cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/h8tcn6ykky
title: A Litopenaeus vannamei shrimp dataset with images and corresponding weight-size measurements for the development of artificial intelligence-based biomass estimation and organism detection algorithms

description: This dataset was compiled with the ultimate goal of developing non-invasive computer vision algorithms for assessing shrimp biometrics and biomass estimation. The main folder, labeled "DATASET," contains five sub-folders—DB1, DB2, DB3, DB4, and DB5—each filled with images of shrimps. Additionally, each sub-folder is accompanied by an Excel file that includes manually measured data for the shrimps pictured. The files are named respectively: DB1_INDUSTRIAL_FARM_1, DB2_INDUSTRIAL_FARM_2_C1, DB3_INDUSTRIAL_FARM_2_C2, DB4_ACADEMIC_POND_S1, and DB5_ACADEMIC_POND_S2.

Here’s a detailed description of the contents of each sub-folder and its corresponding Excel file:

1) DB1 includes 490 PNG images of 22 shrimps taken from one pond at an industrial farm. The associated Excel file, DB1_INDUSTRIAL_FARM_1, contains columns for:
SAMPLE: Reflecting the number of individual shrimps (22 entries or rows).
LENGTH (cm): Measuring from the rostrum (near the eyes) to the start of the tail.
WEIGHT (g): Recorded using a scale.
COMPLETE SHRIMP IMAGES: Indicates if at least one full-body image is available (1) or not (0).

2) DB2 consists of 2002 PNG images of 58 shrimps. The Excel file, DB2_INDUSTRIAL_FARM_2_C1, includes:
SAMPLE: Number of shrimps (58 entries or rows).
CEPHALOTHORAX (cm): Total length of the cephalothorax.
LENGTH (cm) and WEIGHT (g): Similar measurements as DB1.
COMPLETE SHRIMP IMAGES: Presence (1) or absence (0) of full-body images.

3) DB3 contains 1719 PNG images of 50 shrimps, with its Excel file, DB3_INDUSTRIAL_FARM_2_C2, documenting:
SAMPLE: Number of shrimps (50 entries or rows).
Measurements and categories identical to DB2.

4) DB4 encompasses 635 PNG images of 20 shrimps, detailed in the Excel file DB4_ACADEMIC_POND_S1. This includes:
SAMPLE: Number of shrimps (20 entries or rows).
CEPHALOTHORAX (cm), LENGTH (cm), WEIGHT (g), and COMPLETE SHRIMP IMAGES: Documented as in other datasets.

5) DB5 includes 661 PNG images of 20 shrimps, with DB5_ACADEMIC_POND_S2 as the corresponding Excel file. The file mirrors the structure and measurements of DB4.

The images for each foler are named "sm_n", where m is the number of shrimp sample and n is the number of picture of that shrimp.
This carefully structured dataset provides comprehensive biometric data on shrimps, facilitating the development of algorithms aimed at non-invasive measurement techniques. This will likely be pivotal in enhancing the precision of biomass estimation in aquaculture farming, utilizing advanced statistical morphology analysis and machine learning techniques.

CHANGES FROM VERSION 1:

The cephalothorax metric is the length rather than the width. That was an error in the first version. The name in the columns also had a typo, which has been corrected (from CEPHALOTORAX to CEPHALOTHORAX).
DOI: 10.6084/m9.figshare.1138743.v1
title: TXT files - raw LiDAR data from the 2012 LiDAR campaign to Santorini, Greece

description: Text files of the raw LiDAR data from the 2012 NERC ARSF survey of the Kameni islands, Santorini, Greece EU-12-12-137 Data are from 13 flightlines
DOI: 10.6084/m9.figshare.22102322.v1
title: Data for the paper "Maximal accelerations for twelve weeks elicit improvement in a single out of a collection of cycling performance indicators in trained cyclists"

description: Data for the paper: Hyttel MK, Kristiansen M and Hansen EA (2023) Maximal accelerations for twelve weeks elicit improvement in a single out of a collection of cycling performance indicators in trained cyclists. Front. Sports Act. Living 4:1027787. doi: 10.3389/fspor.2022.1027787 <br> <br>
DOI: 10.6084/m9.figshare.28784609.v1
title: Dummy Dataset for Testing

description: This is a dummy dataset created for testing purposes in the context of traffic behavior analysis.
DOI: 10.6084/m9.figshare.5047660.v1
title: Toy Choice Meta-analysis Data Table 1a

description: This is the data related to Table 1a of the Toy Choice in Children Meta-analysis by Barry, Fischer & Todd
DOI: 10.6084/m9.figshare.6083393.v1
title: How the hummingbird wingbeat is tuned for efficient hovering

description: Matlab file containing wingbeat resolved kinematic and power traces from the following article: "How the hummingbird wingbeat is tuned for efficient hovering" published in The Journal of Experimental Biology. <br>For data plotted in the manuscript figures, open "ingersollSuppData.mat".<br>For data from the directly measured vertical force using the Aerodynamic Force Platform, open "statsAFP.mat".<br>For data from the 3D tracked wing kinematics, open "statsModeling.mat". The "valueTitles" cell array contains the names of each of the 35 calculated scalar values per individual. The specific values are stored in the "valuesIndividualMean" array and should be multiplied by the respective "valuesMutiplyFactor" for correct units. Wingbeat resolved traces are also provided and should be plotted against the "percentage" array.<br>For a 3D wing tracking example, run the script track3DWing.m and select all 5 videos. Select the dltCoefs file and the DLTdv5 tracked wing file for an outline of a single wing. It is not needed to "select a previous tracking".
DOI: 10.6084/m9.figshare.14766624.v7
title: YARP reaction database

description: This is a dataset generated by Yet Another Reaction Program (YARP), including pyGSM reaction pathways, Gaussian transitions states optimization file, IRC calculation results, etc. <br> Four systems are provided, 'KHP network' involves reactions of gamma-ketohydroperoxide and it's 12 intended products. 'Z-benchmark' involves reactants obtained from Zimmerman testing set.
DOI: 10.6084/m9.figshare.25952017.v4
title: Dataset for wetland plant elementome

description: This file contains the dataset and R code used in this study. In this dataset file, we contain eight sheets. Sheet 1, named "Metadata" contains a detailed description of the abbreviations that appear in the dataset. Sheet 2, named "plant elementome_17 element", we have a 1059*77 matrix that contains all the data used in the analysis of this study; Sheet 3, named "LMM_data ", represents the data used in Linear mixed effect model (LMM); Sheet 4, named "LMM_res ", represents the statistical result of Linear mixed effect model (LMM); Sheet 5, named "stoichiometric ratios", represents the data used in Phylogenetically controlled mixed models; Sheet 6, named "node-stability", represents the data used in leaf mutli-element network analysis; Sheet 7, named "sp_element", represents the data used for phylogenetic signal detection; Sheet 8, named "sp_tree", represents the data used to constructe the phylogenetic tree. Missing data is represented by "null".
DOI: 10.6084/m9.figshare.11611911.v1
title: sheep data 1986 to 1996.csv

description: This dataset is used in the analysis for Kentie, R., Clegg, S.M., Tuljapurkar, S., Gaillard, J.-M. & Coulson, T. (accepted). Life history strategy varies with the strength of competition in a food-limited ungulate population. Ecology Letters.<br>Note that this dataset is previously published in Coulson, T. (2012). Integral projections models, their construction and use in posing hypotheses in ecology. Oikos, 121, 1337-1350. Coulosn acknowledges the Soay sheep project, and particularly Josephine Pemberton, Tim Clutton-Brock and Loeske Kruuk, for agreeing that the data this paper is based upon can be made publically available.
DOI: 10.6084/m9.figshare.21904071.v1
title: Percentages - descriptive analysis.pdf

description: Descriptive analysis- percetanges included in the analysis of the students peer-feedback
DOI: 10.6084/m9.figshare.26418109.v1
title: Escape the Math Room

description: Escape the Math Room is a fun, interactive, sequence of puzzles designed to encourage mathematical collaboration, team work, and ingenuity. Using advanced mathematical topics in an accessible way, Escape the Math Room appropriately challenges students ranging from high school to advanced undergraduate. The activity can be adjusted by adding or removing hints and explanations to accommodate the audience. This activity can be used on its own for a mathematical event, in class on a “free day” or as a team building exercise.
DOI: 10.6084/m9.figshare.28200599.v1
title: 40 metabolic-pathway genes from seven UCYN-A strains

description: With the data S7 and proteomics data from Coale et al. (2024) as references, we extracted the corresponding genes from the seven UCYN-A genomes.
DOI: 10.6084/m9.figshare.5357359.v1
title: One-step ahead forecasting of geophysical processes within a purely statistical framework: Supplementary material

description: Supplementary material for the paper entitled "<b>One-step ahead forecasting of geophysical processes within a purely statistical framework</b>"<br>Abstract: The simplest way to forecast geophysical processes, an engineering problem with a widely recognised challenging character, is the so called “univariate time series forecasting” that can be implemented using stochastic or machine learning regression models within a purely statistical framework. Regression models are in general fast-implemented, in contrast to the computationally intensive Global Circulation Models, which constitute the most frequently used alternative for precipitation and temperature forecasting. For their simplicity and easy applicability, the former have been proposed as benchmarks for the latter by forecasting scientists. Herein, we assess the one-step ahead forecasting performance of 20 univariate time series forecasting methods, when applied to a large number of geophysical and simulated time series of 91 values. We use two real-world annual datasets, a dataset composed by 112 time series of precipitation and another composed by 185 time series of temperature, as well as their respective standardized datasets, to conduct several real-world experiments. We further conduct large-scale experiments using 12 simulated datasets. These datasets contain 24 000 time series in total, which are simulated using stochastic models from the families of Autoregressive Moving Average and Autoregressive Fractionally Integrated Moving Average. We use the first 50, 60, 70, 80 and 90 data points for model-fitting and model-validation and make predictions corresponding to the 51st, 61st, 71st, 81st and 91st respectively. The total number of forecasts produced herein is 2 177 520, among which 47 520 are obtained using the real-world datasets. The assessment is based on eight error metrics and accuracy statistics. The simulation experiments reveal the most and least accurate methods for long-term forecasting applications, also suggesting that the simple methods may be competitive in specific cases. Regarding the results of the real-world experiments using the original (standardized) time series, the minimum and maximum medians of the absolute errors are found to be 68 mm (0.55) and 189 mm (1.42) respectively for precipitation, and 0.23 °C (0.33) and 1.10 °C (1.46) respectively for temperature. Since there is an absence of relevant information in the literature, the numerical results obtained using the standardised real-world datasets could be used as rough benchmarks for the one-step ahead predictability of annual precipitation and temperature.<br>
DOI: 10.6084/m9.figshare.11569437.v1
title: Zebrafish data from alternative models

description: We provide comparative model simulations of wild-type zebrafish patterns generated using two alternative models based off of the agent-based model from "Volkening A and Sandstede B (2018) Iridophores as a source of robustness in zebrafish stripes and variability in Danio patterns. Nat. Commun. 9(3231)." <br>Aternative model I has the following parameter values: B90(Id) = 115 um, B75(Id) = 25 um, Omega-long(Id) = 230 um, B90(Il) = 130 um, B75(ll) = 10 um, and Omega-long(Il) = 190 um, with all other parameters set to the default values in Volkening and Sandstede (2018). Alternative model II was simulated by removing the weak attraction of Xd cells to Id cells from the agent-based model of Volkening and Sandstede (2018). Patterns generated from alternative model II resemble the idefix mutation. All simulations were run in MATLAB, and we run 1000 model simulations per model.<br>Each simulation output contains data in the form of .mat files. Within each .mat file are the cell locations of all cells across the simulation, an array that tracks the distances that the cells moved throughout the simulation, vectors containing the number of cells born/dead/present at each day of the simulation, and the x- and y-boundaries of the domain for each day of the simulations. The cell locations are stored in N X 2 X T arrays, where N is an upper bound on the total number of cells in the simulation, and T is the number of days that the simulation was run for. For example, cellsM(:,:,T) contains the x- and y-coordinates of the melanophore cells on the last day of the simulation. Similarly, cellsIl contains the cell coordinates of the loose iridophores, cellsId contains the cell coordinates of the dense iridophores, cellsXc contains the cell coordinates of the dense xanthophores, and cellsXsn contains the cell coordinates of the loose xanthophores.
DOI: 10.6084/m9.figshare.28746527.v1
title: XRD analysis for S1, S3 and S4

description: The XRD images and analysis for S1,3,4
DOI: 10.6084/m9.figshare.3524633.v1
title: Appendix A. Mathematical details on the estimation of valid variance for the regression coefficient estimates of Step Selection Functions.

description: Mathematical details on the estimation of valid variance for the regression coefficient estimates of Step Selection Functions.
DOI: 10.17632/8v757rr4st
title: Crop Recommendation using Soil Properties and Weather Prediction Dataset

description: The dataset is comprehensive, encompassing various key factors critical to machine learning-based crop recommendation systems. The soil properties dataset includes detailed information such as specific locations identified by latitude and longitude coordinates, soil pH, soil color, surface soil composition, electrical conductivity, and a range of soil macro and micronutrients. These factors are essential in determining the suitability of different crops to various soil types. The crop type information in the dataset primarily comprises cereals, reflecting the major crops in the regions under study.

The climate features dataset includes a broad spectrum of environmental conditions crucial for crop growth. This includes specific location data (latitude and longitude coordinates), as well as seasonal variations such as maximum and minimum temperatures, precipitation levels, humidity, wind speed and direction, surface pressure readings, and cloud cover assessments. These features provide insights into the climatic conditions that crops will experience throughout the growing season. The climate data has been sourced from the National Aeronautics and Space Administration's (NASA) cloud infrastructure.

The soil properties and crop type data have been procured from the Ethiopian Agricultural Transformation Agency (ATA), ensuring that the dataset is grounded in reliable and locally relevant agricultural data. Additionally, crop production data has been obtained from the Ethiopian Statistics Service (ESS), providing a historical context for the crop types included in the dataset. The process of data collection was methodical. Initially, soil properties and crop type data were gathered. Following this, climate feature data was collected based on specific years and geographical coordinates (latitude and longitude), with an emphasis on anticipating the conditions for the upcoming season. This structured approach ensures that the dataset is well-rounded, providing a solid foundation for the development of a machine learning-based crop recommendation system.
DOI: 10.26180/5f55bf3dec612
title: Monash University Earth Science Garden lidar point cloud

description: This dataset is a 3D point cloud of the Monash University Earth Science garden produced by the Monash Drone Discovery Platform. It was acquired using a Robin, backpack mounted lidar system and processed using the TerraSolid Suite.<br>
DOI: 10.6084/m9.figshare.26206430.v1
title: Supplementary Material for: Increased risk of end-stage kidney disease in patients with chronic kidney disease and heart failure with reduced ejection fraction

description: Introduction: The prevalence of heart failure (HF) is more common in people with advanced non-dialysis chronic kidney disease (ND-CKD). It is well known that HF with reduced ejection fraction (HFrEF) is associated with a higher risk of mortality in people with ND-CKD compared to the general population. However, the impact of HFrEF on progression into end-stage kidney disease (ESKD) is not well studied. Our study aimed to examine the independent association of HFrEF on progression to ESKD after correcting for confounding factors using two methods of propensity scoring. 
Methods: This study used data from the Salford Kidney Study, a longitudinal study which has recruited more than 3000 patients with ND-CKD since 2002. Patients without a history of HF during the recruitment questionnaire were included in the control group. Patients with a reported history of HF and echo showing left ventricular ejection fraction &lt;40% at enrolment were included in the HFrEF group. Two propensity score methods were used to attenuate the effects of confounding factors between the two groups – propensity score matching (PSM) and inverse probability weighting (IPW). Univariate and multivariate Cox regression analyses were performed.
Results: A total of 2383 patients were included in the analysis. Patients with HFrEF had significantly higher median age and a higher percentage of male gender compared to patients with no HF (72.5 vs 66.6 years and 71.8% vs 61.1% respectively). Univariate and 5 models of multivariate Cox regression analysis showed that HFrEF in people with CKD was a strong predictor for a higher incidence of ESKD (model 5: HR 1.38; 95% CI = 1.01-1.90; p = 0.044). The association between HFrEF and the risk of ESKD remained significant after using the PSM and the IPW methods. 
Conclusion: Patients with concomitant advanced ND-CKD and prevalent HFrEF were found to have a higher risk of ESKD when compared to patients with no HF. This risk persists despite the adjustment of confounding factors using PSM and IPW.
DOI: 10.17632/f7s5zgf92b
title: Geochemical compositions of subducting sediments and volcanic lavas from global subduction zone

description: data
DOI: 10.6084/m9.figshare.12981818.v1
title: Male fertility assay

description: Numerical data on male fertility following RNAi in Tribolium beetles<br>
DOI: 10.6084/m9.figshare.25762212.v1
title: Training and testing datasets in "Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy".

description: Training and testing dataset for the MEHnet model on hydrocarbon molecules (hydrocarbons.tar.gz) and the QM9 dataset (QM9.tar.gz). We also include the raw computational data files and scripts to analyze the raw data files in Raw_data_and_scripts.zip.
DOI: 10.60893/figshare.sss.28904597.v1
title: 02001-01

description: Sb2S3 Survey (Al)
DOI: 10.6084/m9.figshare.28964759.v3
title: SRR3414629.uniq.sorted.bam

description: lblDFGDg
DOI: 10.17632/4r393cs7vs
title: Code and Dataset: "Corruption, Unemployment, and Clientelism: A Political Economy Approach"

description: These Code and Dataset corresponds to the article "Corruption, Unemployment, and Clientelism: A Political Economy Approach" published in "Economic Modelling" Journal.
DOI: 10.6084/m9.figshare.27157026.v1
title: <b>Taxing the Digital Economy for Fiscal Sustainability in Developing Countries: Lessons for African Countries</b>

description: The dataset is used to investigate the role of taxing the digital economy in achieving fiscal balance and sustainability in Africa.
DOI: 10.6084/m9.figshare.3524753.v1
title: Appendix A. Estimating the effect of egg predation on larval densities with photographs of the field site, of Hyperolius spinigularis, and of egg-stage predators.

description: Estimating the effect of egg predation on larval densities with photographs of the field site, of Hyperolius spinigularis, and of egg-stage predators.
DOI: 10.6084/m9.figshare.12318425.v1
title: Data and codes of "Behavioral responses of Japanese macaques to playback-simulated intergroup encounters"

description: (1) "<b>data_coo.csv</b>": data on coo calling(2) "<b>data_vs.csv</b>": data on visual scanning(3) "<b>data_prox.csv</b>": data on the maximum number of group members within sight (a measure of the group cohesion)(4) "<b>code_coo.stan</b>": Stan file for modeling coo calling(5) "<b>code_vs.stan</b>": Stan file for modeling visual scanning(6) "<b>code_prox.stan</b>": Stan file for modeling group cohesion(7) "<b>playback_stan.R</b>": R file for running the three models(8) "<b>summary_fit_coo.csv</b>": csv output for the summary of the posterior distributions for the model on coo calling(9) "<b>summary_fit_vs.csv</b>": csv output for the summary of the posterior distributions for the model on visual scanning<br>(10) "<b>summary_fit_prox.csv</b>": csv output for the summary of the posterior distributions for the model on group cohesion<br>
DOI: 10.6084/m9.figshare.14461796.v5
title: AttentionBase

description: AttentionBase, A Novel, Large-Scale, and Preprocessed Database for Human Attention-Level Detection
DOI: 10.6084/m9.figshare.22321468.v1
title: Raw data of the survey questionnaire

description: Raw data of the survey questionnaire
DOI: 10.6084/m9.figshare.5765352.v1
title: Wavelet coherence cryptocurrency online indicator data set

description: Online indicators and price data for the cryptocurrencies used within paper