cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/4hh45vkp38.3
title: Data for: Deep Learning-based Early Weed Segmentation using UAV Images of Sorghum Fields

description: Weeds are undesired plants in agricultural fields that affect crop yield and quality by competing for nutrients, water, sunlight and space. Site-specific weed management (SSWM) through variable rate herbicide application and mechanical weed control have long been recommended in order to reduce the amount of herbicide and impact caused by uniform spraying. Accurate detection and classification of weeds in crop fields is a crucial first step for implementing such precise strategies. Drones are commonly used for image capturing but high wind pressure and different drone settings have a severe effect on the image quality, which potentially results in degraded images, e.g. due to motion blur. 
We publish a manually annotated and expert curated drone image dataset for weed detection in sorghum fields under challenging conditions. Our results show that our trained models generalize well regarding the detection of weeds, even for degraded captures due to motion blur. An UNet-like architecture with ResNet-34 as feature extractor achieved an F1-score of over 89 % on a hold-out test-set. Further analysis indicate that the trained model performed well in predicting the general plant shape, while most mis-classifications appeared at borders of the plants. Beyond that, our approach can detect intra-row weeds without additional information as well as partly occluded plants in contrast to existing research.

Github link: https://github.com/grimmlab/UAVWeedSegmentation
DOI: 10.5281/zenodo.7541190
title: Estimating Compositions and Nutritional Values of Seed Mixes based on Vision Transformers

description: The cultivation of seed mixtures for local pastures is a traditional mixed cropping techniques of cereals and legumes for producing at a low production cost, a balanced animal feed in energy and protein in livestock systems. By considerably improving the autonomy and safety of agricultural systems, as well as reducing their impact on the environment, it is a type of crop that responds favorably both to the evolution of the European regulations on the use phyto-sanitary products, and the expectations of consumers who wish to increase their consumption of organic products. However, farmers find it difficult to adopt it because cereals and legumes do not ripen synchronously and the harvested seeds are heterogeneous, making it more difficult to assess their nutritional value. Many efforts therefore remain to be made to acquire and aggregate technical and economical references to evaluate to what extent the cultivation of seed mixtures could positively contribute to secure and reduce costs on herd feeding. The work presented in this paper proposes to evaluate recent deep learning techniques that could be transferred to an online or smartphone application to automatically estimate the nutritive value of harvested seed mixes to help farmers better managing the yield and thus engage them to promote and contribute to better knowledge of this type of cultivation. For this purpose, we have built an original image dataset containing 4,749 images of seed mixes, covering 11 seed varieties, with which we have compared 2 types of deep learning models. Our results highlight the potential of this method, and show that the best performing model is a recent state-of-the-art Vision Transformer pre-trained with self-supervision (BeiT). It allows an estimation of the nutritive value of seed mixtures with a coefficient of determination \(R^2\) Score of 0.91, which demonstrates the interest of this type of approach, for its possible use on a large scale.
DOI: 10.5281/zenodo.7541191
title: Estimating Compositions and Nutritional Values of Seed Mixes based on Vision Transformers

description: The cultivation of seed mixtures for local pastures is a traditional mixed cropping techniques of cereals and legumes for producing at a low production cost, a balanced animal feed in energy and protein in livestock systems. By considerably improving the autonomy and safety of agricultural systems, as well as reducing their impact on the environment, it is a type of crop that responds favorably both to the evolution of the European regulations on the use phyto-sanitary products, and the expectations of consumers who wish to increase their consumption of organic products. However, farmers find it difficult to adopt it because cereals and legumes do not ripen synchronously and the harvested seeds are heterogeneous, making it more difficult to assess their nutritional value. Many efforts therefore remain to be made to acquire and aggregate technical and economical references to evaluate to what extent the cultivation of seed mixtures could positively contribute to secure and reduce costs on herd feeding. The work presented in this paper proposes to evaluate recent deep learning techniques that could be transferred to an online or smartphone application to automatically estimate the nutritive value of harvested seed mixes to help farmers better managing the yield and thus engage them to promote and contribute to better knowledge of this type of cultivation. For this purpose, we have built an original image dataset containing 4,749 images of seed mixes, covering 11 seed varieties, with which we have compared 2 types of deep learning models. Our results highlight the potential of this method, and show that the best performing model is a recent state-of-the-art Vision Transformer pre-trained with self-supervision (BeiT). It allows an estimation of the nutritive value of seed mixtures with a coefficient of determination \(R^2\) Score of 0.91, which demonstrates the interest of this type of approach, for its possible use on a large scale.
DOI: 10.5281/zenodo.8169473
title: Estimating Compositions and Nutritional Values of Seed Mixes based on Vision Transformers

description: The cultivation of seed mixtures for local pastures is a traditional mixed cropping techniques of cereals and legumes for producing at a low production cost, a balanced animal feed in energy and protein in livestock systems. By considerably improving the autonomy and safety of agricultural systems, as well as reducing their impact on the environment, it is a type of crop that responds favorably both to the evolution of the European regulations on the use phyto-sanitary products, and the expectations of consumers who wish to increase their consumption of organic products. However, farmers find it difficult to adopt it because cereals and legumes do not ripen synchronously and the harvested seeds are heterogeneous, making it more difficult to assess their nutritional value. Many efforts therefore remain to be made to acquire and aggregate technical and economical references to evaluate to what extent the cultivation of seed mixtures could positively contribute to secure and reduce costs on herd feeding. The work presented in this paper proposes to evaluate recent deep learning techniques that could be transferred to an online or smartphone application to automatically estimate the nutritive value of harvested seed mixes to help farmers better managing the yield and thus engage them to promote and contribute to better knowledge of this type of cultivation. For this purpose, we have built an original image dataset containing 4,749 images of seed mixes, covering 11 seed varieties, with which we have compared 2 types of deep learning models. Our results highlight the potential of this method, and show that the best performing model is a recent state-of-the-art Vision Transformer pre-trained with self-supervision (BeiT). It allows an estimation of the nutritive value of seed mixtures with a coefficient of determination \(R^2\) Score of 0.91, which demonstrates the interest of this type of approach, for its possible use on a large scale.
DOI: 10.5281/zenodo.8177571
title: Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug Responses

description: Patient-derived organoids (PDOs) can model personalized therapy responses, however current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly-multiplexed mass cytometry platform to measure post translational modification (PTM) signaling, DNA-damage, cell-cycle activity, and apoptosis in &gt;2,500 colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed <em>Trellis</em> — a highly-scalable, hierarchical tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rare, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate cancer cell plasticity — shifting proliferative stem cells to slow-cycling revival stem cells via YAP to protect cancer cells from chemotherapy. This repo contains the processed scRNA-seq Scanpy AnnData objects generated from the study. More information describing the data can be found at: https://github.com/TAPE-Lab/Ramos-et-al-Trellis
DOI: 10.5281/zenodo.8177570
title: Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug Responses

description: Patient-derived organoids (PDOs) can model personalized therapy responses, however current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly-multiplexed mass cytometry platform to measure post translational modification (PTM) signaling, DNA-damage, cell-cycle activity, and apoptosis in &gt;2,500 colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed <em>Trellis</em> — a highly-scalable, hierarchical tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rare, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate cancer cell plasticity — shifting proliferative stem cells to slow-cycling revival stem cells via YAP to protect cancer cells from chemotherapy. This repo contains the processed scRNA-seq Scanpy AnnData objects generated from the study. More information describing the data can be found at: https://github.com/TAPE-Lab/Ramos-et-al-Trellis
DOI: 10.17632/c3zhzgwsnw.1
title: Generation of Peptide Libraries: Applying Various In silico Enzymatic Digestion Protocols on the Composite Protein Database (nr).

description: Peptide Diversity Generation through In silico Enzymatic Digestion of the Composite Protein Database (nr) from Cephalopod's Posterior Salivary Glands resulted in 13 distinct peptide libraries. These libraries were created using various digestion protocols, including just one enzyme (Trypsin, Chymotrypsin, Proteinase-K, AspN, and GluC), sequential mode with two enzymes (Tryp-Chym, Tryp-ProtK, Tryp-AspN, and Tryp-GluC), and concurrent mode with two enzymes (Tryp-Chym, Tryp-ProtK, Tryp-AspN, and Tryp-GluC).
DOI: 10.17632/4wkt6thgp6.1
title: Data for: Accurate Machine Learning-Based Germination Detection, Prediction and Quality Assessment of Various Seed Cultivars

description: Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done mainly manual, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks for accurate seed germination detection for high-throughput seed germination experiments. This dataset contains captures of Germination Experiments of 824 Zea mays seeds, 811 Secale cereale seeds and 814 Pennisetum glaucum seeds. Approximately 10 seeds were placed in one petri dish and captured by a low-cost Raspberry Pi Camera Module (v2.1) in intervals of 30 minutes for ~ 2 days. All Images were annotated by Bounding Boxes containing their germination state (germinated/non-germinated). The code for running the models that are built with this data can be found on GitHub (https://github.com/grimmlab/GerminationPrediction).
DOI: 10.17632/4wkt6thgp6.3
title: Data for: Accurate Machine Learning-Based Germination Detection, Prediction and Quality Assessment of Various Seed Cultivars

description: Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done mainly manual, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks for accurate seed germination detection for high-throughput seed germination experiments.

This dataset contains captures of Germination Experiments of 824 Zea mays seeds, 811 Secale cereale seeds and 814 Pennisetum glaucum seeds. Approximately 10 seeds were placed in one petri dish and captured by a low-cost Raspberry Pi Camera Module (v2.1) in intervals of 30 minutes for ~ 2 days. All Images were annotated by Bounding Boxes containing their germination state (germinated/non-germinated). The code for running the models that are built with this data can be found on GitHub (https://github.com/grimmlab/GerminationPrediction). 
Please cite our original publication if you have used the data in your project or in any follow-up analysis (https://doi.org/10.1186/s13007-020-00699-x):

@article{genze_accurate_2020,
	title = {Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops},
	volume = {16},
	issn = {1746-4811},
	url = {https://doi.org/10.1186/s13007-020-00699-x},
	doi = {10.1186/s13007-020-00699-x},
	abstract = {Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments.},
	number = {1},
	journal = {Plant Methods},
	author = {Genze, Nikita and Bharti, Richa and Grieb, Michael and Schultheiss, Sebastian J. and Grimm, Dominik G.},
	month = dec,
	year = {2020},
	pages = {157},
}
DOI: 10.17632/khm5gy5m65.1
title: Native aortic valve thrombus on transesophageal echocardiography 

description: Detection of thrombus on the native aortic valve on transesophageal echocardiography led to alteration of surgical approach in a case with Double outlet right ventricle: 
1. Double outlet right ventricle with right to left shunting of blood through the ventricular septal defect
2. Incidental native aortic valve thrombus on the non-coronary cusp 
3. Surgical removal of thrombus
DOI: 10.17632/r5xyh53ps4.1
title: The study of metformin on T2DM

description: Uncropped western blots and information for human individuals
DOI: 10.17632/tf2sc36vpy.1
title: Table S1

description: Table S1
DOI: 10.17632/gxmkytwdhx.1
title: Composite Protein Database (nr) from Cephalopod Salivary Apparatus for In silico Enzymatic Digestion and Peptide Library Generation

description: The database includes proteins and translated transcriptomes from the posterior salivary glands (PSG) of Octopus vulgaris and 16 other cephalopods. It will be utilised to generate peptide libraries using various in silico enzymatic digestion protocols to explore peptide diversity.
DOI: 10.17632/h7xh442ncb.1
title: Weibo data related to earthquakes between 2010-2019

description: 5358 data related to 3 Chinese earthquakes between 2010- 2019
DOI: 10.17632/hgwkkmms3h.1
title: Omics Datasets to Create a Composite Protein Database from Cephalopod Salivary Apparatus for In silico Enzymatic Digestion and Peptide Library Generation

description: Database A—protein database from proteogenomic analyses of the Octupus vulgaris salivary apparatus, built by Fingerhut et al. (2018). DOI: 10.1021/acs.jproteome.8b00525, but retrieved from DOI: doi.org/10.3390/data5040110.
Database C—proteins identified with Proteome Discoverer using 12 raw files against the UniProt database for the Metazoan taxonomic selection, built by Almeida et. al (2020) DOI: 10.3390/antibiotics9110757, but retrieved from DOI: doi.org/10.3390/data5040110.
Database D—proteins identified from de novo transcriptome assemblies of 16 cephalopods’ Post Salivary Glands by TransDecoder, built by Almeida et. al (2020) DOI: 10.3390/antibiotics9110757, but retrieved from DOI: doi.org/10.3390/data5040110.
Database E—proteins identified from de novo transcriptome assemblies of 16 cephalopods’ PSGs using a six-frame translation tool, which are not included in Database D, built by Almeida et. al (2020) DOI: 10.3390/antibiotics9110757, but retrieved from DOI: doi.org/10.3390/data5040110.
Database F—proteins obtained using a six-frame translation tool using the transcripts profiled in the transcriptome of O. vulgaris [10.1021/acs.jproteome.8b00525] , but not included in Database A. Built by Almeida et. al (2020) DOI: 10.3390/antibiotics9110757, but retrieved from DOI: doi.org/10.3390/data5040110.
DOI: 10.6084/m9.figshare.23706093.v4
title: Supplemental Material for Peterson et al., 2023

description: Supplemental information for ‘Genetic variants in melanogenesis proteins <em>TYRP1</em> and <em>TYR </em>are associated with the golden rhesus macaque phenotype’
DOI: 10.6084/m9.figshare.23737416
title: Embryoid body development

description: AnnData object of scRNA-seq dataset of embryoid bodies development with experimental time points at 5 different stages. <br> Please cite the original publication: https://doi.org/10.1038/s41587-019-0336-3
DOI: 10.6084/m9.figshare.23706093
title: Supplemental Material for Peterson et al., 2023

description: Supplemental information for ‘Genetic variants in melanogenesis proteins <em>TYRP1</em> and <em>TYR </em>are associated with the golden rhesus macaque phenotype’
DOI: 10.6084/m9.figshare.23706093.v6
title: Supplemental Material for Peterson et al., 2023

description: Supplemental information for ‘Genetic variants in melanogenesis proteins <em>TYRP1</em> and <em>TYR </em>are associated with the golden rhesus macaque phenotype’
DOI: 10.6084/m9.figshare.23706093.v5
title: Supplemental Material for Peterson et al., 2023

description: Supplemental information for ‘Genetic variants in melanogenesis proteins <em>TYRP1</em> and <em>TYR </em>are associated with the golden rhesus macaque phenotype’
DOI: 10.5281/zenodo.8179109
title: Data for "Reversal learning of visual cues in Heliconiini butterflies"

description: Here I provide the data and R code used in the analyses included in the paper "Reversal learning of visual cues in Heliconiini butterflies".
DOI: 10.5281/zenodo.8179108
title: Data for "Reversal learning of visual cues in Heliconiini butterflies"

description: Here I provide the data and R code used in the analyses included in the paper "Reversal learning of visual cues in Heliconiini butterflies".
DOI: 10.34740/kaggle/dsv/6186245
title: SnakeFace
DOI: 10.5519/qd.2msgdeft
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1238 records)
DOI: 10.5281/zenodo.8166250
title: MOM6 M2 tides with porous barriers

description: This repository stores MOM6 model input and output for barotropic tides with porous barriers. input.tar.gz provides all input files for the baseline simulations. Input for experiments with coarsened resolutions can be generated by write_input.py run.tar provides runtime parameters for model experiments. output.tar.gz stores the key output from each experiment.
DOI: 10.5281/zenodo.8166251
title: MOM6 M2 tides with porous barriers

description: This repository stores MOM6 model input and output for barotropic tides with porous barriers. input.tar.gz provides all input files for the baseline simulations. Input for experiments with coarsened resolutions can be generated by write_input.py run.tar provides runtime parameters for model experiments. output.tar.gz stores the key output from each experiment.
DOI: 10.17637/rh.23736189
title: Raw data for ethnopharmacological paper

description: Raw data files for our ethnopharmacological paper, including a readme file.
DOI: 10.5281/zenodo.8179111
title: Compilation of tight ASP programs

description: This repository contains benchmarks and executable used to assess the performance of ProASP systems.
DOI: 10.5281/zenodo.8179110
title: Compilation of tight ASP programs

description: This repository contains benchmarks and executable used to assess the performance of ProASP systems.
DOI: 10.6084/m9.figshare.21397158.v30
title: South African LNCS papers 1973-2022

description: This study investigated the research participation of South African (RSA) authors in LNCS for the period 1973 to 2022.
DOI: 10.3886/e192294v1-140387
title: Data and code for: Women's Labor Force Exits during COVID-19: Differences by Motherhood, Race, and Ethnicity

description: While the descriptive impacts of the pandemic on women have been well documented in the aggregate, we know much less about the impacts of the pandemic on different groups of women. After controlling for detailed job and demographic characteristics, including occupation and industry, we find that the pandemic led to significant excess labor force exits among women living with children under age six relative to women without children. We also find evidence of larger increases in exits among lower-earning women. The presence of children predicted larger increases in exits during the pandemic among Latina and Black women relative to White women. Overall, we find evidence that pandemic induced disruptions to childcare, including informal care from family and friends. Our results suggest that the unique effect of childcare disruptions during the pandemic exacerbated pre-existing racial and income inequalities among women.
description: People 16 and older.
DOI: 10.34740/kaggle/ds/3551778
title: Persian News Dataset
DOI: 10.5517/ccdc.csd.cc2bnx31
title: CCDC 2165043: Experimental Crystal Structure Determination

description: Related Article: Shuting Xu, Bo Zhang, Yunwu Li, Yan Yang, Jun Li, Jiandong Pang|2023|Inorg.Chem.|62|11168|doi:10.1021/acs.inorgchem.3c01290
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.