cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3603815
title: S233

description: A fully annotated subset of the SO242/2_233-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S083: https://doi.org/10.5281/zenodo.3600132 S155: https://doi.org/10.5281/zenodo.3603803 S171: https://doi.org/10.5281/zenodo.3603809 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
DOI: 10.5281/zenodo.3603814
title: S233

description: A fully annotated subset of the SO242/2_233-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S083: https://doi.org/10.5281/zenodo.3600132 S155: https://doi.org/10.5281/zenodo.3603803 S171: https://doi.org/10.5281/zenodo.3603809 This dataset contains the following files: <code>annotations/test.csv</code>: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. <code>annotations/train.csv</code>: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. <code>images/</code>: Directory that contains all the original image files. <code>dataset.json</code>: JSON file that contains information about the dataset. <code>name</code>: The name of the dataset. <code>images_dir</code>: Name of the directory that contains the original image files. <code>metadata_file</code>: Path to the CSV file that contains image metadata. <code>test_annotations_file</code>: Path to the CSV file that contains the test annotations. <code>train_annotations_file</code>: Path to the CSV file that contains the train annotations. <code>annotation_patches_dir</code>: Name of the directory that should contain the scale- and style-transferred annotation patches. <code>crop_dimension</code>: Edge length of an annotation or style patch in pixels. <code>metadata.csv</code>: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
DOI: 10.5281/zenodo.4048491
title: Genotypes original images HR

description: Original images of six ornamental sunflower genotypes that were used in the ongoing research aimed at creating new ornamental varieties in Serbia, (45.26°N, 19.83°E). Varieties : ‘Neoplanta’, ‘Heliopa’, ‘CMS1 30’, 'Ring of Fire', ‘Pacino Gold’ and ‘Dwarf’ were used for ray floret color evaluation.
DOI: 10.5281/zenodo.4048490
title: Genotypes original images HR

description: Original images of six ornamental sunflower genotypes that were used in the ongoing research aimed at creating new ornamental varieties in Serbia, (45.26°N, 19.83°E). Varieties : ‘Neoplanta’, ‘Heliopa’, ‘CMS1 30’, 'Ring of Fire', ‘Pacino Gold’ and ‘Dwarf’ were used for ray floret color evaluation.
DOI: 10.5281/zenodo.4068168
title: Data Set from Renna LV, Bosè F, Brigonzi E, Fossati B, Meola G, Cardani R. Aberrant insulin receptor expression is associated with insulin resistance and skeletal muscle atrophy in myotonic dystrophies. PLoS One. 2019 Mar 22;14(3):e0214254. doi: 10.1371/journal.pone.0214254. PMID: 30901379; PMCID: PMC6430513.

description: Data Set from Renna LV, Bosè F, Brigonzi E, Fossati B, Meola G, Cardani R. Aberrant insulin receptor expression is associated with insulin resistance and skeletal muscle atrophy in myotonic dystrophies. PLoS One. 2019 Mar 22;14(3):e0214254. doi: 10.1371/journal.pone.0214254. PMID: 30901379; PMCID: PMC6430513. This is the abstact: Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are autosomal dominant multisystemic disorders linked to two different genetic loci and characterized by several features including myotonia, muscle atrophy and insulin resistance. The aberrant alternative splicing of insulin receptor (IR) gene and post-receptor signalling abnormalities have been associated with insulin resistance, however the precise molecular defects that cause metabolic dysfunctions are still unknown. Thus, the aims of this study were to investigate in DM skeletal muscle biopsies if beyond INSR missplicing, altered IR protein expression could play a role in insulin resistance and to verify if the lack of insulin pathway activation could contribute to skeletal muscle wasting. Our analysis showed that DM skeletal muscle exhibits a lower expression of the insulin receptor in type 1 fibers which can contribute to the defective activation of the insulin pathway. Moreover, the aberrant insulin signalling activation leads to a lower activation of mTOR and to an increase in MuRF1 and Atrogin-1/MAFbx expression, possible explaining DM skeletal muscle fiber atrophy. Taken together our data indicate that the defective insulin signalling activation can contribute to skeletal muscle features in DM patients and are probably linked to an aberrant specific-fiber type expression of the insulin receptor.
DOI: 10.5281/zenodo.4068167
title: Data Set from Renna LV, Bosè F, Brigonzi E, Fossati B, Meola G, Cardani R. Aberrant insulin receptor expression is associated with insulin resistance and skeletal muscle atrophy in myotonic dystrophies. PLoS One. 2019 Mar 22;14(3):e0214254. doi: 10.1371/journal.pone.0214254. PMID: 30901379; PMCID: PMC6430513.

description: Data Set from Renna LV, Bosè F, Brigonzi E, Fossati B, Meola G, Cardani R. Aberrant insulin receptor expression is associated with insulin resistance and skeletal muscle atrophy in myotonic dystrophies. PLoS One. 2019 Mar 22;14(3):e0214254. doi: 10.1371/journal.pone.0214254. PMID: 30901379; PMCID: PMC6430513. This is the abstact: Myotonic dystrophy type 1 (DM1) and type 2 (DM2) are autosomal dominant multisystemic disorders linked to two different genetic loci and characterized by several features including myotonia, muscle atrophy and insulin resistance. The aberrant alternative splicing of insulin receptor (IR) gene and post-receptor signalling abnormalities have been associated with insulin resistance, however the precise molecular defects that cause metabolic dysfunctions are still unknown. Thus, the aims of this study were to investigate in DM skeletal muscle biopsies if beyond INSR missplicing, altered IR protein expression could play a role in insulin resistance and to verify if the lack of insulin pathway activation could contribute to skeletal muscle wasting. Our analysis showed that DM skeletal muscle exhibits a lower expression of the insulin receptor in type 1 fibers which can contribute to the defective activation of the insulin pathway. Moreover, the aberrant insulin signalling activation leads to a lower activation of mTOR and to an increase in MuRF1 and Atrogin-1/MAFbx expression, possible explaining DM skeletal muscle fiber atrophy. Taken together our data indicate that the defective insulin signalling activation can contribute to skeletal muscle features in DM patients and are probably linked to an aberrant specific-fiber type expression of the insulin receptor.
DOI: 10.5281/zenodo.4068021
title: Results

description: Results of research published in paper "Digital image analysis using <em>FloCIA</em> software for ornamental sunflower ray floret color evaluation"
DOI: 10.5281/zenodo.4068020
title: Results

description: Results of research published in paper "Digital image analysis using <em>FloCIA</em> software for ornamental sunflower ray floret color evaluation"
DOI: 10.5281/zenodo.4068069
title: EstSoil-EH: An eco-hydrological modelling parameters dataset derived from the Soil Map of Estonia data deposit

description: For the EstSoil-EH dataset, we synthesized more than 20 extended eco-hydrological variables for Estonia as numerical and categorical values from the original Soil Map of Estonia, the Estonian 5m Lidar DEM, Estonian Topographic Database and EU-HydroSoilGrids layers. The Soil Map of Estonia maps more than 750 000 soil units throughout Estonia at a scale of 1:10 000 and forms the basis for EstSoil-EH. It is the most detailed and information-rich dataset for soils in Estonia, with 75% of mapped units smaller than 4.0 ha, based on Soviet era field mapping. For each soil unit, it describes the soil type (i.e. soil reference group), soil texture, and layer information with a composite text code, which comprises not only of the actual texture class, but also of classifiers for rock content, peat soils, distinct compositional layers and their depths. To use these as eco-hydrological process properties in modelling applications we translated the text codes into numbers. The derived parameters include soil profiles (e.g., layers, depths), texture (clay, silt, sand components), coarse fragments and rock content. In addition, we aggregated and predicted physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content, available water capacity).<br> The developed methodology and dataset will be an important resource for the Baltic region, but possibly also all other regions where detailed field-based soil mapping data is available. Countries like Lithuania and Latvia have similar historical soil records from the Soviet era that could be turned into value-added datasets such as the one we developed for Estonia. We created an extended eco-hydrological dataset for Estonia, the EstSoil-EH v1.0, containing derived numerical values for the following data in all of the mapped soil units in the 1:10 000 soil map: soil profiles (e.g., layers, depths), texture (clay, silt, and sand components), rockiness, and physical variables related to water and carbon (bulk density, hydraulic conductivity, organic carbon content). Ultimately, our objective was to develop a reproducible method for deriving numerical values to support modelling and prediction of eco-hydrological processes in Estonia using the popular Soil and Water Assessment Tool. For more information on the development of this dataset look for "EstSoil-EH v1.0: An eco-hydrological modelling parameters dataset derived from the Soil Map of Estonia", Alexander Kmoch, Arno Kanal†, Alar Astover, Ain Kull, Holger Virro, Aveliina Helm, Meelis Pärtel, Ivika Ostonen and Evelyn Uuemaa, 2019, Earth System Science Data (ESSD), ISSN 1866-3508, eISSN 1866-3516,<br> https://www.earth-syst-sci-data.net/
DOI: 10.5281/zenodo.4048474
title: 153 Segmented images for software testing

description: Accuracy of the newly-developed FloCIA software was investigated using 153 photographs of sunflowers and expert evaluator answers which had been defined as the ground truth When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\Segmented images.
DOI: 10.5281/zenodo.4048473
title: 153 Segmented images for software testing

description: Accuracy of the newly-developed FloCIA software was investigated using 153 photographs of sunflowers and expert evaluator answers which had been defined as the ground truth When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\Segmented images.
DOI: 10.5281/zenodo.4048444
title: 153 Original images for software testing

description: Accuracy of the newly-developed FloCIA software was investigated using 153 photographs of sunflowers and expert evaluator answers which had been defined as the ground truth When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\Original images.
DOI: 10.5281/zenodo.4048443
title: 153 Original images for software testing

description: Accuracy of the newly-developed FloCIA software was investigated using 153 photographs of sunflowers and expert evaluator answers which had been defined as the ground truth When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\Original images.
DOI: 10.5281/zenodo.4048482
title: Genotypes original images

description: When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\Genotypes original images.
DOI: 10.5281/zenodo.4048481
title: Genotypes original images

description: When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\Genotypes original images.
DOI: 10.5281/zenodo.4048510
title: Genotypes original images LR

description: Original images of six ornamental sunflower genotypes that were used in the ongoing research aimed at creating new ornamental varieties in Serbia, (45.26°N, 19.83°E). Varieties : ‘Neoplanta’, ‘Heliopa’, ‘CMS1 30’, 'Ring of Fire', ‘Pacino Gold’ and ‘Dwarf’ were used for ray floret color evaluation.
DOI: 10.5281/zenodo.4048509
title: Genotypes original images LR

description: Original images of six ornamental sunflower genotypes that were used in the ongoing research aimed at creating new ornamental varieties in Serbia, (45.26°N, 19.83°E). Varieties : ‘Neoplanta’, ‘Heliopa’, ‘CMS1 30’, 'Ring of Fire', ‘Pacino Gold’ and ‘Dwarf’ were used for ray floret color evaluation.
DOI: 10.5281/zenodo.4048436
title: UPOV images\Manually segmented

description: Manually segmented images from the UPOV (International Union for the Protection of New Varieties of Plants) <em>Description of Components and Varieties of Sunflower</em> (GEVES, 2000) for determination of ray florets color. When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\UPOV images\Manually segmented.
DOI: 10.5281/zenodo.4048435
title: UPOV images\Manually segmented

description: Manually segmented images from the UPOV (International Union for the Protection of New Varieties of Plants) <em>Description of Components and Varieties of Sunflower</em> (GEVES, 2000) for determination of ray florets color. When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\UPOV images\Manually segmented.
DOI: 10.5281/zenodo.4068184
title: Data set from Ferrulli A, Macrì C, Terruzzi I, Massarini S, Ambrogi F, Adamo M, Milani V, Luzi L. Weight loss induced by deep transcranial magnetic stimulation in obesity: A randomized, double-blind, sham-controlled study. Diabetes Obes Metab. 2019 Aug;21(8):1849-1860. doi: 10.1111/dom.13741. Epub 2019 May 15. PMID: 30957981.

description: Data set from Ferrulli A, Macrì C, Terruzzi I, Massarini S, Ambrogi F, Adamo M, Milani V, Luzi L. Weight loss induced by deep transcranial magnetic stimulation in obesity: A randomized, double-blind, sham-controlled study. Diabetes Obes Metab. 2019 Aug;21(8):1849-1860. doi: 10.1111/dom.13741. Epub 2019 May 15. PMID: 30957981. This is the abstract: <strong>Aim: </strong> To test the hypothesis that deep transcranial magnetic stimulation (dTMS) reduces food craving and causes weight loss via neuromodulation. <strong>Materials and methods: </strong> This pilot study was designed as a randomized, double-blind, sham-controlled study. A total of 33 obese people (nine men, 24 women, mean age 48.1 ± 10.6 years, body mass index [BMI] 36.9 ± 4.7 kg/m<sup>2</sup> ) were randomized and completed the study: 13 participants underwent a 5-week treatment with high-frequency (HF) dTMS (18 Hz; HF group), 10 were treated with low-frequency (LF) dTMS (1 Hz; LF group), and 10 were sham-treated (sham group). Food craving, and metabolic and neuro-endocrine variables were evaluated at baseline, after the 5-week treatment, and at follow-up visits (1 month, 6 months, 1 year after the end of treatment). <strong>Results: </strong> The mixed-model analysis for repeated measures showed a significant interaction of time and groups for body weight (P = 0.001) and BMI (P = 0.001), with a significant body weight (-7.83 ± 2.28 kg; P = 0.0009) and BMI (-2.83 ± 0.83, P = 0.0009) decrease in the HF versus the sham group. A decreasing trend in food craving in the HF versus the LF and sham groups (P = 0.073) was observed. A significant improvement of metabolic and physical activity variables was found (P &lt; 0.05) in the HF group. <strong>Conclusions: </strong> We demonstrated the safety and efficacy of dTMS, in addition to physical exercise and a hypocaloric diet, in reducing body weight for up to 1 year in obese people. We hypothesize that a possible mechanism of HF dTMS treatment is modulation of the dopaminergic pathway and stimulation of physical activity.
DOI: 10.5281/zenodo.4068183
title: Data set from Ferrulli A, Macrì C, Terruzzi I, Massarini S, Ambrogi F, Adamo M, Milani V, Luzi L. Weight loss induced by deep transcranial magnetic stimulation in obesity: A randomized, double-blind, sham-controlled study. Diabetes Obes Metab. 2019 Aug;21(8):1849-1860. doi: 10.1111/dom.13741. Epub 2019 May 15. PMID: 30957981.

description: Data set from Ferrulli A, Macrì C, Terruzzi I, Massarini S, Ambrogi F, Adamo M, Milani V, Luzi L. Weight loss induced by deep transcranial magnetic stimulation in obesity: A randomized, double-blind, sham-controlled study. Diabetes Obes Metab. 2019 Aug;21(8):1849-1860. doi: 10.1111/dom.13741. Epub 2019 May 15. PMID: 30957981. This is the abstract: <strong>Aim: </strong> To test the hypothesis that deep transcranial magnetic stimulation (dTMS) reduces food craving and causes weight loss via neuromodulation. <strong>Materials and methods: </strong> This pilot study was designed as a randomized, double-blind, sham-controlled study. A total of 33 obese people (nine men, 24 women, mean age 48.1 ± 10.6 years, body mass index [BMI] 36.9 ± 4.7 kg/m<sup>2</sup> ) were randomized and completed the study: 13 participants underwent a 5-week treatment with high-frequency (HF) dTMS (18 Hz; HF group), 10 were treated with low-frequency (LF) dTMS (1 Hz; LF group), and 10 were sham-treated (sham group). Food craving, and metabolic and neuro-endocrine variables were evaluated at baseline, after the 5-week treatment, and at follow-up visits (1 month, 6 months, 1 year after the end of treatment). <strong>Results: </strong> The mixed-model analysis for repeated measures showed a significant interaction of time and groups for body weight (P = 0.001) and BMI (P = 0.001), with a significant body weight (-7.83 ± 2.28 kg; P = 0.0009) and BMI (-2.83 ± 0.83, P = 0.0009) decrease in the HF versus the sham group. A decreasing trend in food craving in the HF versus the LF and sham groups (P = 0.073) was observed. A significant improvement of metabolic and physical activity variables was found (P &lt; 0.05) in the HF group. <strong>Conclusions: </strong> We demonstrated the safety and efficacy of dTMS, in addition to physical exercise and a hypocaloric diet, in reducing body weight for up to 1 year in obese people. We hypothesize that a possible mechanism of HF dTMS treatment is modulation of the dopaminergic pathway and stimulation of physical activity.
DOI: 10.5281/zenodo.4048518
title: UPOV images\Original

description: Images from the UPOV (International Union for the Protection of New Varieties of Plants) <em>Description of Components and Varieties of Sunflower</em> for determination of ray florets color. UPOV (2000). Guidelines for the Conduct of Tests for Distinctness, Uniformity and Stability: Sunflower (<em>Helianthus annuus</em> L.). TG/81/6. Geneva, Switzerland: International Union for the Protection of New Varieties of Plants. Available from: http://www.upov.int/edocs/tgdocs/en/tg081.pdf When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\UPOV images\Manually segmented.
DOI: 10.5281/zenodo.4048517
title: UPOV images\Original

description: Images from the UPOV (International Union for the Protection of New Varieties of Plants) <em>Description of Components and Varieties of Sunflower</em> for determination of ray florets color. UPOV (2000). Guidelines for the Conduct of Tests for Distinctness, Uniformity and Stability: Sunflower (<em>Helianthus annuus</em> L.). TG/81/6. Geneva, Switzerland: International Union for the Protection of New Varieties of Plants. Available from: http://www.upov.int/edocs/tgdocs/en/tg081.pdf When using with the FloCIA software, these images should be in the folder: C:\FloCIA\Sunflowers\UPOV images\Manually segmented.
DOI: 10.5281/zenodo.4048513
title: Genotypes segmented images

description: Segmented images of six ornamental sunflower genotypes that were used in the ongoing research aimed at creating new ornamental varieties in Serbia, (45.26°N, 19.83°E). Varieties : ‘Neoplanta’, ‘Heliopa’, ‘CMS1 30’, 'Ring of Fire', ‘Pacino Gold’ and ‘Dwarf’ were used for ray floret color evaluation.
DOI: 10.5281/zenodo.4048512
title: Genotypes segmented images

description: Segmented images of six ornamental sunflower genotypes that were used in the ongoing research aimed at creating new ornamental varieties in Serbia, (45.26°N, 19.83°E). Varieties : ‘Neoplanta’, ‘Heliopa’, ‘CMS1 30’, 'Ring of Fire', ‘Pacino Gold’ and ‘Dwarf’ were used for ray floret color evaluation.
DOI: 10.6084/m9.figshare.13054184
title: R0160_20170525a_videos

description: Rat 0160, skilled reaching data collected in Leventhal laboratory by Alexandra Bova
DOI: 10.5281/zenodo.3897401
title: Data and results from Spage2vec: Unsupervised representation of localized spatial gene expression signatures

description: This repository contains spatial gene expression data for reproducing analysis results of spage2vec manuscript [1]. [1] Partel G, Wählby C. Spage2vec: Unsupervised representation of localized spatial gene expression signatures. The FEBS Journal. 2020. Acknowledgement: We thank Xiaoyan Qian, Simone Codeluppi, Jeffrey Moffitt, Kok Hao Chen, Alistair N. Boettiger and Dhananjay Bambah-Mukku for generating and sharing in situ sequencing [2], osmFISH [3] and MERFISH [4-5] datasets. And Jeongbin Park, Wonyl Choi for uploading osmFISH [3] and MERFISH [4] data to public repository. [2] Qian, Xiaoyan, et al. "Probabilistic cell typing enables fine mapping of closely related cell types in situ." Nature methods 17.1 (2020): 101-106. [3] Codeluppi, Simone, et al. "Spatial organization of the somatosensory cortex revealed by osmFISH." Nature methods 15.11 (2018): 932-935. [4] Moffitt, Jeffrey R., et al. "Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region." Science 362.6416 (2018): eaau5324. [5] Chen, Kok Hao, et al. "Spatially resolved, highly multiplexed RNA profiling in single cells." <em>Science</em> 348.6233 (2015): aaa6090.