cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5589427
title: CStone paper: data for method S1

description: These datasets were simulated in order to demonstrate the effects of chimerism on differential expression analysis as described in supplementary method S1 of our manuscript titled “CStone: A <em>de novo</em> transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure.”. In brief from the 26,679 cDNA reference transcripts obtained from Ensembl, described for <em>D. melanogaster </em>(table 1 within manuscript), five read datasets, each containing two million read pairs, were simulated. For each, low level read count variation was introduced by allowing the number of reads required for an even coverage, as described in the manuscript, to be increased by a factor of between 0 and 0.2. The expected per site coverage provided by 2 million reads pairs across the 26,679 transcripts was slightly greater than 7X. These five replicates were allocated to condition A. A further five replicates were then simulated, where this time 1000 of the reference transcripts were selected at random for possible over-expression. For these 1000 genes the number of reads required was increased above the background variation by a factor of between 1 and 5. These five replicates were allocated to condition B. Related software to this project are:<br> 1. CStone &lt;<br> 2. CSReadGen<br> 3. CView<br> 4. ChimSim<br> 5. TVScript A related poster discussing the the identification of chimerism during assembly is available here (DOI: 10.5281/zenodo.6022493) and one discussing the effects of chimerism is available here (DOI: 10.5281/zenodo.6023170). General details of the project are available here.
description: The Portuguese Foundation for Science and Technology, FCT, supported this work through project PTDC/BIA-EVL/29115/2017. JA and RL were supported by Funds through FCT under the reference POCI-01-0145-FEDER-029115
DOI: 10.5281/zenodo.5589426
title: CStone paper: data for method S1

description: These datasets were simulated in order to demonstrate the effects of chimerism on differential expression analysis as described in supplementary method S1 of our manuscript titled “CStone: A <em>de novo</em> transcriptome assembler for short-read data that identifies non-chimeric contigs based on underlying graph structure.”. In brief from the 26,679 cDNA reference transcripts obtained from Ensembl, described for <em>D. melanogaster </em>(table 1 within manuscript), five read datasets, each containing two million read pairs, were simulated. For each, low level read count variation was introduced by allowing the number of reads required for an even coverage, as described in the manuscript, to be increased by a factor of between 0 and 0.2. The expected per site coverage provided by 2 million reads pairs across the 26,679 transcripts was slightly greater than 7X. These five replicates were allocated to condition A. A further five replicates were then simulated, where this time 1000 of the reference transcripts were selected at random for possible over-expression. For these 1000 genes the number of reads required was increased above the background variation by a factor of between 1 and 5. These five replicates were allocated to condition B. Related software to this project are:<br> 1. CStone &lt;<br> 2. CSReadGen<br> 3. CView<br> 4. ChimSim<br> 5. TVScript A related poster discussing the the identification of chimerism during assembly is available here (DOI: 10.5281/zenodo.6022493) and one discussing the effects of chimerism is available here (DOI: 10.5281/zenodo.6023170). General details of the project are available here.
description: The Portuguese Foundation for Science and Technology, FCT, supported this work through project PTDC/BIA-EVL/29115/2017. JA and RL were supported by Funds through FCT under the reference POCI-01-0145-FEDER-029115
DOI: 10.15454/vc2jei
title: INRA:Wheat:TAFERSTAT

description: Abstract:TAFERSTAT is a Wheat accession from GnpIS.
DOI: 10.15454/oswo2o
title: INRA-ONF:Populus nigra:LOT-017

description: Abstract:LOT-017 is a Populus nigra accession from GnpIS.
DOI: 10.15454/copiqu
title: INRA-ONF:Populus nigra:LOT-035

description: Abstract:LOT-035 is a Populus nigra accession from GnpIS.
DOI: 10.15454/abbzs6
title: INRA-ONF:Populus nigra:LOT-038

description: Abstract:LOT-038 is a Populus nigra accession from GnpIS.
DOI: 10.15454/8br5hp
title: INRA-ONF:Populus nigra:LOT-040

description: Abstract:LOT-040 is a Populus nigra accession from GnpIS.
DOI: 10.15454/f6gpow
title: INRA-ONF:Populus nigra:MAR-003

description: Abstract:MAR-003 is a Populus nigra accession from GnpIS.
DOI: 10.15454/hwaxvb
title: INRA-ONF:Populus nigra:MAR-005

description: Abstract:MAR-005 is a Populus nigra accession from GnpIS.
DOI: 10.15454/ninxkp
title: INRA-ONF:Populus nigra:MAR-006

description: Abstract:MAR-006 is a Populus nigra accession from GnpIS.
DOI: 10.15454/vktsyc
title: INRA-ONF:Populus nigra:MAR-008

description: Abstract:MAR-008 is a Populus nigra accession from GnpIS.
DOI: 10.15454/7icp62
title: ONF:Quercus petraea:QP.05_4102

description: Abstract:QP.05_COMPIEGNE_4102 is a Quercus petraea accession from GnpIS.
DOI: 10.3886/e162261v1
title: Adaptation Fund in Alaotra-Mangoro, Madagascar

description: This is a summary of my project
DOI: 10.5281/zenodo.6042410
title: An unenhanced abdominal CT dataset for kidney and kidney stone segmentation

description: 260 unenhanced abdominal CT scans from 2018 to 2020 were collected from the First Affiliated Hospital of Guangzhou Medical University. After data desensitization and data cleaning, the clean NIfTI image of abdominal CT scans was annotated based on clinical requirements and image segmentation needs. Two labels (“kidney” and “kidney stone”) were considered for masking and labeling. The 3D Slicer tool was used to mask the CT scans. The data annotations process comprised three stages. Data annotation was performed under the guidance of a professional urologist. Each CT scan contains an original raw image and a masked image for kidney and kidney stone. Please note that the whole dataset is split into 5 parts for easy download.
DOI: 10.5281/zenodo.6021680
title: An unenhanced abdominal CT dataset for kidney and kidney stone segmentation

description: 260 unenhanced abdominal CT scans from 2018 to 2020 were collected from the First Affiliated Hospital of Guangzhou Medical University. After data desensitization and data cleaning, the clean NIfTI image of abdominal CT scans was annotated based on clinical requirements and image segmentation needs. Two labels (“kidney” and “kidney stone”) were considered for masking and labeling. The 3D Slicer tool was used to mask the CT scans. The data annotations process comprised three stages. Data annotation was performed under the guidance of a professional urologist. Each CT scan contains an original raw image and a masked image for kidney and kidney stone. Please note that the whole dataset is split into 5 parts for easy download.
DOI: 10.5281/zenodo.6039841
title: Use of Gamification as Implementation for Software Process Improvement

description: Studies carried out in the context of software process improvement (SPI) report problems and difficulties that organizations face to implement process models and standards, mainly related to the inability to overcome some critical factors. Thus, properly conducting SPI can facilitate the development of more efficient processes to overcome such difficulties, using gamification elements. Therefore, this article aims to identify problems in the implementation phase of an SPI effort and use gamification as a tool to mitigate barriers during SPI implementation in a company that opted for an SPI program such as CMMI Dev 2.0 and /or MR-MPS-SW and create the applicability guide using gamification in SPI that can be used by software organizations. The search string returned 499 (Four hundred and Ninety-Nine) works, of which 7 (Seven) were selected according to the Systematic Review of Literature criteria.
DOI: 10.5281/zenodo.6046131
title: Use of Gamification as Implementation for Software Process Improvement

description: Studies carried out in the context of software process improvement (SPI) report problems and difficulties that organizations face to implement process models and standards, mainly related to the inability to overcome some critical factors. Thus, properly conducting SPI can facilitate the development of more efficient processes to overcome such difficulties, using gamification elements. Therefore, this article aims to identify problems in the implementation phase of an SPI effort and use gamification as a tool to mitigate barriers during SPI implementation in a company that opted for an SPI program such as CMMI Dev 2.0 and /or MR-MPS-SW and create the applicability guide using gamification in SPI that can be used by software organizations. The search string returned 499 (Four hundred and Ninety-Nine) works, of which 7 (Seven) were selected according to the Systematic Review of Literature criteria.
DOI: 10.5281/zenodo.6030031
title: Use of Gamification as Implementation for Software Process Improvement

description: Studies carried out in the context of software process improvement (SPI) report problems and difficulties that organizations face to implement process models and standards, mainly related to the inability to overcome some critical factors. Thus, properly conducting SPI can facilitate the development of more efficient processes to overcome such difficulties, using gamification elements. Therefore, this article aims to identify problems in the implementation phase of an SPI effort and use gamification as a tool to mitigate barriers during SPI implementation in a company that opted for an SPI program such as CMMI Dev 2.0 and /or MR-MPS-SW and create the applicability guide using gamification in SPI that can be used by software organizations. The search string returned 499 (Four hundred and Ninety-Nine) works, of which 7 (Seven) were selected according to the Systematic Review of Literature criteria.
DOI: 10.6084/m9.figshare.19161155
title: Detection of released free heme by erythrocytes by metabolomics

description: <b>Heme analysis by metabolomics</b><br><b>Sample Extraction</b>: metabolomics´ analyzes were performed by combining supernatants from two different solvent precipitation process: (i) 400 µL of sodium-phosphate buffer (pH 7.0) was added to 400 µL of whole blood and mixed. Then, 1200 µL of acetonitrile was added, vortexed for 10 seconds and centrifuged at 13,000 g. The same process was repeated (ii) in another 400 µL of sodium-phosphate buffer (pH 7.0) previously added to 400 µL of whole blood and mixed, but replacing the acetonitrile by 1200 µL of methanol, vortexed for 10 seconds, and centrifuged at 13,000 g. Both supernatants were transferred to a new test tube and mixed. The organic solvents were evaporated at nitrogen flow, and the extracted resuspended in 1 mL of 2% acetic acid water solution for the analysis by LC-HRMS. <b>LC-HRMS analysis:</b> The analytical platform used was an AccelaLC (ThermoFisher Scientific, Bremen, Germany) coupled to a QExactive Orbitrap Plus (MS) mass spectrometer (ThermoFisher Scientific, Bremen, Germany). Non-Target statistical analyzes were performed using a differential analysis platform, the SIEVE® software version 2.1 (ThermoFisher Scientific, Rockford, USA). The masses selected from the analysis of SIEVE were searched for in two different databases of human metabolites: Human Metabolome Database (HMDB; The metabolomics Innovation Center, Canada, USA) and MetabolomicsDatabase (METLIN ™; California, USA). <b>Chemometrics: </b>The raw data acquired with LC–HRMS were directly processed with Sieve applying the experiment type defined as “Small Molecule”, “Chromatographic Alignment and Framing”, and “Two Sample Differential Analysis” options. Sieve® software operated in two steps, comprising a first alignment of the chromatogram employing a ChromAlign™ algorithm, and second, a recursive base-peak framing, where spectral data from samples in the experiment were grouped by relative intensity. The data matrix was constructed by Sieve with the samples as observations, the m/z values and retention time pairs as the response variables, identifying statistically significant abundance differences in the signal intensity of m/z values among different samples.
DOI: 10.6084/m9.figshare.19161155.v1
title: Detection of released free heme by erythrocytes by metabolomics

description: <b>Heme analysis by metabolomics</b><br><b>Sample Extraction</b>: metabolomics´ analyzes were performed by combining supernatants from two different solvent precipitation process: (i) 400 µL of sodium-phosphate buffer (pH 7.0) was added to 400 µL of whole blood and mixed. Then, 1200 µL of acetonitrile was added, vortexed for 10 seconds and centrifuged at 13,000 g. The same process was repeated (ii) in another 400 µL of sodium-phosphate buffer (pH 7.0) previously added to 400 µL of whole blood and mixed, but replacing the acetonitrile by 1200 µL of methanol, vortexed for 10 seconds, and centrifuged at 13,000 g. Both supernatants were transferred to a new test tube and mixed. The organic solvents were evaporated at nitrogen flow, and the extracted resuspended in 1 mL of 2% acetic acid water solution for the analysis by LC-HRMS. <b>LC-HRMS analysis:</b> The analytical platform used was an AccelaLC (ThermoFisher Scientific, Bremen, Germany) coupled to a QExactive Orbitrap Plus (MS) mass spectrometer (ThermoFisher Scientific, Bremen, Germany). Non-Target statistical analyzes were performed using a differential analysis platform, the SIEVE® software version 2.1 (ThermoFisher Scientific, Rockford, USA). The masses selected from the analysis of SIEVE were searched for in two different databases of human metabolites: Human Metabolome Database (HMDB; The metabolomics Innovation Center, Canada, USA) and MetabolomicsDatabase (METLIN ™; California, USA). <b>Chemometrics: </b>The raw data acquired with LC–HRMS were directly processed with Sieve applying the experiment type defined as “Small Molecule”, “Chromatographic Alignment and Framing”, and “Two Sample Differential Analysis” options. Sieve® software operated in two steps, comprising a first alignment of the chromatogram employing a ChromAlign™ algorithm, and second, a recursive base-peak framing, where spectral data from samples in the experiment were grouped by relative intensity. The data matrix was constructed by Sieve with the samples as observations, the m/z values and retention time pairs as the response variables, identifying statistically significant abundance differences in the signal intensity of m/z values among different samples.
DOI: 10.5281/zenodo.6046077
title: Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation

description: This record contains raw data related to the article “Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation”. Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (<em>p</em> ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification.
description: This research was supported by the Italian Ministry of Health funds (RC2020-CA1B ID: 2757547) and by Fondazione Gigi e Pupa Ferrari ONLUS (FPF-14).
DOI: 10.5281/zenodo.6046076
title: Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation

description: This record contains raw data related to the article “Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation”. Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (<em>p</em> ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification.
description: This research was supported by the Italian Ministry of Health funds (RC2020-CA1B ID: 2757547) and by Fondazione Gigi e Pupa Ferrari ONLUS (FPF-14).
DOI: 10.5281/zenodo.6046097
title: Tracks for "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"

description: TempestExtremes track files for CAM5 analysis presented in "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"
DOI: 10.5281/zenodo.6046096
title: Tracks for "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"

description: TempestExtremes track files for CAM5 analysis presented in "Attribution of 2020 hurricane season extreme rainfall to human-induced climate change"
DOI: 10.6084/m9.figshare.19161635
title: Data File for "Marginal Cost of a Public School Student Depends on the State Funding Formula"

description: Data File for:<br>Joshua Hall and Erika Barker, “Marginal Cost of a Public School Student Depends on the State Funding Formula,” <i>Journal of Brief Ideas</i>, 22 October 2019.<br>
DOI: 10.6084/m9.figshare.19161635.v1
title: Data File for "Marginal Cost of a Public School Student Depends on the State Funding Formula"

description: Data File for:<br>Joshua Hall and Erika Barker, “Marginal Cost of a Public School Student Depends on the State Funding Formula,” <i>Journal of Brief Ideas</i>, 22 October 2019.<br>
DOI: 10.5281/zenodo.6037632
title: MicroED data collected from tetragonal lysozyme (April 2019)

description: Data collected at 200 kV on the JEOL 2100 LaB6 TEM at Stockholm University using a TimePix detector. The data were collected using the SoPhy control software and manual control of stage position and rotation. The raw data are TIFF files. Files converted into SMV and mrc formats are also included in the datasets. Additional files include images of the corresponding crystals in dm3 format and text files with notes on initial goniometer rotation position, spot size, exposure time and speed setting. Certain datasets have conversion errors, but are included for completeness. Processing these data has been used an example in a manuscript undergoing peer review at the time of data deposition.
DOI: 10.5281/zenodo.6037631
title: MicroED data collected from tetragonal lysozyme (April 2019)

description: Data collected at 200 kV on the JEOL 2100 LaB6 TEM at Stockholm University using a TimePix detector. The data were collected using the SoPhy control software and manual control of stage position and rotation. The raw data are TIFF files. Files converted into SMV and mrc formats are also included in the datasets. Additional files include images of the corresponding crystals in dm3 format and text files with notes on initial goniometer rotation position, spot size, exposure time and speed setting. Certain datasets have conversion errors, but are included for completeness. Processing these data has been used an example in a manuscript undergoing peer review at the time of data deposition.
DOI: 10.5281/zenodo.5541606
title: Data for "Transient density-induced dipolar interactions in a thin vapor cell"

description: Data for "Transient density-induced dipolar interactions in a thin vapor cell" as .npz files. The "datasets.csv" file lists, where each dataset is used in the journal publication. Additional there are two Python scripts (.py) which show how to load and plot a dataset. The dataset can be selected by changing the file name string in the script. Note, that for loading and plotting the simulation dataset the "plot_simulation.py" script has to be used.
DOI: 10.48432/msosbr/dslc50
title: niagara-vsp-anonymized.bag

description: :unav
DOI: 10.48432/msosbr/kvpqkg
title: ROS_BAG_INFO-niagara-vsp-anonymized.txt

description: :unav
DOI: 10.48432/msosbr/hgryj4
title: ROS_BAG_INFO-trellis-anonymized.txt

description: :unav
DOI: 10.48432/msosbr/dlmvdj
title: trellis-anonymized.bag

description: :unav
DOI: 10.48432/msosbr/g59vla
title: pushcart-2.jpg

description: :unav
DOI: 10.48432/msosbr/dp75ht
title: pushcart-1.jpg

description: :unav
DOI: 10.48432/msosbr/3kuyit
title: system-components.jpg

description: :unav
DOI: 10.48432/msosbr/jcdbpt
title: data-example.png

description: :unav
DOI: 10.48432/msosbr/jh7mav
title: ReadingTheAACr3Data.ipynb
DOI: 10.48432/msosbr/nzahg7
title: niagara-vsp.zip
DOI: 10.48432/msosbr/oe2oew
title: trellis.zip
DOI: 10.48432/msosbr/hig4r5
title: util.zip
DOI: 10.48432/msosbr/jzkyh1
title: README.md

description: This is a detailed description of the dataset, a "datasheet for the dataset", as proposed by Gebru et al. (see https://arxiv.org/abs/1803.09010v8).
DOI: 10.48432/msosbr
title: Ambient-awareness in agriculture: 3-D structure and reasoning in the crop field (AACr3).

description: This dataset was created to enable research on semantic SLAM in agriculture, using a vineyard as a case study. The research goal is the automated recovery of geo-referenced 3-D structure of crop fields and the detection and classification of objects of interest, such as trunks, leaves, and fruits. The instances are readings of different sensors: a color stereo camera, a color 4K camera, a 16-channels LIDAR, a GNSS sensor with ground-base correction (Real-Time Kinematics), and IMUs. All sensors were assembled in a system mounted on a push cart and moved along the rows of two vineyards plots. The plots presented two different growing management systems: vertical shoot positioning growing (VSP) and Y-trellis (YT). This work was funded by the São Paulo Research Foundation (FAPESP) and IBM under grant 17/19282-7, Ambient-awareness in agriculture: 3-D structure and reasoning in the crop field (AACr3). For a complete description of this dataset, see README.md.