cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27922470.v1
title: <b>ML_AB </b>

description: HCP 4x4x4 MgH0.06 on-the-fly VASP-MLFF configurations dataset 0-700K<i>Hydrogen Diffusion in Magnesium Using Ma</i><i>chine Learning Potentials</i>
DOI: 10.17632/bgr8mh5jyt
title: Instances for: Apron Layout Planning - Optimal Positioning of Aircraft Stands

description: This data set contains the sets and parameter values needed to reproduce the computational experiments discussed in Section 6 of our paper "Apron Layout Planning - Optimal Positioning of Aircraft Stands".
DOI: 10.17632/rx7mr3r5yg
title: Image Dataset for Disease Detection in Sponge Gourd (Luffa aegyptiaca) Leaves

description: Dataset Purpose: Annotated images of sponge gourd (Luffa aegyptiaca) leaves for disease detection and classification.

Location: Collected from multiple regions in Bangladesh.

Leaf Categories: Five distinct disease categories, each with specific characteristics:
Fresh Leaf: Healthy, non-diseased leaves.
Mosaic Leaf: Leaves with mosaic patterns caused by viral infections.
Insect Disease Leaf: Leaves damaged and discolored by insect infestations.
Downy Mildew: Fungal infection marked by pale yellow spots and powdery growth.
Bacterial Leaf Spot Disease: Necrotic spots on leaves due to bacterial infections.

Applications: Supports the development and evaluation of machine learning models for disease detection in sponge gourd cultivation.

Audience: Useful for researchers and practitioners in agriculture, plant pathology, and computer vision.

Dataset Features: Images are annotated and labeled, providing a comprehensive resource for automated disease classification in plants.
DOI: 10.6084/m9.figshare.13578371.v1
title: SIFTER F-actin LatA vs. Control Benchmarking Against Flow Cytometry

description: Images of SIFTER gel separations of F-actin complexes from U2OS cells untreated (DMSOcontrl) or treated with Latrunculin A (LatA).
DOI: 10.6084/m9.figshare.25637856.v1
title: Brain2GAN (StyleGAN-XL)

description: See https://github.com/neuralcodinglab/brain2gan.
DOI: 10.6084/m9.figshare.8926079.v1
title: grid.461819.3

description: Network Graphs for institution grid.461819.3 created for "What Does a University Look Like?" poster
DOI: 10.17632/x5fyb55w9x
title: De novo generation of the NPM-ALK fusion recapitulates the pleiotropic phenotypes of ALK+ ALCL pathogenesis and reveals the ROR2 receptor as target for tumor cells

description: ABSTRACT

Anaplastic large cell lymphoma positive for ALK (ALK+ ALCL) is a rare type of non-Hodgkin lymphoma. This lymphoma is caused by chromosomal translocations involving the anaplastic lymphoma kinase gene (ALK). In this study, we aimed to identify mechanisms of transformation and therapeutic targets by generating a model of ALK+ ALCL lymphomagenesis ab initio with the specific NPM-ALK fusion.
Methods:
We performed CRISPR/Cas9-mediated genome editing of the NPM-ALK chromosomal translocation in primary human activated T lymphocytes.
Results:
Both CD4+ and CD8+ NPM-ALK-edited T lymphocytes showed rapid and reproducible competitive advantage in culture and led to in vivo disease development with nodal and extra-nodal features. Murine tumors displayed the phenotypic diversity observed in ALK+ ALCL patients, including CD4+ and CD8+ lymphomas. Assessment of transcriptome data from models and patients revealed global activation of
the WNT signaling pathway, including both canonical and non-canonical pathways, during ALK+ ALCL lymphomagenesis. Specifically, we found that the WNT signaling cell surface receptor ROR2 represented a robust and genuine marker of all ALK+ ALCL patient tumor samples.
Conclusions:
In this study, ab initio modeling of the ALK+ ALCL chromosomal translocation in mature T lymphocytes enabled the identification of new therapeutic targets. As ROR2 targeting approaches for other cancers are under development (including lung and ovarian tumors), our findings suggest that ALK+ ALCL cases with resistance to current therapies may also benefit from ROR2 targeting strategies.
DOI: 10.6084/m9.figshare.7117679.v1
title: One-hour social and reproductive behavioral time series of Japanese quail in diverse social environments.

description: We favor the conformation of divergent social environments by using the preselecting animals based on a combination of the 4 behavioral tests described in the project description. Social groups composed of three adult quail, 2 females and 1 male (156-171 days old). We evaluated social and reproductive behavior in these groups.Groups were housed in a white wooden apparatus measuring 80 x 40 x 40 cm (width x length x height, respectively) with wood-shavings on the floor. A feeder and an automatic nipple drinker were positioned in opposite corners of the apparatus. Nylon monofilament line was extended over the top of the boxes with a 1 cm separation in order to prevent the birds from escaping without interfering with their visualization. A video camera was suspended 1.5m above the box. Since only 4 social groups could be tested simultaneously, the setup was repeated 3 consecutive times. For convenience boxes in which each social group were placed were numbered 1-12, where boxes 1-4 were tested simultaneously first, 5-8 second and 9-12 last.We used the ANY-MAZE@ to register the following behaviors as described in Caliva et al. (2017) during a one-hour period between 9 and 10 am inmediatly after placing in box (Day1) after 48h of habituation to the novel setting (Day3):<b>Pecks: </b>when one bird raises its head and vigorously pecks the other bird’s body (usually on the head).<b>Grabs: </b>when a male catches (“grabs”) with their beak the neck or head region of the female.<b>Mounts: </b>while performing a grab, the male approaches a female from behind, and places both feet on the dorsal surface of its torso, stepping over the females’ tail.<b>Cloacal contacts: </b>during mounting, the male lifts his tail and tilts his pelvis underneath the other bird and briefly presses its cloaca against the female.<b>Threats: </b>one bird stands with its neck and head raised in front of the other bird that usually has its head at a lower level than the first.<b>Chase: </b>a bird runs after another that is escaping.<b>Foraging:</b> pecking at the ground or actively moving litter with beak. <b>Feeding:</b> peaking at food in the feeding trough. <b>Dust bathing:</b> vertical wing shakes in a lying position .Time series denote with the number one changes towards actively performing the given behavior, while the number zero indicates cessation of that behavior. <br>All time series are stored as text files (.txt). Considering that the 36 animals were evaluated in 12 mixed-sex groups of 3 birds in individual experimental boxes, each subject quail was identified by their experimental group number (Box), ID number of wing band, and sex (femaleA, femaleB or male). In the case of females an indication A or B is used to discriminate between the two. In the file name an indication of the corresponding bird is provided as "BoxNº_IDNº_sex_DayNº" <br>Data descriptor submitted to Sci Data "High resolution behavioral time series of Japanese quail within their their social environment"<b></b>
DOI: 10.6084/m9.figshare.23281202.v1
title: 2021 ROMS Data

description: We simulate the SCS dipole in 2021 with a model simulation domain shown in 98-130°E, 4-25°N, with land to its west and ocean on the remaining three sides. To resolve the mesoscale eddy, the spatial resolution is set to 1/24°×1/24°, and the vertical direction is divided into 32 layers. HYCOM data are used as initial and boundary fields, and CFSR data are used as atmospheric forcing fields. The western boundary of the simulation area is closed, and the east, north and south boundaries are opened.
DOI: 10.6084/m9.figshare.7330931.v1
title: Projecting the future levelized cost of electricity storage technologies : dataset

description: Corresponding paper: O. Schmidt, S. Melchior, A. Hawkes, I. Staffell. Projecting the future levelized cost of electricity storage technologies. Joule (2018).<br>Link to the paper: https://cell.com/joule/fulltext/S2542-4351(18)30583-X<br>This dataset compiles levelized cost of storage data in energy terms (LCOS, US$/MWh) and power terms, i.e. annuitized capacity cost (ACC, US$/kW-yr), for 9 electricity storage technologies from 2015 projected to 2050. <br>One spreadsheet provides the data for 12 applications as well as the probability for each of the 9 technologies to exhibit lowest LCOS or ACC in a distinct application. Figures 1 and 2 and Supplementary Figures 3 and 4 of the respective publication are based on this data.<br>The remaining files contain LCOS and ACC results for various annual full equivalent cycle and discharge duration combinations, regardless of actual application requirements. Electricity price is fixed at 50 US$/MWh. Figures 3 and 4 and Supplementary Figures 5 and 6 of the respective publication are based on this data.<br>Please see the paper for a full analysis and discussion of the results.
DOI: 10.17632/ztb7vdr24f
title: Multicellular stratification in HBV-associated FL

description: -The evolution trajectory of malignant B cells revealed pathogenesis from HBV infection.
-Specific malignant cell subsets and FDC subsets were dominate stratified by dynamic HBV-infected states.
-Maintenance of predominant malignant cell subsets after immunochemotherapy may promote POD24 occurrence.
-Malignant cell subset colocalized with certain immunosuppressive cohorts could characterize POD24 events.
-Newly identified featured molecules from specific malignant cells subsets provides potential therapeutic targets.
DOI: 10.6084/m9.figshare.26340250.v1
title: <b>Self -rescue—the Rhizosphere Microbiomes Cry-for-Help Strategy Alleviates Drought Stress in Spring Wheat</b>

description: 数字的原始数据。
DOI: 10.17632/37rv646rzm
title: DDX5 super-enhancer promote vasculogenic mimicry formation and metastasis in nasopharyngeal carcinoma by enhancing ADAM10 transcription
DOI: 10.17632/nsvw3p3vpw
title: ZBP1 Senses Splicing Aberration by Z-RNA for Cell Death

description: Raw data (original Western blot images and microscopy images)
DOI: 10.6084/m9.figshare.19387442.v2
title: Supplemental Data - Optical density (600) of E. coli, S. saprophyticus, and M. plutonius at mid-log phase when treated with 70% ethanol extracts of honey comb, brood comb, or propolis.

description: OD<sub>600</sub> readings of wells containing bacteria and treatment (od) or bacteria and solvent (od_solvent) at the mid-growth phase (tmid; as determined by a standard growth control) over a 12-hour period. The difference between these two OD600 readings was recorded (od_diff) to account for the bacterial inhibition by the 70% ethanol solvent. Controls for each replicate included the aforementioned solvent control (solv.ctrl), untreated standard growth control (avg.std), and a positive control of antibiotic oxytetracycline hydrochloride (avg.otc). Each treatment extract was subject to a series of eight two-fold dilutions. Diluted treatments are indicated by numbers, where honey.1 would be the crude extract and honey.8 would be the most dilute.
DOI: 10.17632/hynjgj24jv
title: A multiconfiguration relativistic DIRAC-FOCK program

description: Title of program: MULTICONFIGURATION DIRAC-FOCK Catalogue Id: ACRV_v1_0 Nature of problem The relativistic Dirac-Fock equations, for neutral atoms or ions in a bound state, are solved numerically within the multi-configuration approximation. CORRECTION SUMMARY: Vol:Year:Page 13:1977:71 "000B CORRECTION 7/12/76" "A multiconfiguration relativistic Dirac-Fock program.(C.P.C. 9(1975)31)." J.P. Desclaux CORRECTION SUMMARY: Vol:Year:Page 0:unpublished:unpublished "000A CORRECTION 4/01/75" "Unpublished correction to a multiconfiguration relativistic DIRAC- FOCK program." J.P. Desclaux Note: correction instructions are contained in source code Versions of this program held in the CPC repository in Mendeley Data ACRV_v1_0; MULTICONFIGURATION DIRAC-FOCK; 10.1016/0010-4655(75)90054-5 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.17632/ks96gsk9c7
title: stata codes for From Struggle to Startup: How Childhood Socioeconomic Status Shapes Entrepreneurial Happiness

description: relevant stata codes to replicate the results presented in "From Struggle to Startup: How Childhood Socioeconomic Status Shapes Entrepreneurial Happiness”
DOI: 10.17632/kyy4sdh2ry
title: AI-Enabled Precision Medicine

description: Artificial Intelligence (AI) has emerged as a transformative force in healthcare, particularly in the realm of precision medicine. This paper explores the multifaceted role of AI in enhancing diagnostic processes, treatment personalization, and the prediction of patient outcomes, ultimately leading to more targeted and effective healthcare interventions.
The integration of AI into diagnostic processes has shown remarkable potential in improving the accuracy and efficiency of disease detection. AI algorithms, particularly those based on machine learning and deep learning, have demonstrated the ability to analyze complex medical data, such as imaging and genetic information, with a level of detail and speed that surpasses human capabilities. This has led to earlier detection of diseases, more precise diagnoses, and the identification of novel biomarkers, paving the way for timely interventions and improved patient outcomes.
In the domain of treatment personalization, AI is enabling the tailoring of medical interventions to the unique characteristics of individual patients. By analyzing vast amounts of patient data, including genetic profiles, medical histories, and lifestyle factors, AI models can predict patient responses to various treatments, allowing for the selection of the most effective therapies. This personalized approach to treatment has the potential to optimize patient outcomes, reduce adverse effects, and improve overall treatment efficacy.
Moreover, AI's predictive capabilities are revolutionizing the way healthcare providers anticipate and manage patient outcomes. By leveraging AI algorithms to analyze historical and real-time patient data, healthcare professionals can predict the likelihood of disease progression, treatment response, and potential complications. This predictive insight allows for proactive patient management, enabling timely interventions and personalized care plans that can improve patient outcomes and quality of life.
However, the integration of AI into precision medicine is not without its challenges. Ethical considerations, such as data privacy, informed consent, algorithmic bias, and accountability, must be carefully navigated to ensure the responsible and equitable deployment of AI in healthcare. Additionally, the development of robust regulatory frameworks and the need for interdisciplinary collaboration among healthcare professionals, AI researchers, and policymakers are crucial in addressing these challenges and realizing the full potential of AI-enabled precision medicine.
Looking to the future, the continued advancement of AI technologies, the integration with other emerging fields such as genomics and digital health, and the potential for global impact in healthcare accessibility and equity are promising avenues for further exploration. As AI-enabled precision medicine evolves, it holds the potential to transform healthcare delivery, making it more personalized, predictive, and participatory.
DOI: 10.6084/m9.figshare.28021238.v1
title: Supporting data: Evaluating the physiological benefits of behavioral flexibility in chacma baboons (<i>Papio ursinus</i>) using a biophysical model

description: Observed core body temperature and predicted core body temperature using Niche Mapper<sup>TM</sup> for a subadult male chacma baboon
DOI: 10.6084/m9.figshare.6007223.v1
title: File 12: Genome sequences and annotations

description: A bzip2 compressed tar archive containing IMG annotated genome assemblies, gene and protein sequences, and associated annotation files derived from the IMG Annotation Pipeline version 4. These data are maintained at the US DOE Joint Genome Institute's Integrated Microbial Genomes (IMG) Repository: https://img.jgi.doe.gov
DOI: 10.17632/dh47xpkzh8
title: Multi-Modal Emotion Labelling of Hinglish Utterances using Iterative Active Learning

description: The described method presents a system designed for labeling emotions in Hinglish utterances. It incorporates diverse types of information, encompassing text and emojis. The active learning approach is employed to iteratively refine emotion labeling by selecting challenging examples for human annotation. This method is particularly beneficial for understanding emotions in the linguistic and cultural context of Hinglish, which amalgamates Hindi and English elements. It holds practical applications in sentiment analysis and interpretation of customer feedback.
DOI: 10.6084/m9.figshare.13349282.v3
title: Drosophila central brain connectivity

description: See https://github.com/mhturner/SC-FC for analysis code and figure generation using this dataset<br><br>-StructuralMatrix_branson.csv and CorrelationMatrix_Branson.csv are the fine-grained Branson segmentation based structural and functional connectivity matrices shown in Fig. S3.<br>-body_ids.csv is the list of unique body ID numbers in the Hemibrain connectome that were used to compute structural connectivity.<br><br>data_TurnerMannClandinin:<br>Compressed directory containing these subdirectories (in <b>bold</b>):<b>-atlas_data</b> contains original Ito and Branson brain atlas/segmentation files<br><b></b><b>-ito_68_atlas</b> & <b>branson_999_atlas</b> each contain .nii.gz image files containing the registered brain atlas mask for each fly. The mask numbers correspond to the regions as in Original_Index_panda_full.csv and atlas_roi_values<br><br><b>-ito_responses</b> & <b>branson_responses</b> each contain pandas dataframes, one for each fly, describing raw fluorescence responses for each brain region in that atlas. The sampling rate is 1.2 Hz.<br>-<b>connectome_connectivity</b> contains computed pandas dataframe files for various structural connectivity metrics, for the regions included in the main paper analysis<br>-<b>hemi_2_atlas</b> contains results of structural connectivity computations from R code included in the corresponding GitHub repository.<br><br>-<b>subsample</b> contains numpy data the result of the region subsampling analysis<br>-<b>template_brains</b> contains template brain files in the original (JFRC2) brain template space as well as the same atlases transformed to JRC2018 space (in both .tif and .nii.gz format)<br>registration.xform.xipTransform file (CMTK format) to convert from JFRC2 template brain space, where the Ito atlas and Branson segmentation are, to JRC2018 template brain space<br><br>
DOI: 10.6084/m9.figshare.26575129.v1
title: Tree Figures and Files produced from <b>Historical biogeography and the evolution of habitat preference in the North American camel spider family, Eremobatidae (Arachnida:Solifugae)</b>

description: Tree files and figures from: Historical biogeography and the evolution of habitat preference in the North American camel spider family, Eremobatidae (Arachnida:Solifugae)
DOI: 10.6084/m9.figshare.28500593.v4
title: Fishing Out Nutrients - Nutrient Extraction Data

description: <b>Overview</b>This repository contains data for the manuscript <i>'Fisheries disrupt marine nutrient cycles through biomass extraction'</i> by González Ortiz et al. (2025). The data here includes the list of sources for the nutrient composition data (List_of_Sources.csv), taxonomic information and nutrient compositions of the assessed taxa (IndustrialTaxa_NutrientContent.csv), and the extraction distribution matrices for each nutrient (NutrientExtraction_DistributionMatrices.csv). The Fisheries_NutrientExtraction_index CSV corresponds to the extraction distribution matrices and offers the identifying information for each row of the distribution matrices, including taxa, year, and marine region where it was fished. An explanation of the extraction estimates is below. <b>Extraction Estimates</b>Total catches (tonnes year⁻¹) for each species and taxonomic group caught in an EEZ or high seas region between 1960 and 2018 resulted in a dataset of over 6.7 million entries.For each entry, 100 random nutrient composition values were sampled from a normal distribution based on species/taxonomic group means and standard deviations. These values were multiplied by the total landed amount to generate 100 nutrient extraction estimates per entry.Mean extraction values for carbon (C), nitrogen (N), and phosphorus (P) were calculated, along with standard deviations and 95% confidence intervals.The full distribution matrices of extraction estimates are found as a zipped 7z folder in this repository. This zipped folder contains three CSVs, one for each nutrient (C, N, and P).The Fisheries_NutrientExtraction_index CSV file contains the identifying information for each row of the fisheries dataset which the distribution matrices are based on. Each row in the Fisheries_NutrientExtraction_index CSV corresponds to that same row in each of the nutrient extraciton distributions CSVs.The analysis was conducted in <b>R</b> and the associated code can be found at the following GitHub repository: https://github.com/FishyAdrian/Fishing_Out_Nutrients.CitationIf you use this data, please cite this Figshare repository and the original manuscript once published.ContactPlease contact <b>Adrian Gonzalez Ortiz</b> (adgon@umich.edu) for questions regarding the data.
DOI: 10.17632/jy3xk9bsxz
title: Data for: Global versus local processes during the Pliensbachian–Toarcian transition at the Peniche GSSP, Portugal: A multi-proxy record

description: Raw data, figure showing the readjustment of the carbon isotope data, and clumped isotope values (brachiopod shells) discussed in the text
DOI: 10.6084/m9.figshare.17048207.v2
title: Knowledge Sharing for Shared Success in The Decade on Ecosystem Restoration

description: <b>Figure 1: </b>Following (Young et al., 2005) and (Brudvig, 2011), we present research publication trends for restoration ecology. Data are from a Web of Science search conducted in December 2020. The dark green dashed line represents a search string of (topic = “ restor* AND ecol* ”), and the light green solid line represents (topic = “ restor* AND ecol* ” ) AND topic= (experiment* OR monitor*). A) total number of articles every year, and B) proportion of the total number of articles every year for each search string category. n = the total number of articles across all years.
DOI: 10.6084/m9.figshare.29085002.v1
title: City-level process-related CO₂ emissions in China 2000–2021

description: This is the data file for review of the manuscript "City-level process-related CO₂ emissions in China 2000–2021" submitted to Scientific Data.
DOI: 10.17632/cszx852hgs
title: Characterization of twelve biomasses for energy purposes

description: Proximate and Ultimate Analysis, Ash Melting Behavior and Calorimetry applied to twelve selected Cuban biomasses in order to evaluate their potential use for gasification and/or pyrolysis