cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/7jw62vmkm5.1
title: Sunopta financial data

description: SunOpta is a commodity fruit and plant milk producer benefiting from excitement around oat milk.  The stock spiked and is returning to a lower level, as it tends to do historically after investors get burned by management's hype and remember that this is a terrible business.

 

Let's start on oat milk, which is growing quickly and is the main source of investor excitement.  Making oat milk is easy-- oats, water, and a blender.  You can do it at home if you want, at a fraction of the cost of packaged oat milk.  Their largest customer is Starbucks, which has been pushing oat milk, and STKL has been building the capacity for them as well as Planet Oat, owned by Hood.

Per the Chobani S-1, oat milk market size in 2021 was $370M at retail.  It has grown since then, mostly through pricing.  Back of the envelope, assuming that oat milk was $3/half gallon in 2021 (and $4 now), then this was around 240M liters.  STKL is building a new plant in Fort Worth.  Oatly/Ya Ya are also building a new plant in Fort Worth.  The combined capacities of these plants are 300M liters.  Even assuming strong volume demand growth since 2021, the impact on supply will be massive and should tip the market into oversupplied.  Since oat milk was 18% of the plant-based category in 2021 (per Chobani), it is likely 25-30% now, implying that share gains may be tougher to come by.

Last quarter, OTLY reported 7% volume growth.  Assuming STKL saw the same pricing growth as OTLY (30%), then their volumes grew 3%.  Overall Q1 revenues and EBIT were down for STKL in Q1.  I don't think it's crazy to say that increasing market supply >50% into flattish volume trends will lead to pricing pressure, and it seems likely that revenue growth could/should be negative for this segment over the next 12 months.  OTLY is still a customer of STKL, and will in-source that production, which is an additional headwind.  Additionally, oat prices have mean-regressed after a sharp spike, and as OTLY is paid a processing fee with oats as a pass-through cost, this should also pressure the topline.  

And what if oat milk is a true fad?  This seems possible to me-- after all, it doesn't taste that great.  A weak consumer might balk at paying $1 upcharges on their lattes, or another trendy plant might be blended into a new "milk".
DOI: 10.17632/fgvxctvw5y.1
title: Data for Metaheuristics for the no-wait flow shop scheduling problem with single server to minimize total tardiness

description: This data set represents randomly generated instances for the problem for the flow shop scheduling problem with single server to minimize total tardiness. The folder includes three files, one for the description of instances and two files for small and large instances respectively.
DOI: 10.17632/gh3trr7p9x.1
title: (WTO) in Escalating International Trade

description: No doubt, the establishment of WTO has been an excellent achievement for international trade due to the liberalization of trade and the removal of barriers and tariffs that came with it.
DOI: 10.17632/fz6f2prz8y.1
title: Towards Enhancing Novice Learners’ Programming Skills: Lessons Learned from a MOOC-based Approach

description: The dataset encompasses diagnostic and perception survey responses, final grades, and screenshots of the developed MOOC, providing support for the results presented in the paper.
DOI: 10.17632/s3yy42kp83.1
title: Supplementary materials for "Real-world characterization of skin toxicities and treatment interruptions associated with epidermal growth factor receptor tyrosine kinase inhibitors: A retrospective, single-center study"

description: # Supplementary Material and Method
# Supplementary table 1
# Supplementary table 2
DOI: 10.17632/5y2pj9y2bc.1
title: Efficacy and safety of oral upadacitinib monotherapy in patients with recalcitrant vitiligo

description: Mendeley supplemental figure 1.  Patients 1 and 10 before and after 8 and 16 weeks of treatment
DOI: 10.17632/6hvcvnmxgm.1
title: The influence of dimethyl sulfoxide on the flow behavior of calcium silicate cement in root canal obturation

description: This study aims to develop and introduce calcium silicate cement containing DMSO for automated root canal fillings. 
We hypothesize that DMSO would not alter the flow characteristics of calcium silicate cements. The second hypothesis was that DMSO would not affect the apico-coronal extension of root canal fillings in simulated root canals. The third hypothesis was that DMSO would not affect the microleakage of an extracted human tooth. 
There are three types of data in the data set.
The first data show flow characteristics of calcium silicate cements(CSC and DMSO_CSC) in the gypsum and resin channel. The flow behavior of calcium silicate cement in each channel was observed using a high-speed camera , then filling length was analyzed using the video. 

The second data show apico-coronal extension of root canal obturation in 8 different types of simulated root canals filled with CSC or DMSO_CSC. The apico-coronal extension was measured and analyzed based on its filling material (CSC or DMSO_CSC), apical size of the canal, taper of the canal, and length of the canal. 

The third data show nano-scaled leakage of root canal fillings of CSC or DMSO_CSC or control (gutta percha with AH plus sealer).
DOI: 10.17632/t4pphrvfzy.1
title: Blood multi-omics characteristics of an ADARB1-centred network in Alzheimer's disease and Parkinson's disease

description: Dataset of RNA and metabolite used in the paper
DOI: 10.17632/t4pphrvfzy.2
title: Orchestration of a blood-derived and ADARB1-centred network in Alzheimer's and Parkinson's disease

description: To better understand the relationship between peripheral blood leukocyte and brain-based epigenetic activity in the context of Alzheimer's disease (AD) and Parkinson's disease (PD), we conducted a pilot study on peripheral blood leukocytes by comprehensively analysing transcriptomic and metabolic features in blood (NCONTROL = 15; NAD = 11; PD = 13). Using next-generation sequencing, we identified 766 miRNAs and 19,512 mRNAs/lncRNAs after removing low-quality reads and low-abundance RNAs. Using LC-MS/MS, we identified 8 DE metabolites of 7 short-chain fatty acids and 23 neurotransmitters. Using a comprehensive integrative analysis of multi-omics data, we identified an ADARB1-centred co-expression sub-network consisting of ADARB1, LINC02960 and 109 miRNAs simultaneously correlated with diagnosis, glutamate and phenethylamine. A putative interaction within the ADARB1 module was raised in the blood ecosystem of AD and PD.
DOI: 10.17632/dy79j7bgnf.1
title: AvoStemsDB

description: The image set consists of 133 images grouped into two classes of diseased and healthy stems with a distribution of 65 and 68 images respectively. Each image has a dimension of 500 x 400 pixels with a depth of 96 dpi and a .jpg extension.
DOI: 10.6084/m9.figshare.23737116
title: Murine intestinal organid scEU-seq data - Preprocessed

description: scRNA-seq dataset of murine intestinal organoids metabolically labeled and sequenced with scEU-seq. The data has been preprocessed (filtered, normalized, PCA, neighbors, UMAP). See here for details. <br> Please cite the original publication: https://doi.org/10.1126/science.aax3072
DOI: 10.25345/c5jm23s1g
title: MassIVE MSV000092512 - Zhang EPESE Extracellular Vesicles Discovery Proteomics
title: Quantitative LC/MS/MS was performed on 2 uL (1 ug) of each sample, using a nanoAcquity UPLC system (Waters Corp) coupled to a Thermo Orbitrap Fusion Lumos high resolution accurate mass tandem mass spectrometer (Thermo) equipped with a FAIMSPro device via a nanoelectrospray ionization source. Briefly, the sample was first trapped on a Symmetry C18 20 mm x 180 um trapping column (5 ul/min at 99.9/0.1 v/v water/acetonitrile), after which the analytical separation was performed using a 1.8 um Acquity HSS T3 C18 75 um x 250 mm column (Waters Corp.) with a 90-min linear gradient of 5 to 30% acetonitrile with 0.1% formic acid at a flow rate of 400 nanoliters/minute (nL/min) with a column temperature of 55C. Data collection on the Fusion Lumos mass spectrometer was performed for three difference compensation voltages (-40v, -60v, -80v). Within each CV, a data-dependent acquisition (DDA) mode of acquisition with a r 120,000 (m/z 200) full MS scan from m/z 375 - 1500 with a target AGC value of 4e5 ions was performed. MS/MS scans were acquired in the Orbitrap at r 50,000 ( m/z 200) from m/z 100 with a target AGC value of 1e5 and max fill time of 35 ms. The total cycle time for each CV was 1s, with total cycle times of 3 sec between like full MS scans. A 20s dynamic exclusion was employed to increase depth of coverage. The total analysis cycle time for each sample injection was approximately 2 hours.
DOI: 10.17632/c6zvtcnmgs.1
title: Using barium isotopes to distinguish metamorphic and magmatic fluids for the gold deposits

description: data of differences between magmatic and metamorphic fluids (Table A1), major and trace elements (Table A2), and Ba isotopes of  of various orerocks from the Hadamen and Haoyaoerhudong gold deposits
DOI: 10.17632/p279c9gxsd.1
title: Revisão de Sistemática Bibliográfica: Planejamento e Controle da Produção aplicado a serrarias

description: Identificar quais são os conceitos, guias, ferramentas, técnicas, modelos e práticas existentes na literatura sobre a planejamento e controle da produção e como se desdobram em ações para o gerenciamento das operações produtivas
DOI: 10.17632/zt2j5j4v44.1
title: Perovskite-Thin-Films

description: A dataset containing scans of MAPbI thin films, Photoluminescence- and UV-Vis-spectra as well as thickness measurements.
DOI: 10.17632/zt2j5j4v44.2
title: Perovskite-Thin-Films

description: A dataset containing scans of Triple-Cation Lead Bromide Iodide thin films, Photoluminescence- and UV-Vis-spectra as well as thickness measurements.
DOI: 10.17632/zt2j5j4v44.3
title: Perovskite-Thin-Films

description: A dataset containing scans of Triple-Cation Lead Bromide Iodide thin films, Photoluminescence- and UV-Vis-spectra as well as thickness measurements. The results of a study on the dataset was published in https://www.sciencedirect.com/science/article/pii/S0038092X23004656. The corresponding code can be found at https://github.com/Galdramadhur/Perovskite-Thin-Film-Characterization.
DOI: 10.17632/4gyr7htmzw.1
title: Rachel E. Szabo, PhD Thesis, Supplementary Material (August 2023)

description: This dataset contains large supplementary tables referenced in Rachel E. Szabo's doctoral thesis (submitted to MIT in August 2023).
DOI: 10.17632/tm2t9pmwbg.1
title: Genomics Data of L. borgpetersenii strain HP364 and L. weilii strain SC295 isolated from Rodents in Malaysia

description: Genomics data of L. borgpetersenii strain HP364 and L. weilii strain SC295.
L. borgpetersenii strain HP364 and L. weilii strain SC295 were isolated from rodents captured from human leptospirosis outbreak areas in Selangor, Malaysia.
DOI: 10.6084/m9.figshare.23737170
title: Murine intestinal organoid scEU-seq data - Raw

description: AnnData object of scRNA-seq dataset of murine intestinal organoids metabolically labeled and sequenced with scEU-seq. The data has been collected as provided by the original publication in an AnnData file. See here for details. <br> Please cite the original publication: https://doi.org/10.1126/science.aax3072
DOI: 10.24381/cds.8be2c014
title: Temperature statistics for Europe derived from climate projections
DOI: 10.5517/ccdc.csd.cc2dr35g
title: CCDC 2226735: Experimental Crystal Structure Determination

description: Related Article: Ruchi Sharma, Swati Jain, Naveen Sihag, M. Ramu Yadav|2023|Chem.Commun.|59|8945|doi:10.1039/D3CC01740K
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.
DOI: 10.5517/ccdc.csd.cc2dz02h
title: CCDC 2233366: Experimental Crystal Structure Determination

description: Related Article: Ruchi Sharma, Swati Jain, Naveen Sihag, M. Ramu Yadav|2023|Chem.Commun.|59|8945|doi:10.1039/D3CC01740K
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.
DOI: 10.5517/ccdc.csd.cc2f9nxb
title: CCDC 2243622: Experimental Crystal Structure Determination

description: Related Article: Ruchi Sharma, Swati Jain, Naveen Sihag, M. Ramu Yadav|2023|Chem.Commun.|59|8945|doi:10.1039/D3CC01740K
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.
DOI: 10.5517/ccdc.csd.cc2ffrd1
title: CCDC 2247543: Experimental Crystal Structure Determination

description: Related Article: Sudhir Lima, Maximilian H. Pohle, Michael Böhme, Helmar Görls, Thomas Lohmiller, Alexander Schnegg, Rupam Dinda, Winfried Plass|2023|Dalton Trans.|52|9787|doi:10.1039/D3DT01604H
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.
DOI: 10.5517/ccdc.csd.cc2ffrf2
title: CCDC 2247544: Experimental Crystal Structure Determination

description: Related Article: Sudhir Lima, Maximilian H. Pohle, Michael Böhme, Helmar Görls, Thomas Lohmiller, Alexander Schnegg, Rupam Dinda, Winfried Plass|2023|Dalton Trans.|52|9787|doi:10.1039/D3DT01604H
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.
DOI: 10.5517/ccdc.csd.cc2fj459
title: CCDC 2249830: Experimental Crystal Structure Determination

description: Related Article: Ruchi Sharma, Swati Jain, Naveen Sihag, M. Ramu Yadav|2023|Chem.Commun.|59|8945|doi:10.1039/D3CC01740K
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.
DOI: 10.5517/ccdc.csd.cc2fpxz1
title: CCDC 2255404: Experimental Crystal Structure Determination

description: Related Article: Richard H. Jones, Craig L. Bull, Kevin S. Knight, William G. Marshall|2023|CrystEngComm|25|4146|doi:10.1039/D3CE00362K
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.
DOI: 10.5517/ccdc.csd.cc2fl3pv
title: CCDC 2251737: Experimental Crystal Structure Determination

description: Related Article: Wei Bai, Lei Li, Yukang Fu, Junping Tang, Yue Zhao, Yilun Wang, Yang Li|2023|Dalton Trans.|52|9731|doi:10.1039/D3DT01558K
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.
DOI: 10.5517/ccdc.csd.cc2c7w7r
title: CCDC 2182314: Experimental Crystal Structure Determination

description: Related Article: Leyi Chen, Fang Sun, Quanli Shen, Lei Wang, Yonggang Liu, Hao Fan, Qing Tang, Zhenghua Tang|2023|Dalton Trans.|52|9441|doi:10.1039/D3DT01326J
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.
DOI: 10.58073/sruc.23736045
title: Diversity of STEC in Scottish sheep and dairy cattle populations

description: The data currently being gathered relate to faecal samples from Scottish sheep and dairy cattle populations. These samples are tested for the presence of Escherichia coli. Further testing will establish the subtype - e.g. O157 and non-O157 STEC - and will identify which stx genes are present. Ultimately, a subset of samples will undergo Whole-genome Sequencing.
DOI: 10.58073/sruc.23736045.v1
title: Diversity of STEC in Scottish sheep and dairy cattle populations

description: The data currently being gathered relate to faecal samples from Scottish sheep and dairy cattle populations. These samples are tested for the presence of Escherichia coli. Further testing will establish the subtype - e.g. O157 and non-O157 STEC - and will identify which stx genes are present. Ultimately, a subset of samples will undergo Whole-genome Sequencing.
DOI: 10.5519/0016914
title: Artefacts

description: Cultural and historical artefacts from The Natural History Museum
DOI: 10.18120/6t3a-fz70
title: Responsive Neurostimulation for Treatment Resistant Depression
DOI: 10.17632/rnjxjzkndw.1
title: CFR Scheme Evidence Base Diagram (Metadata for health systems design)

description: E-R Style Graphical Depiction of Essential Elements and Relationships for Designing and Optimizing Community First Responder (CFR) Schemes
DOI: 10.17632/k5gy8nht6j.1
title: Data for: A bibliometric analysis on drought and heat indices in agriculture

description: These additional supplementary materials to the manuscript entitled "A bibliometric analysis on drought and heat indices in agriculture", include:

1) the searching queries used to retrieve the literature from Scopus and Web of Science (in the folder "1_bibliographic_searching_queries")

2)  the complete lists of heat and drought indices retrieved through the performed bibliometric analysis; for each index the formulation and the main reference is reported (in the folder “2_index_tables”)

3) scientific maps obtained for the sub-periods of literature identified in the study (in the folder “3_coword_maps”)

4) the output files provided by the VOSviewer software (version 1.6.18), allowing to generate interactive maps (in the folder “4_VOSviewer_files”)
DOI: 10.17632/4hh45vkp38.1
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. To reduce the amount of herbicide and impact caused by uniform spraying, site-specific weed management (SSWM) through variable rate herbicide application and mechanical weed control have long been recommended. To implement such precise strategies, accurate detection and classification of weeds in crop fields is a crucial first step. For efficiency, unmanned aerial vehicles (UAV) are commonly used for image capturing. However, high wind pressure and different drone settings have a severe effect on the capturing quality, what potentially results in degraded images, e.g. due to motion blur. In this paper, we investigate the generalization capabilities of deep learning methods for early weed detection in sorghum fields in such challenging capturing conditions. For this purpose, we developed weed segmentation models using two different state-of-the-art deep learning architectures in combination with residual neural networks as feature extractors.
We publish a manually annotated and expert curated UAV imagery 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. Fully Convolutional Networks with a ResNet-50 feature extractor achieved an F1-score of over 86 % 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: 10.17632/4hh45vkp38.2
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