cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5rr22hgzwr
title: Lung cancer segmentation dataset with Lung-RADS class

description: The lung cancer segmentation dataset comprises CT images paired with corresponding lung cancer masks, meticulously labeled by radiologists according to the Lung-RADS System. This dataset is a fusion of original Kazakhstani local data from the Kazakh Research Institute of Oncology and Radiology, and the openly available LIDC-IDRI dataset [1], which has been re-labeled. The entire dataset has been divided into training and testing sets, with the training set consisting of 708 CT images and the test set containing 264 CT images. All images share a standardized labeling format: 
• label1 – class according to Lung-RADS System 
• mask – binary mask of lung cancer area
• hu_array_old – CT image converted standardized in Hounsfield Units
• hu_array – CT image with dropped non-lung area via thresholding-based algorithm
Important! Fields label1 and mask have been manually labeled by the doctor-radiologist, hu_array has been labeled via automated thresholding-based algorithm and was not additionally checked by the human eye. 
[1] Armato III, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., Kazerooni, E. A., MacMahon, H., Van Beek, E. J. R., Yankelevitz, D., Biancardi, A. M., Bland, P. H., Brown, M. S., Engelmann, R. M., Laderach, G. E., Max, D., Pais, R. C. , Qing, D. P. Y. , Roberts, R. Y., Smith, A. R., Starkey, A., Batra, P., Caligiuri, P., Farooqi, A., Gladish, G. W., Jude, C. M., Munden, R. F., Petkovska, I., Quint, L. E., Schwartz, L. H., Sundaram, B., Dodd, L. E., Fenimore, C., Gur, D., Petrick, N., Freymann, J., Kirby, J., Hughes, B., Casteele, A. V., Gupte, S., Sallam, M., Heath, M. D., Kuhn, M. H., Dharaiya, E., Burns, R., Fryd, D. S., Salganicoff, M., Anand, V., Shreter, U., Vastagh, S., Croft, B. Y., Clarke, L. P. (2015). Data From LIDC-IDRI [Data set]. The Cancer Imaging Archive, doi: https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX
DOI: 10.17632/jhhth9pgc4
title: Data for: Research on the Relationship between Urban Morphology and the Air Temperature Based on Mobile Measurement

description: those are the original data used in this paper.
DOI: 10.6084/m9.figshare.7570454.v1
title: Additional file 4: of Secretome analysis of Strongyloides venezuelensis parasitic stages reveals that soluble and insoluble proteins are involved in its parasitism

description: Table S3. List of proteins identified in the Strongyloides venezuelensis parasitic female excretory/secretory (E/S) insoluble fraction. (XLSX 18 kb)
DOI: 10.6084/m9.figshare.20226297.v1
title: Normalized Remote Sensing Agricultural GPP Monsoon 2016

description: agricultural gross primary productivity
DOI: 10.6084/m9.figshare.20485563.v1
title: Estimation of net ecosystem carbon exchange at climate sites by combing remote sensing data and FLUXNET2015 data with machine learning algorithms

description: The Eddy Covariance (EC) flux stations have great limitations in the evaluation of the global Net ecosystem carbon exchange (NEE) and in the uncertainty reduction due to their sparse and uneven distribution and spatial representation. If the EC stations are linked with widely distributed meteorological stations using machine learning (ML) and remote sensing, it will play a big role in effectively improving the accuracy of the global NEE assessment and reducing the uncertainty. In this study, we first optimized the hyperparameters and input variables of the ML model based on the adaptive genetic algorithm. Then, we developed 566 random forest (RF)-based NEE estimation models by the strategy of spatial leave-out-one cross- validation (SLOOCV). We innovatively established the Euclidean distance-based accuracy projection algorithm of the R square (R2), which could test the accuracy for each model to estimate the NEE of the specific flux at the weather station. Only the model with the highest R2 was selected from the models with a prediction accuracy of R2&gt;0.5 for the specific meteorological stations to estimate its NEE. 4,674 out of 10,289 weather stations around the world might match at least one of the 566 NEE estimation models with a projected accuracy of R2 &gt; 0.5. The NEE estimation models we screened for the meteorological stations showed a reliable performance and a higher accuracy than former studies. The NEE values of the most (96.9%) screened meteorological stations around the world are negative (carbon sink) and most (65.3%) of those showed an increasing trend in the mean annual NEE (carbon sink). The FLUXCOM NEE estimation denoted larger values than our estimations at the corresponding meteorological stations. The NEE dataset produced at the meteorological stations could be used as a supplement to the EC observations and quasi-observation data to assess the NEE products of the global grid. The results of this work will provide theoretical and technical support for the climate change policy formulation and the terrestrial carbon sink assessments.
DOI: 10.6084/m9.figshare.29035835.v1
title: Data supporting "Skin Effect Induced Anomalous Dynamics from Charge-Fluctuating Initial States"

description: Non-equilibrium dynamics in non-Hermitian systems has attracted significant interest, particularly due to the skin effect and its associated anomalous phenomena. Previous studies have primarily focused on initial states with a definite particle number. Here, we present a systematic study of non-reciprocal quench dynamics in the pairing states with indefinite particle number. Our study uncovers a range of novel behaviors. Firstly, we demonstrate a universal tendency towards half-filling of particle density at late times. At early times for certain initial states, we observe a chiral wavefront in both particle number distribution and charge inflow, associated with a sharp decrease in particle number. Furthermore, we find that non-Hermiticity could enhance the growth of entanglement in the initial stages of evolution. In the intermediate time regime, the characteristic skin effect leads to particle accumulation on one side, leading to a pronounced reduction in entanglement entropy. Moreover, our results reveal the presence of the quantum Mpemba effect during the restoration of U(1) symmetry. Our findings open new avenues for exploring exotic dynamic phenomena in quantum many-body systems arising from the interplay of symmetry breaking and non-Hermiticity.
DOI: 10.17632/4kkfffmtk5
title: Group walking

description: Coordinating our actions with others changes how we behave and feel. Here we provide evidence that interacting with others rests on a balance between self-other integration and segregation. Using a group walking paradigm, participants were instructed to synchronise with a metronome while listening to the sounds of 8 virtual partners. By manipulating the similarity and synchronicity of the partners' steps to the participant's own, our novel auditory task disentangles the effects of synchrony and self-other similarity and examines their contribution to both collective and individual awareness. We measured temporal coordination (step timing regularity and synchrony with the metronome), gait patterns, and subjective reports about sense of self and group cohesion. The main findings show that coordination is best when participants hear distinct but synchronous virtual others,  leading to greater subjective feelings of agency, strength, dominance, and happiness.  Data for objective coordination measures, gait and subjective ratings are included here.
DOI: 10.6084/m9.figshare.6137192.v1
title: American Gut Project fecal sOTU counts table

description: The Deblur sOTU counts table for the fecal samples used in the American Gut Project manuscript. The samples were trimmed to a common read length of 125nt, and processed by Deblur (Amir et al mSystems 2017). Blooms were removed (Amir et al mSystems 2017) and any sample with fewer than 1250 sequences was omitted. This table is not rarefied,.
DOI: 10.6084/m9.figshare.12323780.v1
title: CpG results from all days in culture (with and without 5AZA treatment)

description: All called CpG sites from Days 4, 5, 6, 7, and 10 in culture with and without 20 uM 5AZA treatment including KO annotations. <br>
DOI: 10.6084/m9.figshare.26409736.v1
title: The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network"

description: <b>Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network</b><br>The data and codes that support the findings of our study "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network".DataThe following data are provided in the 'Data' folder:`traj.csv`: The trajectory data used for model training and testing. Each point consist of 'trajectory_id', 'timestamp', 'longitude(x)', 'latitude(y)', 'azimuth', and 'inter_id' attributes.`trueLane.csv`: The ground truth lanes for model training and testing. Each lane includes 'geometry' and 'inter_id' attributes.CodesThis repository contains the following Python codes：`data_processing.py`: Contains the implementation of data processing and feature extraction. It includes functions related to trajectory data processing, trajectory feature extraction, and the calculation of graph node and edge features.`run_process.py`: Contains the code for executing data processing and feature extraction.`GTN.py`: Contains the implementation of Graph Transformer Network for lane extraction. It includes classes and functions related to the architecture of the Graph Transformer Network and the set-based lane extraction loss calculation.`train_GTN.py`: Contains the code for Graph Transformer Network model training.`test_GTN.py`: Contains the code for model inference and lane extraction.Running the Code<b>Data processing and feature extraction</b><pre>python run_process.py</pre>This step processes trajectory data, extracts graph node features and edge features, and saves them as CSV files in the `processed_data` folder.<b>Model training</b><code>python train_GTN.py</code>This step trains the GTN model. The trained model is saved in `model/GTN_model.pth`.<b>Model inference and lane extraction</b><code>python test_process.py</code>This step performs model inference and extracts lanes for the test intersections. The lane extraction result is saved in `result/predicted_lane.csv`.RequirementsThe codes use the following dependencies with Python 3.11networkx==3.2.1pytorch==2.0.1torch-geometric==2.5.3geopandas==1.0.1<br>
DOI: 10.6084/m9.figshare.16577327.v1
title: Regression

description: Regression analysis between mutation rate, effective population size (Ne), and genome size
DOI: 10.6084/m9.figshare.28435331.v2
title: Supplementary materials for Association between volatile organic compounds and circadian syndrome among pre- and postmenopausal women: A population-based study.

description: Supplementary materials for Association between volatile organic compounds and circadian syndrome among pre- and postmenopausal women: A population-based study.
DOI: 10.17632/7k5vv22xv4
title: Reaction of bacterial poly-3-hydroxybutyrate with thionyl chloride in the presence of zinc chloride, and preparation of chlorine-containing oligomers

description: Poly-3-hydroxybutyrate (PHB) was subjected to degradation by thionyl chloride in the presence of zinc chloride in chloroform solution. 0.025 mol (2.15 g) of PHB calculated by a PHB monomer fragment (86 g/mol) was dissolved by heating at 50°C in 100 ml of chloroform with stirring on a magnetic stirrer. After complete dissolution, at a temperature of 50°C and strong stirring (750 rpm), 0.0025 mol (340 mg) of finely crystalline zinc chloride was added, followed by 0.1 mol (7.26 ml) of thionyl chloride. Continued heating and stirring under reflux. Samples were periodically taken by precipitating the polymer with hexane at a polymer solution to hexane volume ratio of 2:5, followed by washing and purification on a Schott filter. The experiment was carried out for 96 hours. Molecular weight for all samples was measured three times.
DOI: 10.6084/m9.figshare.11733615.v1
title: Three variants of synthetic benchmarks time series of GPS and ERA-Interim IWV differences

description: <b>Description of the synthetic datasets</b><br>Daily synthetic series of 6000 samples (i.e. length of 16y*365d) for 120 IGS sites for GPS-retrieved (IGS repro1) Integrated Water Vapour (IWV) values, and IWV differences between ERA-Interim model and GPS (IGS repro1) were simulated based on the characterisation (signal and noises) derived from the real datasets. The real ERAI-GPS IWV differences were firstly homogenized with a manual detection of breaks to provide the most consistent series. All manually detected epochs of breaks were cross-validated with information included in the log-files of the stations. If manually detected breaks were not reported as change in a log-file, then they were not corrected for, unless the offset is clearly seen in differences (ERAI-GPS). We assume the ERA-Interim model as an absolute reference with no artificial breaks. Under this assumption, only climate signals should be responsible for jumps in the time series.<br><br>We tested different approaches, as we generated synthetic datasets for the IGS repro1, the ERA-Interim, and their differences. We found that generating directly the synthetic differences is closer to the real differences than building the differences afterwards based on generated synthetic ERA-Interim and the generated synthetic IGS repro1 IWV time series separately, so we proceeded with the synthetic differences. <br><br><b>Specifications of the synthetic datasets available</b><br>Three different variants of those synthetic datasets were constructed:<br>Variant 1: <i>The ‘Easy’ dataset</i>: it includes seasonal signals (annual, semi-annual, 3 & 4 months if present for a particular station) + offsets + white noise.<br>Variant 2: <i>The ‘Moderate’ dataset</i>: seasonal signals (annual, semi-annual, 3 & 4 months) + offsets + autoregressive process of the first order + white noise (AR(1)+WH).<br>Variant 3: The <i>‘Complex’ dataset</i>: trend + seasonal signals (annual, semi-annual, 3 & 4 months) + offsets + AR(1)+WH + gaps.<br><br>Variant 1 was created only for the ERA-Interim - GPS differences while Variants 2 and 3 were created both for 1) differences of IWV between ERAI and GPS (ERAI-GPS), and 2) GPS itself. The values of trends, amplitudes of seasonal signals, noise process and percentage of gaps were directly modelled taking into account the derived characteristics from the real datasets. The epochs of offsets were simulated randomly, separately for each variant, but the number and amplitudes of the offsets are characteristic for the real datasets. <br><b>File format of the synthetic datasets </b><br>Each of the simulated series are stored in a separated file. As for the real dataset, each file includes three columns: “year, y-x, x”. “Year” is a date formatted as YYMMDD.HHMMSS (e.g. 950101.120000 for 1st January 1995 at 12:00 UTC), column “y-x” includes differences between the ERAI and GPS synthetic values (in that order), and “x” means the ‘GPS-retrieved’ IWV synthetic values.
DOI: 10.6084/m9.figshare.18586136.v4
title: Chinese hydropower database, HP model and outputs

description: You are welcome to use and find errors in the database. I hope you can inform me of any errors you find by sending me an email titled "Hydropower database errors" to meviolet@126.com.<br>1. Chinese Hydropower Database: Sheet "CHD" is the hydropower database in mainland China, which comprises 3591 (3701 including Taiwan Province, in the updated version 2022.12) hydropower plant records spread across 29 (30 in the version 2022.12) provincial-level divisions; Sheet "Attribute description" describes attributes provided in the CHD database.<br>2. HP model: R script for simulating mothly hydroelectricity production based on monthly gridded streamflow series and CHD<br>3. CSV files: The outputs of HP model with streamflow from different model combinations, monthly HP from 1971-2099 for each conventional plant record.<br>4. Citation:(1) Wan, Wenhua, Petra Döll, and Hang Zheng. "Risk of climate change for hydroelectricity production in China is small but significant reductions cannot be precluded for more than a third of the installed capacity." <i>Water Resources Research</i> 58.8 (2022): e2022WR032380.or (2) 万文华, 梁雪容, 郑航, 等. 中国水电站数据库构建、验证与定量分析[J]. 水利水电技术(中英文), 2022, 53(12)
DOI: 10.6084/m9.figshare.3486458.v1
title: Sample data for quantification of gene expression by qPCR.

description: We used qPCR to quantitate the differences in the expression of several genes after cultivation of human SH-SY5Y neuroblastoma cell line with or without 10 μM retinoic acid (RA). The data has been formatted as for a submission to NCBI GEO, with descriptions of the experiment and data in sheets in a spreadsheet.<br>
DOI: 10.6084/m9.figshare.11734203.v2
title: The mRNA expression data of DTC patients

description: The mRNA expression data of cancerous and paracancerous tissue were collected up to October 29, 2019 from the TCGA-THCA data set<br>
DOI: 10.6084/m9.figshare.14604474.v1
title: RuizTagle_etalData.xlsx

description: Data used for analysis.
DOI: 10.6084/m9.figshare.1572974.v1
title: Composite Indicator Methodologies Included In Study

description: Annex for Beccari B. A Comparative Analysis of Disaster Risk, Vulnerability and Resilience Composite Indicators. PLOS Currents Disasters. 2016 Mar 14 . Edition 1. doi: 10.1371/currents.dis.453df025e34b682e9737f95070f9b970.
DOI: 10.6084/m9.figshare.21629471.v1
title: SNPs for Zostera marina population genomics

description: 11705 core SNPs
<br>
DOI: 10.17632/2jngk5tzm4
title: The VENUS/NWChem software package. Tight coupling between chemical dynamics simulations and electronic structure theory

description: Abstract The interface for VENUS and NWChem, and the resulting software package for direct dynamics simulations are described. The coupling of the two codes is considered to be a tight coupling since the two codes are compiled and linked together and act as one executable with data being passed between the two codes through routine calls. The advantages of this type of coupling are discussed. The interface has been designed to have as little interference as possible with the core codes of both VENUS a... Title of program: VENUS/NWChem Catalogue Id: AERS_v1_0 Nature of problem Direct dynamics simulations play an important role in investigating and understanding atomic-level chemical dynamics information such as atomistic reaction mechanisms, unimolecular and bimolecular rate constants, intramolecular vibrational energy redistribution rates, etc. The ability to couple direct dynamics with electronic structure methods brings a level of fidelity to the simulations that is important for complex systems. However, a tight coupling between two codes that have their own devel ... Versions of this program held in the CPC repository in Mendeley Data AERS_v1_0; VENUS/NWChem; 10.1016/j.cpc.2013.11.011 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)
DOI: 10.17632/cjhd9hvdd8
title: War Stories in Social Media:  Personal Experience of Russia-Ukraine War

description: LIWC raw data - CDI and categories
DOI: 10.17632/mp5ncd4z93
title: Pharmacogenomic profiling of intra-tumor heterogeneity by a large organoid biobank of liver cancer

description: Mendeley data 1: Immunofluorescence staining of P1 (HCC, shown in Fig. 1D) and P29 (ICC, shown in Data S1.3) organoids. HCC markers (AFP and Hepatocyte) and biliary markers (EPCAM and KRT19) were assayed by immunofluorescence staining, using two organoid cultures of the same passage, due to the limit of three channels (blue, green and red), with overexposure condition applied.
Mendeley data 2: H&E and IHC staining of three tumor regions from a HCC patient P1, and H&E, IHC and immunofluorescence staining of the corresponding organoids. HCC markers (AFP and HepPar1) and biliary markers (EPCAM and KRT19) were assayed by IHC and immunofluorescence staining. Scale bars indicate 50 μm.
Mendeley data 3: H&E and IHC staining of tumor lesions from HCC (A), ICC (B) and CHC (C) patients, and H&E, IHC and immunofluorescence staining of the derived organoids. HCC markers (AFP, HepPar1) and ICC markers (KRT19 and EPCAM) were assayed by IHC and immunofluorescence staining. Scale bars indicate 50 μm.
DOI: 10.6084/m9.figshare.20304456.v1
title: Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response.

description: We performed the first integrative single-cell and spatial transcriptomic analysis on HPV-negative oral squamous cell carcinoma (OSCC) to comprehensively characterize tumor core (TC) and leading edge (LE) transcriptional architectures. We show that the TC and LE are characterized by unique transcriptional profiles, cellular compositions, and ligand-receptor interactions. We demonstrate that LE regions are conserved across multiple cancers while TC states are more tissue specific, highlighting the existence of common mechanisms underlying tumor progression and invasion. Additionally, we found our LE gene signature is associated with worse clinical outcomes while the TC gene signature is associated with improved prognosis across multiple cancer types. Finally, using an <em>in silico</em> modeling approach, we describe spatially-regulated patterns of cell development in OSCC that are predictably associated with drug response. Our work provides pan-cancer insights into TC and LE biology and a platform for data exploration (http://www.pboselab.ca/spatial_OSCC/) that can be foundational for developing novel targeted therapies. <br> Data uploaded: 1) .loom files as outputs of the scvelo pipeline for each object independently. 2) .Robj files for each of the 12 samples formatted as a Seurat object 3. ScPred model as a .Robj file
DOI: 10.6084/m9.figshare.17881073.v1
title: Data and code to “Field experiments underestimate aboveground biomass response to drought”

description: File collection to the paper “Field experiments underestimate aboveground biomass response to drought” by Kröel-Dulay et al. Nature Ecology and Evolution (2022)This file collection contains the data files and computer codes (R scripts) that are needed to reproduce the analyses presented in the paper. Specifically, the collection contains the following files:Table_of_all_collected_data.xlsx file contains all data that we collected and used for our meta-analyses (Data sheet), and the explanation of the data (Data description sheet). Data_focal_N-weighted.csv file contains the data that were included in the focal meta-analysis weighted by replication (N) for grasslands with biomass data and site aridity index lower than 1 (focal data set). Data_focal_unweighted.csv file contains the data that were included in the unweighted meta-analysis for grasslands with biomass data and site aridity index lower than 1. Data_focal_variance-weighted.csv file contains the data that were included in the meta-analysis weighted by the inverse of the pooled variance for a subset of the focal data set where variance estimates were available. Data_additional_N-weighted.csv file contains the data that were left out from the focal data set (shrublands, grasslands with cover estimates and/or site aridity index exceeding 1), but were included in a separate N-weighted meta-analysis. R_scripts.docx file contains the R code for our each analysis.<br>
DOI: 10.6084/m9.figshare.26909314.v1
title: 7 - gtdbtk_2.4_afterQC

description: GTDB-Tk 2.4 - Taxonomic classification of the reconstructed MAGs from gorilla, human, cattle, and goat gut microbiomes in Uganda
DOI: 10.6084/m9.figshare.8142323.v2
title: Code for simulation from Inferring collective behaviour from a fossilized fish shoal.

description: The simulation program is implemented in Microsoft Visual Studio C++ 2017.