cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13273295
title: Training set for nuclei classification

description: An R object (list) containing two matrices : (1) a tensor containing brightfield cell patches (2) A matrix containing image annotations.<br>
DOI: 10.6084/m9.figshare.13273295.v1
title: Training set for nuclei classification

description: An R object (list) containing two matrices : (1) a tensor containing brightfield cell patches (2) A matrix containing image annotations.<br>
DOI: 10.6084/m9.figshare.13273355
title: Keras trained UNET model

description: HDF5 file representing the trained UNET network for nuclear regions segmentation in brightfield images.<br>
DOI: 10.6084/m9.figshare.13274699.v1
title: Figure 4

description: Fracture surface roughness<br>
DOI: 10.6084/m9.figshare.13274699
title: Figure 4

description: Fracture surface roughness<br>
DOI: 10.6084/m9.figshare.13274528
title: Keras trained DCNN model

description: HDF5 file representing the trained DCNN for brightfield nuclear images classification.
DOI: 10.6084/m9.figshare.13274528.v1
title: Keras trained DCNN model

description: HDF5 file representing the trained DCNN for brightfield nuclear images classification.
DOI: 10.6084/m9.figshare.13274723
title: Figure 5

description: Recurrent times of test and Parkfield<br>
DOI: 10.6084/m9.figshare.13274723.v1
title: Figure 5

description: Recurrent times of test and Parkfield<br>
DOI: 10.6084/m9.figshare.13274666
title: Supplementary Material for: Total Platelet Transcriptomics and Its Network Analysis by RNA-Seq and miRNA-Seq and PCA Application in Essential Thrombocythaemia

description: Differentiating the aetiology of thrombocytosis is limited yet crucial in patients with essential thrombocythaemia (ET). MicroRNAs (miRNAs) regulate haematopoiesis and lineage commitment; aberrant expression of miRNAs plays an important role in myeloproliferative neoplasms. However, the miRNA profile has been poorly explored in ET patients compared to patients with reactive thrombocytosis (RT). A total of 9 samples, including 5 ET patient samples, 2 RT patient samples, and 2 healthy control samples, were analysed in this study. We produced 81.43 million reads from transcripts and 59.60 million reads from small RNAs. We generated a comprehensive miRNA-mRNA regulatory network and identified unique 14 miRNA expression patterns associated with ET. Among the 14 miRNAs, miR-1268a was downregulated in ET and showed an inverse correlation with its 8 putative target genes, including genes associated with thrombus formation and platelet activation (<i>CDH6, EHD2, FUT1, KIF26A, LINC00346, PTPRN, SERF1A,</i> and <i>SLC6A9</i>). Principal component analysis (PCA) showed ET and non-ET groups well clustered in space, suggesting each group had a distinctive expression pattern of mRNAs and miRNAs. These results suggest that the significant dysregulation of miR-1268a and its 8 target genes could be a unique expression of platelet mi­RNAs and miRNA/mRNA regulatory network in ET patients.
DOI: 10.6084/m9.figshare.13274666.v1
title: Supplementary Material for: Total Platelet Transcriptomics and Its Network Analysis by RNA-Seq and miRNA-Seq and PCA Application in Essential Thrombocythaemia

description: Differentiating the aetiology of thrombocytosis is limited yet crucial in patients with essential thrombocythaemia (ET). MicroRNAs (miRNAs) regulate haematopoiesis and lineage commitment; aberrant expression of miRNAs plays an important role in myeloproliferative neoplasms. However, the miRNA profile has been poorly explored in ET patients compared to patients with reactive thrombocytosis (RT). A total of 9 samples, including 5 ET patient samples, 2 RT patient samples, and 2 healthy control samples, were analysed in this study. We produced 81.43 million reads from transcripts and 59.60 million reads from small RNAs. We generated a comprehensive miRNA-mRNA regulatory network and identified unique 14 miRNA expression patterns associated with ET. Among the 14 miRNAs, miR-1268a was downregulated in ET and showed an inverse correlation with its 8 putative target genes, including genes associated with thrombus formation and platelet activation (<i>CDH6, EHD2, FUT1, KIF26A, LINC00346, PTPRN, SERF1A,</i> and <i>SLC6A9</i>). Principal component analysis (PCA) showed ET and non-ET groups well clustered in space, suggesting each group had a distinctive expression pattern of mRNAs and miRNAs. These results suggest that the significant dysregulation of miR-1268a and its 8 target genes could be a unique expression of platelet mi­RNAs and miRNA/mRNA regulatory network in ET patients.
DOI: 10.25345/c58v2c
title: MassIVE MSV000086504 - GNPS-the mass spectrum of sheng-q-65 PT of chenrui
title: This is the mass spectrum data of a plant extract.
DOI: 10.5281/zenodo.3941996
title: Supporting Information: Environmental benefits of large-scale second-generation bioethanol production in the EU: An integrated supply chain network optimization and Life Cycle Assessment approache

description: This supporting information provides all input data of the model, the assumptions made and the literature and database references for the publication<em> Environmental benefits of large-scale second-generation bioethanol production in the EU: An integrated supply chain network optimization and Life Cycle Assessment approache</em>. The environmental data is based on life cycle assessments, with full information on the life cycle inventory and the results of the life cycle impact assessment based on the ReCiPe method. It also includes detailed results for all objective functions in all scenarios (optimization of 18 midpoints, 3 endpoints, and economic optimization in 5 tax scenarios and 2 feedstock scenarios), and detailed results of the sensitivity analysis and Pareto optimization.
DOI: 10.14459/2020mp1554803
title: SEN12MS-CR

description: The SEN12MS-CR dataset contains 122,218 patch triplets of corresponding Sentinel-1 dual-pol SAR data, Sentinel-2 multi-spectral images, and cloud-covered Sentinel-2 multi-spectral images. The patches are distributed across the land masses of the Earth and spread over all four meteorological seasons. This is reflected by the dataset structure. All patches are provided in the form of 16-bit GeoTiffs containing the following specific information:<br> - Sentinel-1 SAR: 2 channels corresponding to sigma nought backscatter values in dB scale for VV and VH polarization. - Sentinel-2 multi-Spectral: 13 channels corresponding to the 13 spectral bands (B1, B2, B3, B4, B5, B6, B7, B8, B8a, B9, B10, B11, B12). - cloud-covered Sentinel-2 multi-Spectral: 13 channels corresponding to the 13 spectral bands (B1, B2, B3, B4, B5, B6, B7, B8, B8a, B9, B10, B11, B12).
DOI: 10.13011/m3-rcyy-gx13
title: PRISMS-Fatigue Data
DOI: 10.6084/m9.figshare.13274771
title: Supplementary Material for: Imaging Identification of Rapidly Progressing Autosomal Dominant Polycystic Kidney Disease: Simple Eligibility Criterion for Tolvaptan

description: <b><i>Background:</i></b> Tolvaptan was approved for the treatment of autosomal dominant polycystic kidney disease (ADPKD). However, the official indication of “rapidly progressive disease” is described differently in the clinical guidelines. We aim to define “rapidly progressive disease” by risk of ESRD, which is evaluated using estimated height-adjusted total kidney volume (HtTKV) growth rate. <b><i>Methods:</i></b> The risk of ESRD was retrospectively analyzed in 617 initially non-ESRD adults with ADPKD and observed with standard of care between 2007 and 2018. The estimated annual growth rate of the HtTKV, termed as eHTKV-α (%/year), is derived from the following equation: [HtTKV at age <i>t</i>] = K(1 + eHTKV-α/100)<sup><i>t</i></sup>, where <i>K</i> = 150 mL/m is used in Mayo Imaging Classification and <i>K</i> = 130 mL/m is proposed for individually stable eHTKV-α value from baseline. The accuracy of eHTKV-α to predict ESRD for censored ages was analyzed using time-dependent receiver-operating characteristic curves (ROC). The cutoff point of initially measured eHTKV-α to predict ESRD was assessed using Kaplan-Meier and Cox’s proportional hazards models. Performance characteristics of the cutoff point for censored ages were calculated using time-dependent ROC and validated by the bootstrap method. <b><i>Results:</i></b> The area under the time-dependent ROC of eHTKV-α to predict ESRD at age 65 was 0.89 ± 0.04 (<i>K</i> = 130). The mean renal survival was less than 70 years at eHTKV-α ≥4.0%/year (<i>K</i> = 130). Mean renal survival was approximately 12 years shorter, and hazard ratio of ESRD was more than 5-time higher at this cutoff point than at lower point. Time-dependent sensitivity for age 65 and cutoff point of 4.0%/year (<i>K</i> = 130) was 93.4 ± 0.3%. Between cutoff points ≥4.0%/year (<i>K</i> = 130) and ≥3.5%/year (<i>K</i> = 150), there was no significant difference in performance characteristics and accuracy to predict ESRD. <b><i>Conclusion:</i></b> eHTKV-α well predicts ESRD. Initially, measured eHTKV-α ≥4.0%/year (<i>K</i> = 130) defines high-risk ESRD. Without additional conditions, a single eHTKV-α cutoff point identifies subjects that are most likely to benefit from tolvaptan.
DOI: 10.6084/m9.figshare.13103013
title: A block bootstrap for quasi-likelihood in sparse functional data

description: This article utilizes bootstrap quasi-likelihood (QL) to model sparse functional data. The proposed method combines parallel block bootstrap and QL to fit the functional data. The parameter space is considered as a finite-dimensional space through a certain optimization rule. Statistical errors of the proposed method are discussed. Some asymptotic properties of the method are established under several mild conditions as well. Several simulations are conducted to examine the finite-sample performance of the method. The performance is also demonstrated by analysing real data.
DOI: 10.6084/m9.figshare.13103013.v1
title: A block bootstrap for quasi-likelihood in sparse functional data

description: This article utilizes bootstrap quasi-likelihood (QL) to model sparse functional data. The proposed method combines parallel block bootstrap and QL to fit the functional data. The parameter space is considered as a finite-dimensional space through a certain optimization rule. Statistical errors of the proposed method are discussed. Some asymptotic properties of the method are established under several mild conditions as well. Several simulations are conducted to examine the finite-sample performance of the method. The performance is also demonstrated by analysing real data.
DOI: 10.6084/m9.figshare.13123285
title: Xylanolytic and Ethanologenic Potential of Gut Associated Yeasts from Different Species of Termites from India

description: Xylophagous termites are capable of degrading lignocellulose by symbiotic gut microorganisms along with the host’s indigenous enzymes. Therefore, the termite gut might be a potential niche to obtain natural yeasts with celluloytic, xylanolytic and ethanologenic traits required for bioethanol production from lignocellulosic biomass. In this study, we cultured 79 yeasts from three different termites <i>viz. Coptotermes heimi</i>, <i>Odontotermes javanicus</i> and <i>Odontotermes obesus</i>. After suitable screening methods, we identified 53 yeasts, which belonged to 10 genera and 16 different species of both ascomycetous and basidiomycetous yeasts. Most yeasts in the present study represent their first-ever isolation from the termite gut. Representative strains of identified yeasts were evaluated for their cellulolytic, xylanolytic, and ethanologenic abilities. None of the isolates showed cellulase activity; 22 showed xylanolytic activity, while six produced substantial quantities of ethanol. Among xylanolytic cultures, <i>Pseudozyma hubeiensis</i> STAG 1.7 and <i>Hannaella pagnoccae</i> STAG 1.14 produced 1.31 and 1.17 IU of xylanase. Among ethanologenic yeasts, the strains belonging to genera <i>Candida</i> and <i>Kodamaea</i> produced high amount of ethanol. Overall, highest ethanol level of 4.42 g/L was produced by <i>Candida tropicalis</i> TS32 using 1% glucose, which increased up to 22.92 g/L at 35 °C, pH 4.5 with 5% glucose. Fermentation of rice straw hydrolysate gave 8.95 g/l of ethanol with a yield of 0.42 g/g using the strain TS32. Our study highlights the gut of wood-feeding termites as a potential source of diverse yeasts that would be useful in the production of xylanase and bioethanol.
DOI: 10.6084/m9.figshare.13123285.v1
title: Xylanolytic and Ethanologenic Potential of Gut Associated Yeasts from Different Species of Termites from India

description: Xylophagous termites are capable of degrading lignocellulose by symbiotic gut microorganisms along with the host’s indigenous enzymes. Therefore, the termite gut might be a potential niche to obtain natural yeasts with celluloytic, xylanolytic and ethanologenic traits required for bioethanol production from lignocellulosic biomass. In this study, we cultured 79 yeasts from three different termites <i>viz. Coptotermes heimi</i>, <i>Odontotermes javanicus</i> and <i>Odontotermes obesus</i>. After suitable screening methods, we identified 53 yeasts, which belonged to 10 genera and 16 different species of both ascomycetous and basidiomycetous yeasts. Most yeasts in the present study represent their first-ever isolation from the termite gut. Representative strains of identified yeasts were evaluated for their cellulolytic, xylanolytic, and ethanologenic abilities. None of the isolates showed cellulase activity; 22 showed xylanolytic activity, while six produced substantial quantities of ethanol. Among xylanolytic cultures, <i>Pseudozyma hubeiensis</i> STAG 1.7 and <i>Hannaella pagnoccae</i> STAG 1.14 produced 1.31 and 1.17 IU of xylanase. Among ethanologenic yeasts, the strains belonging to genera <i>Candida</i> and <i>Kodamaea</i> produced high amount of ethanol. Overall, highest ethanol level of 4.42 g/L was produced by <i>Candida tropicalis</i> TS32 using 1% glucose, which increased up to 22.92 g/L at 35 °C, pH 4.5 with 5% glucose. Fermentation of rice straw hydrolysate gave 8.95 g/l of ethanol with a yield of 0.42 g/g using the strain TS32. Our study highlights the gut of wood-feeding termites as a potential source of diverse yeasts that would be useful in the production of xylanase and bioethanol.
DOI: 10.5281/zenodo.4287093
title: Table S1

description: Supplementary file
DOI: 10.5281/zenodo.4287092
title: Table S1

description: Supplementary file
DOI: 10.25387/g3.13180655.v1
title: Supplemental Material for Huang et al., 2020

description: Mutations identified in the 15 mutants are provided in four supplementary files and supplementary tables. Raw INDELs and raw SNPs are presented in “Supplementary File 1” and “Supplementary File 2” respectively. Filtered INDELs and SNPs are shown in “Supplementary File 3” and “Supplementary File 4”. The numbers of mutations identified in different region after the filtration are provided in “Supplementary Table 1”. Genes selected to conduct gene knock-out are provided in “Supplementary Table 2”. Genes where mutations were identified are provided in “Supplementary Table 3”.
DOI: 10.25387/g3.13180655
title: Supplemental Material for Huang et al., 2020

description: Mutations identified in the 15 mutants are provided in four supplementary files and supplementary tables. Raw INDELs and raw SNPs are presented in “Supplementary File 1” and “Supplementary File 2” respectively. Filtered INDELs and SNPs are shown in “Supplementary File 3” and “Supplementary File 4”. The numbers of mutations identified in different region after the filtration are provided in “Supplementary Table 1”. Genes selected to conduct gene knock-out are provided in “Supplementary Table 2”. Genes where mutations were identified are provided in “Supplementary Table 3”.
DOI: 10.5281/zenodo.4287086
title: Antimicrobial defence mechanisms of Heliothis subflexa

description: Master project data on the antimicrobial defence mechanisms of <em>Heliothis subflexa.</em>
DOI: 10.5281/zenodo.4287085
title: Antimicrobial defence mechanisms of Heliothis subflexa

description: Master project data on the antimicrobial defence mechanisms of <em>Heliothis subflexa.</em>
DOI: 10.6084/m9.figshare.13233353.v1
title: Han R. et al. 2020.rar

description: Extract the compressed file and there are two files:<br>The file of 'statistical data' includes the database that was used to statistical analysis;<br>The file of 'graphic data' includes all values used to build graphs, including means, standard error of mean and other measures reported.
DOI: 10.21227/q1fg-0h34
title: MATLAB code to test SPIM approach for SS

description: MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U. Compare the errors in detection for the SPIM method and the energy detector. Plot the values of the TPR FPR rates to calculate the ROC curve. Determines the MAD PDF of the instantaneous phase and the average instantaneous amplitude for comparison.
DOI: 10.18430/m37kok
title: X-ray diffraction data for the the crystal structure of Papain-Like Protease of SARS CoV-2, C111S mutant, in complex with PLP_Snyder496 (7KOK)
DOI: 10.18430/m37kol
title: X-ray diffraction data for the the crystal structure of Papain-Like Protease of SARS CoV-2 in complex with PLP_Snyder496 (7KOL)
DOI: 10.18430/m37koj
title: X-ray diffraction data for the the crystal structure of Papain-Like Protease of SARS CoV-2, C111S mutant, in complex with PLP_Snyder494 (7KOJ)
DOI: 10.5517/ccdc.csd.cc23wgs8
title: CCDC 1962820: Experimental Crystal Structure Determination

description: Related Article: Joseph Zsombor-Pindera, Farshid Effaty, Léon Escomel, Brian Patrick, Pierre Kennepohl, Xavier Ottenwaelder|2020|J.Am.Chem.Soc.|142|19023|doi:10.1021/jacs.0c09300
DOI: 10.5517/ccdc.csd.cc23wgt9
title: CCDC 1962821: Experimental Crystal Structure Determination

description: Related Article: Joseph Zsombor-Pindera, Farshid Effaty, Léon Escomel, Brian Patrick, Pierre Kennepohl, Xavier Ottenwaelder|2020|J.Am.Chem.Soc.|142|19023|doi:10.1021/jacs.0c09300
DOI: 10.5517/ccdc.csd.cc23wgvb
title: CCDC 1962822: Experimental Crystal Structure Determination

description: Related Article: Joseph Zsombor-Pindera, Farshid Effaty, Léon Escomel, Brian Patrick, Pierre Kennepohl, Xavier Ottenwaelder|2020|J.Am.Chem.Soc.|142|19023|doi:10.1021/jacs.0c09300
DOI: 10.5517/ccdc.csd.cc23wj0k
title: CCDC 1962858: Experimental Crystal Structure Determination

description: Related Article: Joseph Zsombor-Pindera, Farshid Effaty, Léon Escomel, Brian Patrick, Pierre Kennepohl, Xavier Ottenwaelder|2020|J.Am.Chem.Soc.|142|19023|doi:10.1021/jacs.0c09300
DOI: 10.5517/ccdc.csd.cc23wj1l
title: CCDC 1962859: Experimental Crystal Structure Determination

description: Related Article: Joseph Zsombor-Pindera, Farshid Effaty, Léon Escomel, Brian Patrick, Pierre Kennepohl, Xavier Ottenwaelder|2020|J.Am.Chem.Soc.|142|19023|doi:10.1021/jacs.0c09300
DOI: 10.5517/ccdc.csd.cc24yfg0
title: CCDC 1994492: Experimental Crystal Structure Determination

description: Related Article: Silvia Gutiérrez-Tarriño, José Luis Olloqui-Sariego, Juan José Calvente, Guillermo Mínguez Espallargas, Fernando Rey, Avelino Corma, Pascual Oña-Burgos|2020|J.Am.Chem.Soc.|142|19198|doi:10.1021/jacs.0c08882
DOI: 10.5517/ccdc.csd.cc23ytc8
title: CCDC 1965070: Experimental Crystal Structure Determination

description: Related Article: Jiangnan Li, Zhengyang Zhou, Xue Han, Xinran Zhang, Yong Yan, Weiyao Li, Gemma L. Smith, Yongqiang Cheng, Laura J. MCormick MPherson, Simon J. Teat, Mark D. Frogley, Svemir Rudić, Anibal J. Ramirez-Cuesta, Alexander J. Blake, Junliang Sun, Martin Schröder, Sihai Yang|2020|J.Am.Chem.Soc.|142|19189|doi:10.1021/jacs.0c08794
DOI: 10.5517/ccdc.csd.cc23ytd9
title: CCDC 1965071: Experimental Crystal Structure Determination

description: Related Article: Jiangnan Li, Zhengyang Zhou, Xue Han, Xinran Zhang, Yong Yan, Weiyao Li, Gemma L. Smith, Yongqiang Cheng, Laura J. MCormick MPherson, Simon J. Teat, Mark D. Frogley, Svemir Rudić, Anibal J. Ramirez-Cuesta, Alexander J. Blake, Junliang Sun, Martin Schröder, Sihai Yang|2020|J.Am.Chem.Soc.|142|19189|doi:10.1021/jacs.0c08794
DOI: 10.5517/ccdc.csd.cc23ytfb
title: CCDC 1965072: Experimental Crystal Structure Determination

description: Related Article: Jiangnan Li, Zhengyang Zhou, Xue Han, Xinran Zhang, Yong Yan, Weiyao Li, Gemma L. Smith, Yongqiang Cheng, Laura J. MCormick MPherson, Simon J. Teat, Mark D. Frogley, Svemir Rudić, Anibal J. Ramirez-Cuesta, Alexander J. Blake, Junliang Sun, Martin Schröder, Sihai Yang|2020|J.Am.Chem.Soc.|142|19189|doi:10.1021/jacs.0c08794
DOI: 10.6084/m9.figshare.13274840
title: Table 1.txt

description: Sticking coefficients of H2O and CO2 at increasing exposure (and the mean values) on various substrates<br>
DOI: 10.5517/ccdc.csd.cc23ytgc
title: CCDC 1965073: Experimental Crystal Structure Determination

description: Related Article: Jiangnan Li, Zhengyang Zhou, Xue Han, Xinran Zhang, Yong Yan, Weiyao Li, Gemma L. Smith, Yongqiang Cheng, Laura J. MCormick MPherson, Simon J. Teat, Mark D. Frogley, Svemir Rudić, Anibal J. Ramirez-Cuesta, Alexander J. Blake, Junliang Sun, Martin Schröder, Sihai Yang|2020|J.Am.Chem.Soc.|142|19189|doi:10.1021/jacs.0c08794
DOI: 10.5517/ccdc.csd.cc25x9jy
title: CCDC 2023200: Experimental Crystal Structure Determination

description: Related Article: Wenchao Chen, Esther Cai Xia Ang, Siu Min Tan, Zhijie Chua, Jingyun Ren, Ziqi Yang, Bo Teng, Richmond Lee, Haihua Lu, Choon-Hong Tan|2020|J.Am.Chem.Soc.|142|19065|doi:10.1021/jacs.0c00183
DOI: 10.5517/ccdc.csd.cc25xpsl
title: CCDC 2023580: Experimental Crystal Structure Determination

description: Related Article: Wenchao Chen, Esther Cai Xia Ang, Siu Min Tan, Zhijie Chua, Jingyun Ren, Ziqi Yang, Bo Teng, Richmond Lee, Haihua Lu, Choon-Hong Tan|2020|J.Am.Chem.Soc.|142|19065|doi:10.1021/jacs.0c00183
DOI: 10.5517/ccdc.csd.cc25zyrw
title: CCDC 2025749: Experimental Crystal Structure Determination

description: Related Article: Wenchao Chen, Esther Cai Xia Ang, Siu Min Tan, Zhijie Chua, Jingyun Ren, Ziqi Yang, Bo Teng, Richmond Lee, Haihua Lu, Choon-Hong Tan|2020|J.Am.Chem.Soc.|142|19065|doi:10.1021/jacs.0c00183
DOI: 10.5517/ccdc.csd.cc266hyw
title: CCDC 2032048: Experimental Crystal Structure Determination

description: Related Article: Xiaomin Shu, Leitao Huan, Qian Huang, Haohua Huo|2020|J.Am.Chem.Soc.|142|19058|doi:10.1021/jacs.0c10471
DOI: 10.5517/ccdc.csd.cc266j0z
title: CCDC 2032050: Experimental Crystal Structure Determination

description: Related Article: Xiaomin Shu, Leitao Huan, Qian Huang, Haohua Huo|2020|J.Am.Chem.Soc.|142|19058|doi:10.1021/jacs.0c10471
DOI: 10.5281/zenodo.291446
title: PsPM-SCRV6: Skin conductance responses to pain by electric stimulation

description: This dataset includes skin conductance response (SCR) measurements for each of 20 healthy unmedicated participants (10 males and 10 females aged 21.8+/-3.3 years) in response to 10 discomforting electric shocks. Stimuli are 0.5ms wide square current pulse repeated at 500Hz for 100ms. Amplitude is varied (mean +/- SD: 0.78mA +/- 0.43mA). ITI is selected randomly on each trial from 29s, 34s or 39s.
description: See the readme file for more detail. Data are stored as .mat files for use with MATLAB in a format readable by the PsPM toolbox (pspm.sourceforge.net). This research was supported by Wellcome Trust grants 098362/Z/12/Z and 098362/Z/12/Z, and Swiss National Science Foundation grant PA00A-117384.