint/discretion ratios for EU regulations and directives.
DOI: 10.7910/dvn/ff3dqm/ysmn0v
title: prediction_code_MS.R

description: Uses trained machine learning classifiers to produce predicted delegation and constraint ratios for EU Member States.
DOI: 10.7910/dvn/m046sb/hydrxk
title: ILDES_Report_Survey_2004.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/ivllka
title: Camarena_Hagerdal_Interview_Protocol_1.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/jkpcfb
title: guide-data-replication.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/kzxies
title: Camarena_Hagerdal_Interview_Protocol_2.pdf

description: :unav
DOI: 10.7910/dvn/m046sb/u5alxr
title: ts-price-return.tab

description: :unav
DOI: 10.7910/dvn/m046sb/v1dmew
title: readme.txt

description: :unav
DOI: 10.7910/dvn/m046sb/yo5lul
title: time-series-bw.r

description: :unav
DOI: 10.7910/dvn/ff3dqm/bjn2vn
title: analysis_FINAL_com.R

description: Produces discontinuity plots from coded provisions and predicted probabilities from European Union Commission estimates.
DOI: 10.7910/dvn/ff3dqm/cngj2j
title: analysis_FINAL_ms.R

description: Produces discontinuity plots from coded provisions and predicted probabilities from European Union Member State estimates.
DOI: 10.7910/dvn/ff3dqm/ebkttw
title: predicted-v-actual-ratios_MS.R

description: Produces plots for predicted vs. actual delegation/constraint/discretion ratios for EU Member States estimates.
DOI: 10.7910/dvn/ff3dqm/qes52h
title: performance_code_MS.R

description: Code used to assess the performance of gradient boosted tree classifiers for EU Member State delegation and constraint.
DOI: 10.7910/dvn/ff3dqm/uk9mce
title: franchino_replication_data.tab

description: STATA DTA file to be used with "face validity.do"
DOI: 10.7910/dvn/ff3dqm/vpzxjq
title: face validity.do

description: STATA code for reproducing out of sample regressions replicating Franchino's original substantive results (Table 3).
DOI: 10.7910/dvn/ff3dqm/z9qkq8
title: performance_code_COM.R

description: Code used to assess the performance of gradient boosted tree classifiers for EU Commission delegation and constraint.