cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4048040
title: Pollen Video Library for Benchmarking Detection, Classification, Tracking and Novelty Detection Tasks

description: This dataset contains microscopic images and videos of pollen gathered between Feb. and Aug. 2020 in Graz, Austria. Pollen images of 16 types: <code>images_16_types.zip</code> Acer Pseudoplatanus Aesculus Carnea Alnus Anthoxanthum Betula Pendula Brassica Carpinus Corylus Dactylis Glomerata Fraxinus Pinus Nigra Platanus Populus Nigra Prunus Avium Sequoiadendron Giganteum Taxus Baccata Pollen video library <code>pollen_video_library.zip</code> Each type of pollen is in a separate folder, there may be multiple videos per type. In each pollen folder, we included images cropped from the videos by YOLO object detection algorithm trained on a subset of pollen images as described in [1]. Field data over 3 days are gathered in Graz in spring 2020. <code>pollen_field_data.zip</code> Sample code to load the data and visualize the images is in <code>plot_pollen_sample.py</code>. Download and extract the file <code>images_16_types.zip</code> in the same folder as <code>plot_pollen_sample.py</code> to run the example. <strong>Dependecies</strong> opencv numpy matplotlib <strong>Credit</strong> [1] N. Cao, M. Meyer, L. Thiele, and O. Saukh. 2020. Automated Pollen Detection with an Affordable Technology. In Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN). 108–119. <pre><code>@inproceedings{namcao2020pollen, title = {Automated Pollen Detection with an Affordable Technology}, author = {Nam Cao and Matthias Meyer and Lothar Thiele and Olga Saukh}, booktitle = {Proceedings of the International Conference on Embedded Wireless Systems and Networks (EWSN)}, pages={108–119} month = {2}, year = {2020}, } </code></pre>
DOI: 10.6084/m9.figshare.12999116.v1
title: uzb-cyrl-webcrawl-v1-fasttext-cbow-300d

description: 300-dimensional fasttext CBOW word embeddings for the Cyrillic Uzbek language. Trained using the webcrawl corpus v1.
DOI: 10.6084/m9.figshare.12999116
title: 300d fasttext (CBOW) embeddings for Cyrillic Uzbek using webcrawl v1 corpus

description: 300-dimensional fasttext CBOW word embeddings for the Cyrillic Uzbek language. Trained using the webcrawl corpus v1.
DOI: 10.6084/m9.figshare.12999815
title: 100d fasttext (skipgram) embeddings for Cyrillic Uzbek using webcrawl v1 corpus

description: 100-dimensional fasttext skipgram word embeddings for the Cyrillic Uzbek language. Trained using the webcrawl corpus v1.
DOI: 10.6084/m9.figshare.12999815.v1
title: uzb-cyrl-webcrawl-v1-fasttext-skipgram-100d

description: 100-dimensional fasttext skipgram word embeddings for the Cyrillic Uzbek language. Trained using the webcrawl corpus v1.
DOI: 10.6084/m9.figshare.12999833
title: stage1.csv

description: gfghg
DOI: 10.6084/m9.figshare.12999833.v1
title: stage1.csv

description: gfghg
DOI: 10.25387/g3.12985430
title: Supplemental Material for Sandler et al., 2020

description: <b>Figure S1 </b>Visualization of a low-quality genomic region that was filtered from our analyses. <b>Figure S2 </b>Allelic coverage at heterozygous sites before and after filtering. <b>Figure S3</b> Visualization of a validated <i>de novo</i> mutation from both Illumina short read and Sanger sequencing data. <b>Figure S4</b> Visualisation of a putative <i>de novo</i> mutation that was identified as a likely sequencing error <b>Figure S5</b> Visualisation of a putative <i>de novo</i> mutation that was identified as a likely genome misassembly error <b>Table S1</b> List of <i>de novo</i> mutations identified in two species of duckweed <b>Table S2 </b>Mutation rate estimates under the observed allelic bias at <i>de novo</i> mutations <b>Table S3</b> Mutation rate estimates under several scenarios of allelic bias at <i>de novo</i> mutant sites in salt stressed mutation accumulation lines <b>Table S4</b> Mutation rate estimates under several scenarios of allelic bias at <i>de novo</i> mutant sites in control mutation accumulation lines <b>Table S5</b> Number and fraction of callable sites under several scenarios of allelic bias at <i>de novo</i> mutant sites <b>Table S6</b> Summary of mutation rate estimates retrieved from the literature
DOI: 10.25387/g3.12985430.v1
title: Supplemental Material for Sandler et al., 2020

description: <b>Figure S1 </b>Visualization of a low-quality genomic region that was filtered from our analyses. <b>Figure S2 </b>Allelic coverage at heterozygous sites before and after filtering. <b>Figure S3</b> Visualization of a validated <i>de novo</i> mutation from both Illumina short read and Sanger sequencing data. <b>Figure S4</b> Visualisation of a putative <i>de novo</i> mutation that was identified as a likely sequencing error <b>Figure S5</b> Visualisation of a putative <i>de novo</i> mutation that was identified as a likely genome misassembly error <b>Table S1</b> List of <i>de novo</i> mutations identified in two species of duckweed <b>Table S2 </b>Mutation rate estimates under the observed allelic bias at <i>de novo</i> mutations <b>Table S3</b> Mutation rate estimates under several scenarios of allelic bias at <i>de novo</i> mutant sites in salt stressed mutation accumulation lines <b>Table S4</b> Mutation rate estimates under several scenarios of allelic bias at <i>de novo</i> mutant sites in control mutation accumulation lines <b>Table S5</b> Number and fraction of callable sites under several scenarios of allelic bias at <i>de novo</i> mutant sites <b>Table S6</b> Summary of mutation rate estimates retrieved from the literature
DOI: 10.5281/zenodo.3935954
title: Data Set of Interviews with Industry Representatives on Smart Dust

description: Data Set of Interviews with Industry Representatives on Smart Dust This data set comprises the following documents: Coding scheme Model development Interview guideline
DOI: 10.5281/zenodo.3935953
title: Data Set of Interviews with Industry Representatives on Smart Dust

description: Data Set of Interviews with Industry Representatives on Smart Dust This data set comprises the following documents: Coding scheme Model development Interview guideline
DOI: 10.5517/ccdc.csd.cc23xw86
title: CCDC 1964168: Experimental Crystal Structure Determination

description: Related Article: Kai Chen, Ran Li, Yu-Ying Peng, Zhao-Qiang Zhu, Zi-Yi Hua|2021|Inorg.Chim.Acta|514|119987|doi:10.1016/j.ica.2020.119987
DOI: 10.5517/ccdc.csd.cc2506yc
title: CCDC 1996212: Experimental Crystal Structure Determination

description: Related Article: Adharsh Raghavan, Nickolas H. Anderson, Caleb J. Tatebe, Dalton A. Stanley, Matthias Zeller, Suzanne C. Bart|2020|Chem.Commun.|56|11138|doi:10.1039/D0CC03261A
DOI: 10.5517/ccdc.csd.cc25by5n
title: CCDC 2006511: Experimental Crystal Structure Determination

description: Related Article: Waku Shimizu, Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Yoshio Kasashima, Masami Sakamoto|2020|Cryst.Growth Des.|20|5676|doi:10.1021/acs.cgd.0c00955
DOI: 10.5517/ccdc.csd.cc25by6p
title: CCDC 2006512: Experimental Crystal Structure Determination

description: Related Article: Waku Shimizu, Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Yoshio Kasashima, Masami Sakamoto|2020|Cryst.Growth Des.|20|5676|doi:10.1021/acs.cgd.0c00955
DOI: 10.5517/ccdc.csd.cc25by7q
title: CCDC 2006513: Experimental Crystal Structure Determination

description: Related Article: Waku Shimizu, Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Yoshio Kasashima, Masami Sakamoto|2020|Cryst.Growth Des.|20|5676|doi:10.1021/acs.cgd.0c00955
DOI: 10.5517/ccdc.csd.cc25by9s
title: CCDC 2006515: Experimental Crystal Structure Determination

description: Related Article: Waku Shimizu, Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Yoshio Kasashima, Masami Sakamoto|2020|Cryst.Growth Des.|20|5676|doi:10.1021/acs.cgd.0c00955
DOI: 10.5517/ccdc.csd.cc25bybt
title: CCDC 2006516: Experimental Crystal Structure Determination

description: Related Article: Waku Shimizu, Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Yoshio Kasashima, Masami Sakamoto|2020|Cryst.Growth Des.|20|5676|doi:10.1021/acs.cgd.0c00955
DOI: 10.5517/ccdc.csd.cc25bycv
title: CCDC 2006517: Experimental Crystal Structure Determination

description: Related Article: Waku Shimizu, Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Yoshio Kasashima, Masami Sakamoto|2020|Cryst.Growth Des.|20|5676|doi:10.1021/acs.cgd.0c00955
DOI: 10.5517/ccdc.csd.cc25bydw
title: CCDC 2006518: Experimental Crystal Structure Determination

description: Related Article: Waku Shimizu, Naohiro Uemura, Yasushi Yoshida, Takashi Mino, Yoshio Kasashima, Masami Sakamoto|2020|Cryst.Growth Des.|20|5676|doi:10.1021/acs.cgd.0c00955
DOI: 10.6073/pasta/9ced31aa3a6cfd730bfb21ae71754bec
title: Water Quality Data (Extensive) from the Taylor Slough, Everglades National Park (FCE LTER), South Florida from July 1999 to Present

description: Water quality samples are being collected using ISCO autosamplers at all wetland sites (that is, all sites except TS/Ph-9, 10, and 11). The autosamplers contain 24 1L bottles. Water is sampled by programming the autosamplers to take composite samples once every 3 days. These samples are a composite of four 250mL subsamples drawn every 18 hours (a sampling scheme that captures a dawn, noon, dusk, and midnight sample in every three day composite). The samples are collected every 3-4 weeks and analyzed for total phosphorus (TP), total nitrogen (TN), and salinity. When sites are visited to collect these samples, we also collect a grab sample that is immediately put on ice. A portion of these grab samples is filtered through a Whatman GF/F filter immediately upon return to the lab, and the filtered samples are analyzed for inorganic nutrients such as NO2-, NO3-, NH4+, SRP, and DOC. The unfiltered fraction of these grab samples is analyzed for TP, TN, and TOC. We use these monthly grab samples to generate relationships between TP and SRP, and between TN and NO2- + NO3- + NH4+. Dissolved nutrients are measured using standard rapid flow analyzer (RFA) techniques. TP is analyzed with a modified Solorzano and Sharp (1980) technique. TN is measured with an Antec TN analyzer, TOC and DOC are quantified on a Shimadzu TOC Analyzer, and salinity is measured with an YSI conductivity meter. In addition to the regular water quality monitoring, we use the rain level actuators at all freshwater sites to trigger water sampling after rain events exceed a given threshold of duration and/or intensity. As currently programmed, when the threshold of = 2.5 cm of rain per hour is passed, the autosampler at that site collects a 500mL sample 30 minutes after the threshold has been reached. Rain event samples are collected, treated, and analyzed as all other water quality samples.
DOI: 10.34740/kaggle/dsv/1512934
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.831459
title: Data for: Wikipedia as a gateway to biomedical research

description: Wikipedia has been described as a gateway to knowledge. However, the extent to which this gateway ends at Wikipedia or continues via supporting citations is unknown. This dataset was used to establish benchmarks for the relative distribution and referral (click) rate of citations, as indicated by presence of a Digital Object Identifier (DOI), from Wikipedia with a focus on medical citations. This data set includes for each day in August 2016 a listing of all DOI present in the English language version of Wikipedia and whether or not the DOI are biomedical in nature. Source Code for these data are available at: Ryan Steinberg. (2017, July 9). Lane-Library/wiki-extract: initial Zenodo/DOI release. Zenodo. http://doi.org/10.5281/zenodo.824813 This dataset also includes a listing from Crossref DOIs that were referred from Wikipedia in August 2016 (Wikipedia_referred_DOI). Source code for these data sets is available at: Joe Wass. (2017, July 4). CrossRef/logppj: Initial DOI registered release. Zenodo. http://doi.org/10.5281/zenodo.822636 An article based on this data was published in PLOS One: Maggio LA, Willinsky JM, Steinberg RM, Mietchen D, Wass JL, Dong T. Wikipedia as a gateway to biomedical research: The relative distribution and use of citations in the English Wikipedia. PloS one. 2017 Dec 21;12(12):e0190046. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190046
DOI: 10.5281/zenodo.825221
title: Data for: Wikipedia as a gateway to biomedical research

description: Wikipedia has been described as a gateway to knowledge. However, the extent to which this gateway ends at Wikipedia or continues via supporting citations is unknown. This dataset was used to establish benchmarks for the relative distribution and referral (click) rate of citations, as indicated by presence of a Digital Object Identifier (DOI), from Wikipedia with a focus on medical citations. This data set includes for each day in August 2016 a listing of all DOI present in the English language version of Wikipedia and whether or not the DOI are biomedical in nature. Source Code for these data are available at: Ryan Steinberg. (2017, July 9). Lane-Library/wiki-extract: initial Zenodo/DOI release. Zenodo. http://doi.org/10.5281/zenodo.824813 This dataset also includes a listing from Crossref DOIs that were referred from Wikipedia in August 2016 (Wikipedia_referred_DOI). Source code for these data sets is available at: Joe Wass. (2017, July 4). CrossRef/logppj: Initial DOI registered release. Zenodo. http://doi.org/10.5281/zenodo.822636 An article based on this data was published in PLOS One: Maggio LA, Willinsky JM, Steinberg RM, Mietchen D, Wass JL, Dong T. Wikipedia as a gateway to biomedical research: The relative distribution and use of citations in the English Wikipedia. PloS one. 2017 Dec 21;12(12):e0190046. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190046
DOI: 10.5281/zenodo.3461035
title: Data for: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education

description: This is the raw data for the manuscript: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education. The abstract for the study is as follows: Introduction: Many medical education journals use Twitter to garner attention for their articles. The purpose of this study was to test the effects of tweeting on article page views and downloads. Methods: The authors conducted a randomized trial using <em>Academic Medicine</em> articles published in 2015. Beginning in February through May 2018, one article per day was randomly assigned to a Twitter (case) or control group. Daily, an individual tweet was generated for each article in the Twitter group that included the title, #MedEd, and a link to the article. The link delivered users to the article’s landing page, which included immediate access to the HTML full text and a PDF link. The authors extracted HTML page views and PDF downloads from the publisher. To assess differences in page views and downloads between cases and controls, a time-centered approach was used, with outcomes measured at 1, 7, and 30 days. Results: In total, 189 articles (94 cases, 95 controls) were analyzed. After days 1 and 7, there were no statistically significant differences between cases and controls on any metric. On day 30, HTML page views exhibited a 63% increase for cases (M=14.72, SD=63.68) when compared to controls (M=9.01, SD=14.34; incident rate ratio=1.63, p=0.01). There were no differences between cases and controls for PDF downloads on day 30. Discussion: Contrary to the authors’ hypothesis, only one statistically significant difference in page views between the Twitter and control groups was found. These findings provide preliminary evidence that after 30 days a tweet can have a small positive effect on article page views.
DOI: 10.5281/zenodo.3461034
title: Data for: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education

description: This is the raw data for the manuscript: To Tweet or Not to Tweet, That is the Question: A Randomized Trial of Twitter Effects in Medical Education. The abstract for the study is as follows: Introduction: Many medical education journals use Twitter to garner attention for their articles. The purpose of this study was to test the effects of tweeting on article page views and downloads. Methods: The authors conducted a randomized trial using <em>Academic Medicine</em> articles published in 2015. Beginning in February through May 2018, one article per day was randomly assigned to a Twitter (case) or control group. Daily, an individual tweet was generated for each article in the Twitter group that included the title, #MedEd, and a link to the article. The link delivered users to the article’s landing page, which included immediate access to the HTML full text and a PDF link. The authors extracted HTML page views and PDF downloads from the publisher. To assess differences in page views and downloads between cases and controls, a time-centered approach was used, with outcomes measured at 1, 7, and 30 days. Results: In total, 189 articles (94 cases, 95 controls) were analyzed. After days 1 and 7, there were no statistically significant differences between cases and controls on any metric. On day 30, HTML page views exhibited a 63% increase for cases (M=14.72, SD=63.68) when compared to controls (M=9.01, SD=14.34; incident rate ratio=1.63, p=0.01). There were no differences between cases and controls for PDF downloads on day 30. Discussion: Contrary to the authors’ hypothesis, only one statistically significant difference in page views between the Twitter and control groups was found. These findings provide preliminary evidence that after 30 days a tweet can have a small positive effect on article page views.
DOI: 10.6084/m9.figshare.13000193
title: Table S.8 The film usage errors for bale of diameter equal to height in relation to optimal and near-optimal bale dimensions for 3D and conventional wrapping method.

description: Table S.8 The film usage errors for bale of diameter equal to height in relation to optimal and near-optimal bale dimensions for 3D and conventional wrapping method and wrapping parameters resulting in pl=4 to 16 global film layers for pre-assumed bale volume from 1 to 1.5 m<sup>3</sup> and standard film width 0.75m.
DOI: 10.6084/m9.figshare.13000193.v1
title: Table S.8 The film usage errors for bale of diameter equal to height in relation to optimal and near-optimal bale dimensions for 3D and conventional wrapping method.

description: Table S.8 The film usage errors for bale of diameter equal to height in relation to optimal and near-optimal bale dimensions for 3D and conventional wrapping method and wrapping parameters resulting in pl=4 to 16 global film layers for pre-assumed bale volume from 1 to 1.5 m<sup>3</sup> and standard film width 0.75m.
DOI: 10.18112/openneuro.ds002152.v1.0.3
title: DMCC13benchmark
DOI: 10.5517/ccdc.csd.cc25db53
title: CCDC 2007844: Experimental Crystal Structure Determination

description: Related Article: Srabani S. Mishra, Srinath V. K. Kompella, Shobhana Krishnaswamy, Sundaram Balasubramanian, Dillip K. Chand|2020|Inorg.Chem.|59|12884|doi:10.1021/acs.inorgchem.0c01964
DOI: 10.5517/ccdc.csd.cc25db64
title: CCDC 2007845: Experimental Crystal Structure Determination

description: Related Article: Srabani S. Mishra, Srinath V. K. Kompella, Shobhana Krishnaswamy, Sundaram Balasubramanian, Dillip K. Chand|2020|Inorg.Chem.|59|12884|doi:10.1021/acs.inorgchem.0c01964
DOI: 10.5517/ccdc.csd.cc24h16x
title: CCDC 1980658: Experimental Crystal Structure Determination

description: Related Article: Alexander K. Mengele, Carolin Müller, Djawed Nauroozi, Stephan Kupfer, Benjamin Dietzek, Sven Rau|2020|Inorg.Chem.|59|12097|doi:10.1021/acs.inorgchem.0c01022
DOI: 10.5517/ccdc.csd.cc25j0f5
title: CCDC 2011386: Experimental Crystal Structure Determination

description: Related Article: Himanshu Sharma, Priyank Kumar Sharma, Soumyajit Das|2020|Chem.Commun.|56|11319|doi:10.1039/D0CC04418K
DOI: 10.5517/ccdc.csd.cc24bn8f
title: CCDC 1976444: Experimental Crystal Structure Determination

description: Related Article: Xiu-Fang Mo, Chang-Feng Xiong, Ze-Wen Chen, Chao Liu, Piao He, Hai-Xia Tong, Xiao-Yi Yi|2020|Dalton Trans.|49|12365|doi:10.1039/D0DT00185F
DOI: 10.5517/ccdc.csd.cc24bn9g
title: CCDC 1976445: Experimental Crystal Structure Determination

description: Related Article: Xiu-Fang Mo, Chang-Feng Xiong, Ze-Wen Chen, Chao Liu, Piao He, Hai-Xia Tong, Xiao-Yi Yi|2020|Dalton Trans.|49|12365|doi:10.1039/D0DT00185F
DOI: 10.5517/ccdc.csd.cc24bnbh
title: CCDC 1976446: Experimental Crystal Structure Determination

description: Related Article: Xiu-Fang Mo, Chang-Feng Xiong, Ze-Wen Chen, Chao Liu, Piao He, Hai-Xia Tong, Xiao-Yi Yi|2020|Dalton Trans.|49|12365|doi:10.1039/D0DT00185F
DOI: 10.5517/ccdc.csd.cc25jr0h
title: CCDC 2012086: Experimental Crystal Structure Determination

description: Related Article: Nicolaas P. van Leest, Wowa Stroek, Maxime A. Siegler, Jarl Ivar van der Vlugt, Bas de Bruin|2020|Inorg.Chem.|59|12903|doi:10.1021/acs.inorgchem.0c01979
DOI: 10.5517/ccdc.csd.cc25jr1j
title: CCDC 2012087: Experimental Crystal Structure Determination

description: Related Article: Nicolaas P. van Leest, Wowa Stroek, Maxime A. Siegler, Jarl Ivar van der Vlugt, Bas de Bruin|2020|Inorg.Chem.|59|12903|doi:10.1021/acs.inorgchem.0c01979
DOI: 10.5517/ccdc.csd.cc25jr2k
title: CCDC 2012088: Experimental Crystal Structure Determination

description: Related Article: Nicolaas P. van Leest, Wowa Stroek, Maxime A. Siegler, Jarl Ivar van der Vlugt, Bas de Bruin|2020|Inorg.Chem.|59|12903|doi:10.1021/acs.inorgchem.0c01979
DOI: 10.5517/ccdc.csd.cc25jr3l
title: CCDC 2012089: Experimental Crystal Structure Determination

description: Related Article: Nicolaas P. van Leest, Wowa Stroek, Maxime A. Siegler, Jarl Ivar van der Vlugt, Bas de Bruin|2020|Inorg.Chem.|59|12903|doi:10.1021/acs.inorgchem.0c01979
DOI: 10.5517/ccdc.csd.cc25jr4m
title: CCDC 2012090: Experimental Crystal Structure Determination

description: Related Article: Nicolaas P. van Leest, Wowa Stroek, Maxime A. Siegler, Jarl Ivar van der Vlugt, Bas de Bruin|2020|Inorg.Chem.|59|12903|doi:10.1021/acs.inorgchem.0c01979
DOI: 10.5517/ccdc.csd.cc25k6dc
title: CCDC 2012532: Experimental Crystal Structure Determination

description: Related Article: Tsutomu Ishi-i, Honoka Tanaka, Himiko Koga, Yuuma Tanaka, Taisuke Matsumoto|2020|J.Mater.Chem.C|8|12437|doi:10.1039/D0TC03261A
DOI: 10.5517/ccdc.csd.cc25k6fd
title: CCDC 2012533: Experimental Crystal Structure Determination

description: Related Article: Tsutomu Ishi-i, Honoka Tanaka, Himiko Koga, Yuuma Tanaka, Taisuke Matsumoto|2020|J.Mater.Chem.C|8|12437|doi:10.1039/D0TC03261A
DOI: 10.5517/ccdc.csd.cc25m98d
title: CCDC 2014543: Experimental Crystal Structure Determination

description: Related Article: Jan-Erik Siewert, André Schumann, Malte Fischer, Christoph Schmidt, Tobias Taeufer, Christian Hering-Junghans|2020|Dalton Trans.|49|12354|doi:10.1039/D0DT02435J
DOI: 10.5517/ccdc.csd.cc25m99f
title: CCDC 2014544: Experimental Crystal Structure Determination

description: Related Article: Jan-Erik Siewert, André Schumann, Malte Fischer, Christoph Schmidt, Tobias Taeufer, Christian Hering-Junghans|2020|Dalton Trans.|49|12354|doi:10.1039/D0DT02435J
DOI: 10.6084/m9.figshare.12998954
title: R0229_20181030a_videos

description: Rat 0229, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.21227/v9bf-1m46
title: An Interesting Observation on the O

description: This dataset is in support of my claim on an intersting observation on " " submitted to IEEE Transactions on Circuits and Systems I .
DOI: 10.21227/r96w-xb35
title: An Interesting Observation E

description: This dataset is in support of my claim to new observation on " " submitted to IEEE Transactions on Circuits and Syste