cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34820/fk2/zv7gcd
title: Microfinance Institution, Financial Inclusiveness, Digitalization Technology and Macroeconomic Data

description: The data consists of the 709 Microfinance Institutions; infrastructure, clients, credit product, delinquency, outreach, balance sheet, income statement, financial performance, revenue and expenses, risk and liquidity, productivity and efficiency etc. Financial Development, Digitalized Technology, and Macroeconomic Data. Coverage of 73 countries worldwide from 1999 to 2018, 8558 observations, and 116 selected concepts/variables.
DOI: 10.6084/m9.figshare.19480706
title: Supplementary Material for: Outcome Prediction and Inter-Rater Comparison of Four Brain Magnetic Resonance Imaging Scoring Systems of Infants with Perinatal Asphyxia and Therapeutic Hypothermia

description: <b><i>Introduction:</i></b> The brain magnetic resonance imaging (MRI) result is a major predictor for the outcome of term infants with perinatal asphyxia who underwent therapeutic hypothermia. In daily practice, no uniform method is used to assess these images. <b><i>Purpose:</i></b> The aim of this study was to determine which MRI-score best predicts adverse outcome at 24 months of age and has the highest inter-rater reliability. <b><i>Methods:</i></b> Four MRI scoring systems for term infants with perinatal asphyxia were selected: Rutherford score, Trivedi score, Weeke score, and NICHD NRN score. Experienced blinded raters retrospectively evaluated the brain MR Images of 161 infants using all four scoring systems. Long-term outcome (the composite outcome death or adverse outcome, and its separate components) were routinely assessed by standardized testing at the age of 24 months. The predictive accuracy was assessed by logistic regression analyses and expressed as area under the ROC curve (AUC). The inter-rater reliability of the scores was calculated by the weighted Kappa or intraclass correlation. A sensitivity analysis using only high-quality MRI scans was performed. <b><i>Results:</i></b> All four MRI scoring systems demonstrated an AUC of &gt;0.66 for the prediction of adverse outcome and ≥0.80 for the prediction of death. The inter-rater reliability analyses demonstrated the highest reliability for the Weeke and Trivedi scores. When only assessing the high-quality scans, the AUC increased further. <b><i>Conclusion:</i></b> All four MRI brain scores proved reliable predictors for an adverse outcome at 24 months of age. The Weeke and Trivedi score demonstrated the highest inter-rater reliability. The use of high-quality MRI further improved prediction.
DOI: 10.6084/m9.figshare.19480706.v1
title: Supplementary Material for: Outcome Prediction and Inter-Rater Comparison of Four Brain Magnetic Resonance Imaging Scoring Systems of Infants with Perinatal Asphyxia and Therapeutic Hypothermia

description: <b><i>Introduction:</i></b> The brain magnetic resonance imaging (MRI) result is a major predictor for the outcome of term infants with perinatal asphyxia who underwent therapeutic hypothermia. In daily practice, no uniform method is used to assess these images. <b><i>Purpose:</i></b> The aim of this study was to determine which MRI-score best predicts adverse outcome at 24 months of age and has the highest inter-rater reliability. <b><i>Methods:</i></b> Four MRI scoring systems for term infants with perinatal asphyxia were selected: Rutherford score, Trivedi score, Weeke score, and NICHD NRN score. Experienced blinded raters retrospectively evaluated the brain MR Images of 161 infants using all four scoring systems. Long-term outcome (the composite outcome death or adverse outcome, and its separate components) were routinely assessed by standardized testing at the age of 24 months. The predictive accuracy was assessed by logistic regression analyses and expressed as area under the ROC curve (AUC). The inter-rater reliability of the scores was calculated by the weighted Kappa or intraclass correlation. A sensitivity analysis using only high-quality MRI scans was performed. <b><i>Results:</i></b> All four MRI scoring systems demonstrated an AUC of &gt;0.66 for the prediction of adverse outcome and ≥0.80 for the prediction of death. The inter-rater reliability analyses demonstrated the highest reliability for the Weeke and Trivedi scores. When only assessing the high-quality scans, the AUC increased further. <b><i>Conclusion:</i></b> All four MRI brain scores proved reliable predictors for an adverse outcome at 24 months of age. The Weeke and Trivedi score demonstrated the highest inter-rater reliability. The use of high-quality MRI further improved prediction.
DOI: 10.34820/fk2/omcuxw/dagicd
title: 2 dominant yellow.JPG

description: :unav
DOI: 10.34820/fk2/omcuxw/cvkdeg
title: 5 complementary color scheme.JPG

description: :unav
DOI: 10.34820/fk2/omcuxw/4iwfwy
title: 1 dominant white.JPG

description: :unav
DOI: 10.34820/fk2/omcuxw/z411x8
title: 3 monochrome color scheme.JPG

description: :unav
DOI: 10.34820/fk2/omcuxw/yrtuvh
title: 4 analog color scheme.JPG

description: :unav
DOI: 10.5517/ccdc.csd.cc28v74j
title: CCDC 2110577: Experimental Crystal Structure Determination

description: Related Article: Milan Klikar, Dimitris Georgiou, Ioannis Polyzos, Mihalis Fakis, Zdeňka Růžičková, Oldřich Pytela, Filip Bureš|2022|Dyes Pigm.|201|110230|doi:10.1016/j.dyepig.2022.110230
DOI: 10.5517/ccdc.csd.cc28v75k
title: CCDC 2110578: Experimental Crystal Structure Determination

description: Related Article: Milan Klikar, Dimitris Georgiou, Ioannis Polyzos, Mihalis Fakis, Zdeňka Růžičková, Oldřich Pytela, Filip Bureš|2022|Dyes Pigm.|201|110230|doi:10.1016/j.dyepig.2022.110230
DOI: 10.5517/ccdc.csd.cc28w83k
title: CCDC 2111568: Experimental Crystal Structure Determination

description: Related Article: Gopal Krushna Das Adhikari, Bedadyuti Vedvyas Pati, Tanmayee Nanda, Pragati Biswal, Shyam Kumar Banjare, Ponneri C. Ravikumar|2022|J.Org.Chem.|87|4438|doi:10.1021/acs.joc.1c02716
DOI: 10.5517/ccdc.csd.cc28x0v2
title: CCDC 2112304: Experimental Crystal Structure Determination

description: Related Article: Shunlin Zhang, Tianyu Xiao, Farzaneh Fadaei Tirani, Rosario Scopelliti, Mohammad Khaja Nazeeruddin, Dunru Zhu, Paul J. Dyson, Zhaofu Fei|2022|Inorg.Chem.|61|5010|doi:10.1021/acs.inorgchem.1c03862
DOI: 10.5517/ccdc.csd.cc28x0w3
title: CCDC 2112305: Experimental Crystal Structure Determination

description: Related Article: Shunlin Zhang, Tianyu Xiao, Farzaneh Fadaei Tirani, Rosario Scopelliti, Mohammad Khaja Nazeeruddin, Dunru Zhu, Paul J. Dyson, Zhaofu Fei|2022|Inorg.Chem.|61|5010|doi:10.1021/acs.inorgchem.1c03862
DOI: 10.5517/ccdc.csd.cc28x0x4
title: CCDC 2112306: Experimental Crystal Structure Determination

description: Related Article: Shunlin Zhang, Tianyu Xiao, Farzaneh Fadaei Tirani, Rosario Scopelliti, Mohammad Khaja Nazeeruddin, Dunru Zhu, Paul J. Dyson, Zhaofu Fei|2022|Inorg.Chem.|61|5010|doi:10.1021/acs.inorgchem.1c03862
DOI: 10.5517/ccdc.csd.cc28x0y5
title: CCDC 2112307: Experimental Crystal Structure Determination

description: Related Article: Shunlin Zhang, Tianyu Xiao, Farzaneh Fadaei Tirani, Rosario Scopelliti, Mohammad Khaja Nazeeruddin, Dunru Zhu, Paul J. Dyson, Zhaofu Fei|2022|Inorg.Chem.|61|5010|doi:10.1021/acs.inorgchem.1c03862
DOI: 10.5517/ccdc.csd.cc28x0z6
title: CCDC 2112308: Experimental Crystal Structure Determination

description: Related Article: Shunlin Zhang, Tianyu Xiao, Farzaneh Fadaei Tirani, Rosario Scopelliti, Mohammad Khaja Nazeeruddin, Dunru Zhu, Paul J. Dyson, Zhaofu Fei|2022|Inorg.Chem.|61|5010|doi:10.1021/acs.inorgchem.1c03862
DOI: 10.5517/ccdc.csd.cc28x108
title: CCDC 2112309: Experimental Crystal Structure Determination

description: Related Article: Shunlin Zhang, Tianyu Xiao, Farzaneh Fadaei Tirani, Rosario Scopelliti, Mohammad Khaja Nazeeruddin, Dunru Zhu, Paul J. Dyson, Zhaofu Fei|2022|Inorg.Chem.|61|5010|doi:10.1021/acs.inorgchem.1c03862
DOI: 10.5517/ccdc.csd.cc28x119
title: CCDC 2112310: Experimental Crystal Structure Determination

description: Related Article: Shunlin Zhang, Tianyu Xiao, Farzaneh Fadaei Tirani, Rosario Scopelliti, Mohammad Khaja Nazeeruddin, Dunru Zhu, Paul J. Dyson, Zhaofu Fei|2022|Inorg.Chem.|61|5010|doi:10.1021/acs.inorgchem.1c03862
DOI: 10.5517/ccdc.csd.cc28xvxz
title: CCDC 2113112: Experimental Crystal Structure Determination

description: Related Article: Gopal Krushna Das Adhikari, Bedadyuti Vedvyas Pati, Tanmayee Nanda, Pragati Biswal, Shyam Kumar Banjare, Ponneri C. Ravikumar|2022|J.Org.Chem.|87|4438|doi:10.1021/acs.joc.1c02716
DOI: 10.5517/ccdc.csd.cc28yw8d
title: CCDC 2114084: Experimental Crystal Structure Determination

description: Related Article: Ekaterina A. Arkhipova, Anton S. Ivanov, Konstantin I. Maslakov, Stepan Yu. Kupreenko, Mikhail M. Levin, Maksim V. Iurenkov, Konstantin A. Lyssenko, Serguei V. Savilov, Sergey M. Aldoshin|2022|J.Mol.Liq.|355|118933|doi:10.1016/j.molliq.2022.118933
DOI: 10.5517/ccdc.csd.cc28xsvv
title: CCDC 2113048: Experimental Crystal Structure Determination

description: Related Article: Ciming Wang, Huiqun Ju, Xiaolong Zhou, Pengrui Zhang, Zixuan Xue, Lianjing Mao, Fei Shao, Yan Jing, Yongzhong Jia, Jinhe Sun|2022|J.Mol.Liq.|355|118911|doi:10.1016/j.molliq.2022.118911
DOI: 10.5517/ccdc.csd.cc29lht7
title: CCDC 2132949: Experimental Crystal Structure Determination

description: Related Article: Maobo Wu, Jiaqi Shao, Jianxun Zhu, Jiachen Zi|2022|Fitoterapia|158|105171|doi:10.1016/j.fitote.2022.105171
DOI: 10.5517/ccdc.csd.cc29lhxb
title: CCDC 2132952: Experimental Crystal Structure Determination

description: Related Article: Maobo Wu, Jiaqi Shao, Jianxun Zhu, Jiachen Zi|2022|Fitoterapia|158|105171|doi:10.1016/j.fitote.2022.105171
DOI: 10.5517/ccdc.csd.cc29lhzd
title: CCDC 2132954: Experimental Crystal Structure Determination

description: Related Article: Maobo Wu, Jiaqi Shao, Jianxun Zhu, Jiachen Zi|2022|Fitoterapia|158|105171|doi:10.1016/j.fitote.2022.105171
DOI: 10.5517/ccdc.csd.cc29x0w4
title: CCDC 2142096: Experimental Crystal Structure Determination

description: Related Article: Bo-Sheng Zhang, Wan-Yuan Jia, Xue-Ya Gou, Ying-Hui Yang, Fan Wang, Yi-Ming Wang, Xi-Cun Wang, Zheng-Jun Quan|2022|Org.Lett.|24|2104|doi:10.1021/acs.orglett.2c00318
DOI: 10.5517/ccdc.csd.cc2b58c5
title: CCDC 2150016: Experimental Crystal Structure Determination

description: Related Article: Xiao-Hong Li, Xue-Feng Han, Wei-Na Wu, Xiao-Lei Zhao, Yuan Wang, Yun-Chang Fan, Zhi-Hong Xu|2022|Spectrochim.Acta,Part A|275|121160|doi:10.1016/j.saa.2022.121160
DOI: 10.5517/ccdc.csd.cc26rjr8
title: CCDC 2048410: Experimental Crystal Structure Determination

description: Related Article: Irfan Shaikh, Maitrey Travadi, R.N. Jadeja, Ray J. Butcher, Jignesh H. Pandya|2022|J.Indian Chem.Soc.|99|100428|doi:10.1016/j.jics.2022.100428
DOI: 10.34820/fk2/lr0ni5/uk7tx3
title: Data Karakter verse.docx

description: :unav
DOI: 10.14278/rodare.1507
title: Data publication: Development and Biological Evaluation of the First Highly Potent and Specific Benzamide-Based Radiotracer [¹⁸F]BA3 for Imaging of Histone Deacetylases 1 and 2 in Brain

description: The data publication contain: 1) Radiosynthesis data (Scheme of the synthesis module; RP-HPLC chromatograms of formulated [18F]BA3; MLC chromatograms of in vivo metabolism studies) 2) Biological data (Baseline TAC of CD-1 mice brain, biodistribution after i.v. injection of [18F]BA3) 3) Analytical data (1H, 13C, 19F NMR spectra; LC-MS chromatograms for final products)
DOI: 10.34820/fk2/lr0ni5
title: Tinjauan Karakter Bentuk Mata pada Animasi Jepang Modern

description: Tinjauan Karakter Bentuk Mata pada Animasi Jepang Modern
DOI: 10.34820/fk2/t123rf
title: Tampilan Kemasan Depan dan Belakang Produk Kopi Puntang dengan Konsep Ilustrasi Line-Art

description: Gambar ini merupakan final artwork berupa desain kemasan standing pouch dengan grafis kemasan yang dicetak langsung pada kemasan. Dalam artikel yang telah dipublikasikan hanya dicantumkan contoh satu desain tampilan depan dan belakang salah satu varian produk. Dataset ini menampilkan seluruh tampilan depan dan belakang kemasan pada penelitian redesain grafis kemasan produk Kopi Puntang dengan konsep ilustrasi line-art
DOI: 10.6084/m9.figshare.19401410.v3
title: Master Chart containing raw data of both the groups and sample size calculation and statistical anlysis.

description: This dataset includes four files:1) Demographic data2) Master chart for the plaque index and bleeding index3)Master chart for both the groups for all the parameters4) Percentage of root coverage calculation data5)Sample size calculation and statistical analysis 6)Results chart
DOI: 10.34820/fk2/iogavj
title: The Business Model Canvas Mapping and Business Model Innovation of SMEs

description: The dataset shows The Business Model Canvas mapping based on observation and in-depth interviews, and Business Model Innovation based on analysis as a recommended strategy. Case of SMEs in garment and fashion industry.
DOI: 10.5281/zenodo.5205544
title: A Single-Cell Tumor Immune Atlas for Precision Oncology

description: <strong>Publication version of the Single-Cell Tumor Immune Atlas</strong> This upload contains: <strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas <strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas <strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas <strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas <strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells All the files contain the following patient/sample metadata variables: patient: assigned patient identifiers nCountRNA and nFeatureRNA: number of UMIs and genes per cell percent.mt: percentage of mitochondrial genes gender: the patient's gender (male/female/unknown) source: dataset of origin subtype: cancer type (abbreviations as indicated in the preprint) kmeans_cluster: patients clusters, NA if filtered out before clustering lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types) <pre> </pre> If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file. For more information, read our paper, check our GitHub and our ShinyApp. h5ad files can be read with Python using Scanpy, rds files can be read in R using Seurat. For format conversion between AnnData and Seurat we recommend SeuratDisk. For other single-cell data formats you can use sceasy.
DOI: 10.5281/zenodo.4036019
title: A Single-Cell Tumor Immune Atlas for Precision Oncology

description: <strong>Publication version of the Single-Cell Tumor Immune Atlas</strong> This upload contains: <strong>TICAtlas.rds:</strong> an rds file containing a Seurat object with the whole Atlas <strong>TICAtlas.h5ad:</strong> an h5ad file with the whole Atlas <strong>TICAtlas_downsampled.rds:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas <strong>TICAtlas_downsampled.h5ad:</strong> an rds file containing a downsampled version of the Seurat object of the whole Atlas <strong>TICAtlas_metadata.csv: </strong>a comma-separated text file with the metadata for each of the cells All the files contain the following patient/sample metadata variables: patient: assigned patient identifiers nCountRNA and nFeatureRNA: number of UMIs and genes per cell percent.mt: percentage of mitochondrial genes gender: the patient's gender (male/female/unknown) source: dataset of origin subtype: cancer type (abbreviations as indicated in the preprint) kmeans_cluster: patients clusters, NA if filtered out before clustering lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types) <pre> </pre> If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the <strong>TICAtlas_metadata.csv </strong>file. For more information, read our paper, check our GitHub and our ShinyApp. h5ad files can be read with Python using Scanpy, rds files can be read in R using Seurat. For format conversion between AnnData and Seurat we recommend SeuratDisk. For other single-cell data formats you can use sceasy.
DOI: 10.34820/fk2/j2dia8/ehkdo6
title: Tabel Pola Visual Iklan.pdf

description: Tabel Pola Visual Iklan
DOI: 10.34820/fk2/j2dia8
title: Tabel Pola Visual Iklan

description: Dataverse ini merupakan data mentah berupa tabel pola visual dan verbal dari penelitian
DOI: 10.34820/fk2/omcuxw
title: Objek Kajian dalam Penelitian Skema Warna Kemasan dan Persepsi Konsumen

description: Dataset ini merupakan kumpulan objek kajian yang diperlihatkan pada responden penelitian ketika pengumpulan data primer. Gambar objek kajian ini telah diperlihatkan dalam artikel pada Figure 2 the packaging dominant white, dominant yellow, monochrome color scheme, analog, and complementary. Akan tetapi kualitas gambar yang ditampilkan terlalu kecil dan kurang jelas. Untuk itu dataset ini bertujuan menampilkan objek kajian sebagai data pendukung dari publikasi terkait.
DOI: 10.5281/zenodo.6400996
title: Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"

description: These datasets accompany the article published in <em>Remote Sensing </em>entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing". For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: <strong>Raw Metrics: </strong>the raw evaluations for each image returned by each of the 12 evaluation metrics. <strong>Rankings:</strong> the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.
DOI: 10.5281/zenodo.6400995
title: Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"

description: These datasets accompany the article published in <em>Remote Sensing </em>entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing". For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: <strong>Raw Metrics: </strong>the raw evaluations for each image returned by each of the 12 evaluation metrics. <strong>Rankings:</strong> the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.
DOI: 10.6084/m9.figshare.19481339
title: Unsupervised Labor Intelligence Systems: a Detection Approach and Its Evaluation - Appendix

description: Appendix and Data of the Paper
DOI: 10.15132/10000179
title: DRAM: A three-dimensional analytical model for the mobilisation of root reinforcement in direct shear conditions – Data repository

description: This dataset contains data of experiments underlying the work presented in the linked Ecological Engineering journal publication (DOI: 10.1016/j.ecoleng.2022.106621).
Source code for the DRAM model (written in R) can be found in a separate repository (DOI: 10.5281/zenodo.6375587).
DOI: 10.25934/pr00007424.0
title: Available IPD datapackage for study 'A Multicenter, Phase III, Open-Label, Randomized Study in Previously Untreated Patients With Advanced Indolent Non-Hodgkin's Lymphoma Evaluating the Benefit of GA101 (RO5072759) Plus Chemotherapy Compared With Rituximab Plus Chemotherapy Followed by GA101 or Rituximab Maintenance Therapy in Responders'

description: This open-label, randomized study will assess the efficacy and safety of obinutuzumab (RO5072759) in combination with chemotherapy compared to rituximab (MabThera/Rituxan) with chemotherapy followed by obinutuzumab or rituximab maintenance in participants with untreated advanced indolent non-Hodgkin's lymphoma. After the end of the induction period, participants achieving response (Complete response [CR] or partial response [PR]) will undergo a maintenance period continuing on the randomized antibody treatment alone every 2 months until disease progression for a total of 2 years. Anticipated time on study treatment is up to approximately 2.5 years. After maintenance or observation, participants will be followed for 5 years until progression. After progression, participants will be followed for new anti-lymphoma therapy and overall survival until the end of the study.
DOI: 10.25835/0036557
title: Soil Moisture Measuring Campaign Ilmenau 2016/2017

description: Soil moisture and grain size distributions from 10 locations in the Wipperau/Gerdau (Ilmenau) catchments. Collected for the study published here: Uniyal, B., Dietrich, J., Vasilakos, C., Tzoraki, O. (2017): Evaluation of SWAT simulated soil moisture at catchment scale by field measurements and Landsat derived indices. Agricultural Water Management 193, 55‐70, DOI: 10.1016/j.agwat.2017.08.002
DOI: 10.5281/zenodo.6400984
title: Interview data on experts' recommendations for visualizations in libraries

description: Results of an interview study with twelve experts on their project processes and their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities. The file contains two pages: On the first, a meta-model was constructed out of all identified steps during library visualization projects. On the second, all SHIRA suggestions were gathered and analyzed. Feel free to contact me if you have any questions! [1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.
DOI: 10.5281/zenodo.6400983
title: Interview data on experts' recommendations for visualizations in libraries

description: Results of an interview study with twelve experts on their project processes and their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities. The file contains two pages: On the first, a meta-model was constructed out of all identified steps during library visualization projects. On the second, all SHIRA suggestions were gathered and analyzed. Feel free to contact me if you have any questions! [1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.