cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0934710.09fu
title: SH0934710.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0934711.09fu
title: SH0934711.09FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.5078077
title: Data set for phase-field studies of crystal growth in single-seal syntaxial veins in limestone

description: The numerical data in this repository consists of the simulation data of single-seal syntaxial calcite vein formation in limestone. The simulations were performed using the software package named "Pace3D (v. 2.5.1)". The data is organized, the way it appears in the figures in the manuscript and the folders are named accordingly. The simulation data shows intermediate growth stages and was converted from Pace3D output data format to VTK data format. The VTK files can be visualized using open source software packages like Paraview. The data files in each subfolder are also compressed (file format *.gz). For visualization the data has to be decompressed (e.g. with gzip, 7zip).
DOI: 10.5281/zenodo.4597529
title: Data set for phase-field studies of crystal growth in single-seal syntaxial veins in limestone

description: The numerical data in this repository consists of the simulation data of single-seal syntaxial calcite vein formation in limestone. The simulations were performed using the software package named "Pace3D (v. 2.5.1)". The data is organized, the way it appears in the figures in the manuscript and the folders are named accordingly. The simulation data shows intermediate growth stages and was converted from Pace3D output data format to VTK data format. The VTK files can be visualized using open source software packages like Paraview. The data files in each subfolder are also compressed (file format *.gz). For visualization the data has to be decompressed (e.g. with gzip, 7zip).
DOI: 10.5281/zenodo.5325335
title: Running Example Datasets - DiRo2C

description: The two datasets are used to simulate two different black boxes that are supposed to predict different results in certain data areas. Dataset diro2c_running_example_dataset.csv: Two-dimensional dataset with the continuous features x1 and x2. It contains 300 instances with the following properties: min = − 293.39, max = 437.35, µ = − 2.92, and σ = 187.69 for feature x1 and min = − 251.96, max = 316.10, µ = − 3.23, and σ = 101.08 for feature x2. The instances of the datasets are classified into two classes “0” and “1”. The instances are generated by the sklearn “make_classification” function with the following parameters: make_classification(n_samples = 300, n_features = 2, n_informative = 1, n_redundant = 0, n_classes = 2, random_state = 2, n_clusters_per_class = 1, class_sep = 1.8, flip_y = 0, scale = 100). Dataset diro2c_running_example_manipulated_dataset.csv: This dataset is manipulated on the basis of the first dataset as follows: (x1 &lt;= 0 and x2 &lt;= −100 : y = 0), (x1 &gt; 0 and x2 &lt;= −100 : y = 1), (x1 &lt; 150 and x2 &gt; −100 and x2 &lt; 100 : y = 0),<br> (x1 &gt;= 150 and x2 &gt; −100 and x2 &lt; 100 : y = 1), (x1 = −200 and x2 &gt;= 100: y = 0), (x1 &gt; −200 and x2 &gt;= 100 : y = 1)
DOI: 10.5281/zenodo.5227734
title: Running Example Datasets - DiRo2C

description: The two datasets are used to simulate two different black boxes that are supposed to predict different results in certain data areas. Dataset diro2c_running_example_dataset.csv: Two-dimensional dataset with the continuous features x1 and x2. It contains 300 instances with the following properties: min = − 293.39, max = 437.35, µ = − 2.92, and σ = 187.69 for feature x1 and min = − 251.96, max = 316.10, µ = − 3.23, and σ = 101.08 for feature x2. The instances of the datasets are classified into two classes “0” and “1”. The instances are generated by the sklearn “make_classification” function with the following parameters: make_classification(n_samples = 300, n_features = 2, n_informative = 1, n_redundant = 0, n_classes = 2, random_state = 2, n_clusters_per_class = 1, class_sep = 1.8, flip_y = 0, scale = 100). Dataset diro2c_running_example_manipulated_dataset.csv: This dataset is manipulated on the basis of the first dataset as follows: (x1 &lt;= 0 and x2 &lt;= −100 : y = 0), (x1 &gt; 0 and x2 &lt;= −100 : y = 1), (x1 &lt; 150 and x2 &gt; −100 and x2 &lt; 100 : y = 0),<br> (x1 &gt;= 150 and x2 &gt; −100 and x2 &lt; 100 : y = 1), (x1 = −200 and x2 &gt;= 100: y = 0), (x1 &gt; −200 and x2 &gt;= 100 : y = 1)
DOI: 10.5517/ccdc.csd.cc248sx4
title: CCDC 1974666: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc248sz6
title: CCDC 1974668: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc248t08
title: CCDC 1974669: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc248sv2
title: CCDC 1974664: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc27832q
title: CCDC 2063362: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc26c5g7
title: CCDC 2036528: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc248tr0
title: CCDC 1974692: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc27j6cc
title: CCDC 2071152: Experimental Crystal Structure Determination

description: Related Article: Adrian Keating-Clay and Takeshi Miyazawa|2021|CSD Communication|||
description: Related Article: Takeshi Miyazawa, Brendan J. Fitzgerald, Adrian T. Keatinge-Clay|2021|Chem.Commun.|57|8762|doi:10.1039/D1CC03073F
DOI: 10.5517/ccdc.csd.cc27j6dd
title: CCDC 2071153: Experimental Crystal Structure Determination

description: Related Article: Adrian Keating-Clay and Takeshi Miyazawa|2021|CSD Communication|||
description: Related Article: Takeshi Miyazawa, Brendan J. Fitzgerald, Adrian T. Keatinge-Clay|2021|Chem.Commun.|57|8762|doi:10.1039/D1CC03073F
DOI: 10.5517/ccdc.csd.cc26c5f6
title: CCDC 2036527: Experimental Crystal Structure Determination

description: Related Article: Yuanhong Shu|2021|CSD Communication|||
description: Related Article: Yuanhong Shu, Kaiqi Ye, Yuan Yue, Jingbo Sun, Haoran Wang, Jiangbin Zhong, Xiqiao Yang, Hongqiang Gao, Ran Lu|2021|CrystEngComm|23|5856|doi:10.1039/D1CE00086A
DOI: 10.5517/ccdc.csd.cc27l6km
title: CCDC 2073080: Experimental Crystal Structure Determination

description: Related Article: Suguru Ito, Sayaka Nagai, Takashi Ubukata, Takashi Tachikawa|2021|CrystEngComm|23|5899|doi:10.1039/D1CE00445J
DOI: 10.5517/ccdc.csd.cc27l6ln
title: CCDC 2073081: Experimental Crystal Structure Determination

description: Related Article: Suguru Ito, Sayaka Nagai, Takashi Ubukata, Takashi Tachikawa|2021|CrystEngComm|23|5899|doi:10.1039/D1CE00445J
DOI: 10.5517/ccdc.csd.cc27l6mp
title: CCDC 2073082: Experimental Crystal Structure Determination

description: Related Article: Suguru Ito, Sayaka Nagai, Takashi Ubukata, Takashi Tachikawa|2021|CrystEngComm|23|5899|doi:10.1039/D1CE00445J
DOI: 10.5517/ccdc.csd.cc27lf7j
title: CCDC 2073287: Experimental Crystal Structure Determination

description: Related Article: Yusuke Kikuchi, Shinya Matsumoto|2021|CrystEngComm|23|5882|doi:10.1039/D1CE00454A
DOI: 10.5517/ccdc.csd.cc27lf8k
title: CCDC 2073288: Experimental Crystal Structure Determination

description: Related Article: Yusuke Kikuchi, Shinya Matsumoto|2021|CrystEngComm|23|5882|doi:10.1039/D1CE00454A
DOI: 10.5517/ccdc.csd.cc27lf9l
title: CCDC 2073289: Experimental Crystal Structure Determination

description: Related Article: Yusuke Kikuchi, Shinya Matsumoto|2021|CrystEngComm|23|5882|doi:10.1039/D1CE00454A
DOI: 10.5517/ccdc.csd.cc27lfbm
title: CCDC 2073290: Experimental Crystal Structure Determination

description: Related Article: Yusuke Kikuchi, Shinya Matsumoto|2021|CrystEngComm|23|5882|doi:10.1039/D1CE00454A
DOI: 10.5517/ccdc.csd.cc27lwtk
title: CCDC 2073739: Experimental Crystal Structure Determination

description: Related Article: Masaki Yoshida, Yusuke Makino, Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa, Atsushi Kobayashi, Masako Kato|2021|CrystEngComm|23|5891|doi:10.1039/D1CE00459J
DOI: 10.5517/ccdc.csd.cc27lwvl
title: CCDC 2073740: Experimental Crystal Structure Determination

description: Related Article: Masaki Yoshida, Yusuke Makino, Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa, Atsushi Kobayashi, Masako Kato|2021|CrystEngComm|23|5891|doi:10.1039/D1CE00459J
DOI: 10.5517/ccdc.csd.cc27lwwm
title: CCDC 2073741: Experimental Crystal Structure Determination

description: Related Article: Masaki Yoshida, Yusuke Makino, Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa, Atsushi Kobayashi, Masako Kato|2021|CrystEngComm|23|5891|doi:10.1039/D1CE00459J
DOI: 10.5517/ccdc.csd.cc27lwxn
title: CCDC 2073742: Experimental Crystal Structure Determination

description: Related Article: Masaki Yoshida, Yusuke Makino, Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa, Atsushi Kobayashi, Masako Kato|2021|CrystEngComm|23|5891|doi:10.1039/D1CE00459J
DOI: 10.5517/ccdc.csd.cc27lwyp
title: CCDC 2073743: Experimental Crystal Structure Determination

description: Related Article: Masaki Yoshida, Yusuke Makino, Toshiyuki Sasaki, Shunichi Sakamoto, Satoshi Takamizawa, Atsushi Kobayashi, Masako Kato|2021|CrystEngComm|23|5891|doi:10.1039/D1CE00459J
DOI: 10.6084/m9.figshare.9947696.v1
title: Long non-coding RNA <i>SNHG4</i> promotes cervical cancer progression through regulating c-Met via targeting miR-148a-3p

description: Long non-coding RNA (lncRNA) <i>SNHG4</i> has been shown to be associated with the development of a variety of cancers. The purpose of this study was to investigate the effect of <i>SNHG4</i> on cervical cancer (CC) and the corresponding mechanism. The qRT-PCR was used to determine the expressions of SNHG4 and <i>miR-148a-3p</i> in CC cell lines and tissues. Cell apoptosis and proliferation were measured by flow cytometry and MTT assay, respectively. The interaction between SNHG4, <i>miR-148a-3p</i> and <i>c-Met</i> was verified by bioinformatics, dual-luciferase reporter gene and RNA immunoprecipitation (RIP), and the effect of <i>SNHG4</i> on the growth of CC tumor <i>in vivo</i> was explored. The expression of <i>SNHG4</i> was increased in both CC cell lines and tissues, while the expression of <i>miR-148a-3p</i> was down-regulated. Meanwhile, silencing SNHG4 remarkably inhibited CC cell proliferation and promoted apoptosis. In addition, miR-148a-3p was a direct target gene of <i>SNHG4</i>, and down-regulation of miR-148a-3p could observably reverse the effect of silencing <i>SNHG4</i> on the proliferation and apoptosis of CC cells. More importantly, <i>SNHG4</i> could up-regulate the expression of <i>c-Met</i> by targeting and interacting with <i>miR-148a-3p</i>. Finally, <i>in vivo</i> experiments confirmed that silence SNHG4 down-regulated the expression of <i>c-Met</i> by promoting <i>miR-148a-3p</i>, and ultimately suppressed the growth of CC tumor <i>in vivo</i>. In conclusion, <i>SNHG4</i> could be used as a competitive endogenous RNA to bind to miR-148a-3p, thereby up-regulating the expression of <i>c-Met</i> and ultimately promoting the progression of CC, which provided a potential therapeutic target for the targeted treatment of CC.
DOI: 10.5517/ccdc.csd.cc278m27
title: CCDC 2063858: Experimental Crystal Structure Determination

description: Related Article: Shuang Peng, Yan-Jie Qi, Xin-Hao Li, Cai Sun, Ling-Yun Li, Xin-Xiong Li, Shou-Tian Zheng, Qichun Zhang|2021|CrystEngComm|23|5753|doi:10.1039/D1CE00464F
DOI: 10.5517/ccdc.csd.cc278m38
title: CCDC 2063859: Experimental Crystal Structure Determination

description: Related Article: Shuang Peng, Yan-Jie Qi, Xin-Hao Li, Cai Sun, Ling-Yun Li, Xin-Xiong Li, Shou-Tian Zheng, Qichun Zhang|2021|CrystEngComm|23|5753|doi:10.1039/D1CE00464F
DOI: 10.5517/ccdc.csd.cc27mwc4
title: CCDC 2074686: Experimental Crystal Structure Determination

description: Related Article: Benjamin J. Frogley, Anthony F. Hill, Ryan M. Kirk|2021|Chem.Commun.|57|8770|doi:10.1039/D1CC03015A
DOI: 10.5517/ccdc.csd.cc27mwg7
title: CCDC 2074689: Experimental Crystal Structure Determination

description: Related Article: Kanae Yano, Ryo Nishimura, Yohei Hattori, Masakazu Morimoto, Haruki Sugiyama, Takashi Kamitanaka, Satoshi Yokojima, Shinichio Nakamura, Kingo Uchida|2021|CrystEngComm|23|5780|doi:10.1039/D1CE00444A
DOI: 10.5517/ccdc.csd.cc27nbnx
title: CCDC 2075129: Experimental Crystal Structure Determination

description: Related Article: Kanae Yano, Ryo Nishimura, Yohei Hattori, Masakazu Morimoto, Haruki Sugiyama, Takashi Kamitanaka, Satoshi Yokojima, Shinichio Nakamura, Kingo Uchida|2021|CrystEngComm|23|5780|doi:10.1039/D1CE00444A
DOI: 10.5517/ccdc.csd.cc27n8pw
title: CCDC 2075068: Experimental Crystal Structure Determination

description: Related Article: Ma. Carmen García-González, Armando Navarro-Huerta, Fanny Chantal Rodríguez-Muñoz, Estefanía Guadalupe Vera-Alvízar, Marco A. Vera Ramírez, Joelis Rodríguez-Hernández, Mario Rodríguez, Braulio Rodríguez-Molina|2021|CrystEngComm|23|5908|doi:10.1039/D1CE00442E
DOI: 10.5517/ccdc.csd.cc27n8qx
title: CCDC 2075069: Experimental Crystal Structure Determination

description: Related Article: Ma. Carmen García-González, Armando Navarro-Huerta, Fanny Chantal Rodríguez-Muñoz, Estefanía Guadalupe Vera-Alvízar, Marco A. Vera Ramírez, Joelis Rodríguez-Hernández, Mario Rodríguez, Braulio Rodríguez-Molina|2021|CrystEngComm|23|5908|doi:10.1039/D1CE00442E
DOI: 10.5517/ccdc.csd.cc27n8ry
title: CCDC 2075070: Experimental Crystal Structure Determination

description: Related Article: Ma. Carmen García-González, Armando Navarro-Huerta, Fanny Chantal Rodríguez-Muñoz, Estefanía Guadalupe Vera-Alvízar, Marco A. Vera Ramírez, Joelis Rodríguez-Hernández, Mario Rodríguez, Braulio Rodríguez-Molina|2021|CrystEngComm|23|5908|doi:10.1039/D1CE00442E
DOI: 10.5517/ccdc.csd.cc27n8sz
title: CCDC 2075071: Experimental Crystal Structure Determination

description: Related Article: Ma. Carmen García-González, Armando Navarro-Huerta, Fanny Chantal Rodríguez-Muñoz, Estefanía Guadalupe Vera-Alvízar, Marco A. Vera Ramírez, Joelis Rodríguez-Hernández, Mario Rodríguez, Braulio Rodríguez-Molina|2021|CrystEngComm|23|5908|doi:10.1039/D1CE00442E
DOI: 10.5517/ccdc.csd.cc27n8t0
title: CCDC 2075072: Experimental Crystal Structure Determination

description: Related Article: Ma. Carmen García-González, Armando Navarro-Huerta, Fanny Chantal Rodríguez-Muñoz, Estefanía Guadalupe Vera-Alvízar, Marco A. Vera Ramírez, Joelis Rodríguez-Hernández, Mario Rodríguez, Braulio Rodríguez-Molina|2021|CrystEngComm|23|5908|doi:10.1039/D1CE00442E
DOI: 10.5517/ccdc.csd.cc27ncbm
title: CCDC 2075150: Experimental Crystal Structure Determination

description: Related Article: Yue Shen, Shiyin Wang, Xiangyu Zhang, Nan Li, Haichao Liu, Bing Yang|2021|CrystEngComm|23|5918|doi:10.1039/D1CE00449B
DOI: 10.5517/ccdc.csd.cc27nccn
title: CCDC 2075151: Experimental Crystal Structure Determination

description: Related Article: Yue Shen, Shiyin Wang, Xiangyu Zhang, Nan Li, Haichao Liu, Bing Yang|2021|CrystEngComm|23|5918|doi:10.1039/D1CE00449B
DOI: 10.5517/ccdc.csd.cc27ncdp
title: CCDC 2075152: Experimental Crystal Structure Determination

description: Related Article: Yue Shen, Shiyin Wang, Xiangyu Zhang, Nan Li, Haichao Liu, Bing Yang|2021|CrystEngComm|23|5918|doi:10.1039/D1CE00449B
DOI: 10.5517/ccdc.csd.cc27nct3
title: CCDC 2075165: Experimental Crystal Structure Determination

description: Related Article: Avulu Vinod Kumar, Mehdi Rohullah, Jada Ravi, Mallesham Godumala, Mari Annadhasan, Rajadurai Chandrasekar|2021|CrystEngComm|23|5774|doi:10.1039/D1CE00475A
DOI: 10.5517/ccdc.csd.cc27ndlx
title: CCDC 2075189: Experimental Crystal Structure Determination

description: Related Article: Yuji Suzaki, Yugo Fukuchi, Hiroko Tadami, Take-aki Koizumi, Kohtaro Osakada, Tomohito Ide, Masaki Horie, Norihisa Hoshino, Tomoyuki Akutagawa|2021|CrystEngComm|23|5944|doi:10.1039/D1CE00457C
DOI: 10.5287/bodleian:xr15p8bvz
title: Strategies to improve smoking cessation rates in primary care: supplementary tables
DOI: 10.5281/zenodo.5362220
title: Gaussian Quantiles Datasets - DiRo2C

description: The two datasets are used to simulate two different black boxes that are supposed to predict different results in certain data areas. Dataset diro2c_gaussian_dataset.csv: Two-dimensional dataset with the continuous features x1 and x2. It was created with µ = 0 and σ 2 = 0.8. It contains 300 instances (data points) with the following properties for feature x1: min = − 275.71, max = 255.90, µ = 0.04, and σ = 88.54 and with the following properties for feature x2: min = − 252.57, max = 201.03, µ = − 9.44, and σ = 86.20. The instances of the datasets are classified into two classes “0” and “1”. 150 are assigned to the class “0” and 150 instances are assigned to the class “1”. The instances of the dataset are generated by the sklearn “make_gaussian_quantiles” function with the following parameters: make_gaussian_quantiles(n_samples = 300, n_classes = 2, shuffle = False, cov = 0.8, random_state = 7). Afterward, we scale the instances by the factor 100. Dataset diro2c_gaussian_manipulated_dataset.csv: Two-dimensional dataset with the continuous features x1 and x2. The manipulated dataset is generated with µ = 0 and σ 2 = 1.3. It contains 300 instances (datapoints) with the following properties for feature x1: min = − 351.46, max = 326.21, µ = 0.04, and σ = 112.87 and with the following properties for feature x2: min = − 321.97, max = 256.27, µ = − 12.04, and σ = 109.89. The instances of the datasets are classified into two classes “0” and “1”. 150 instances are assigned to the class “0”, and 150 instances are assigned to the class “1”.
DOI: 10.5281/zenodo.5362219
title: Gaussian Quantiles Datasets - DiRo2C

description: The two datasets are used to simulate two different black boxes that are supposed to predict different results in certain data areas. Dataset diro2c_gaussian_dataset.csv: Two-dimensional dataset with the continuous features x1 and x2. It was created with µ = 0 and σ 2 = 0.8. It contains 300 instances (data points) with the following properties for feature x1: min = − 275.71, max = 255.90, µ = 0.04, and σ = 88.54 and with the following properties for feature x2: min = − 252.57, max = 201.03, µ = − 9.44, and σ = 86.20. The instances of the datasets are classified into two classes “0” and “1”. 150 are assigned to the class “0” and 150 instances are assigned to the class “1”. The instances of the dataset are generated by the sklearn “make_gaussian_quantiles” function with the following parameters: make_gaussian_quantiles(n_samples = 300, n_classes = 2, shuffle = False, cov = 0.8, random_state = 7). Afterward, we scale the instances by the factor 100. Dataset diro2c_gaussian_manipulated_dataset.csv: Two-dimensional dataset with the continuous features x1 and x2. The manipulated dataset is generated with µ = 0 and σ 2 = 1.3. It contains 300 instances (datapoints) with the following properties for feature x1: min = − 351.46, max = 326.21, µ = 0.04, and σ = 112.87 and with the following properties for feature x2: min = − 321.97, max = 256.27, µ = − 12.04, and σ = 109.89. The instances of the datasets are classified into two classes “0” and “1”. 150 instances are assigned to the class “0”, and 150 instances are assigned to the class “1”.
DOI: 10.5281/zenodo.5215815
title: EAW FASTQ files for bioinformatic courses (16S rRNA genes, 2018)

description: Selection of 6 samples, with 6 Forward (R1) and 6 Reverse (R2) files, including primers. R1 and R2 reads are ca. 300 bp long, and were obtained from Illumina MiSeq technologies, at the FEM facility sequencing platform. The files refer to the16S rRNA gene reads obtained from the analyses carried out on the samples collected and filtered (Sterivex<sup>TM</sup> 0.22 µm) in different areas and depths of Lake Garda on September, 2018 (see EAW_2018_FASTQ_16S_description.docx). Sampling and analyses were carried out in the framework of the project Eco-AlpsWater (ASP569), funded by the Interreg Alpine Space program. A bioinformatic protocol for analyzing these data using DADA2 is available in Zenodo: <pre>https://doi.org/10.5281/zenodo.5232772 </pre>
DOI: 10.5281/zenodo.5215814
title: EAW FASTQ files for bioinformatic courses (16S rRNA genes, 2018)

description: Selection of 6 samples, with 6 Forward (R1) and 6 Reverse (R2) files, including primers. R1 and R2 reads are ca. 300 bp long, and were obtained from Illumina MiSeq technologies, at the FEM facility sequencing platform. The files refer to the16S rRNA gene reads obtained from the analyses carried out on the samples collected and filtered (Sterivex<sup>TM</sup> 0.22 µm) in different areas and depths of Lake Garda on September, 2018 (see EAW_2018_FASTQ_16S_description.docx). Sampling and analyses were carried out in the framework of the project Eco-AlpsWater (ASP569), funded by the Interreg Alpine Space program. A bioinformatic protocol for analyzing these data using DADA2 is available in Zenodo: <pre>https://doi.org/10.5281/zenodo.5232772 </pre>