cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5285/70146c789eda4296a3c3ab6706931d56
title: S-RIP: Zonal-mean heating rates of global atmospheric reanalyses on pressure levels

description: This dataset contains zonal-mean model-generated and diagnosed heating rates as potential temperature tendencies on pressure levels. The model-generated heating rates consist of total heating rates due to parameterized physics along with heating rates due to long-wave and short-wave radiative transfer, as generated during the model forecast step. The diagnosed heating rates are calculated from the zonal-mean atmospheric diagnostics (Zonal-mean reanalyses on pressure levels dataset) according to the zonal-mean thermodynamic equation. All heating rates are provided 6-hourly on identical horizontal and vertical grids as the dynamical variables included in Zonal-mean reanalyses on pressure levels dataset. However, the time axis of this dataset lags that of Zonal-mean reanalyses on pressure levels dataset by three hours. This dataset was produced to facilitate the comparison of reanalysis datasets for the collaborators of the SPARC- Reanalysis Intercomparison Project (S-RIP). The dataset is substantially smaller in size compared to the full three dimensional reanalysis fields and uses unified numerical methods. The dataset includes all global reanalyses available at the time of its development and will be extended to new reanalysis products in the future.
DOI: 10.5439/1411966
title: Shipboard Radiometer System

description: Shipboard Radiometer System
DOI: 10.15468/dl.cyfkfz
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((149.618166 -32.322071, 149.618166 -32.331054, 149.622658 -32.331054, 149.622658 -32.322071, 149.618166 -32.322071))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0048190-240626123714530/datasets/export for details.
DOI: 10.17632/7rn62y2cps.1
title: Agronomic and Environmental Assessment of Cover Crops Project Data

description: This data set includes agronomic data (soil properties, cover crop biomass, and cash crop yields) collected from an experimental setup designed to investigate the effects of cover crop and tillage practices on soil properties and crop production in Illinois. The data collection begun in the spring of 2012 and spanned until the spring of 2017 at six sites across Illinois. This project was funded by IL Nutrient Research and Education Council (IL-NREC), Award No. 2013-03556- 01 “Agronomic and Environmental Assessment of Cover Crops in Illinois.”
DOI: 10.17632/84pvnm3rj3.1
title: Spiking Neural Networks for Computer Vision

description: EVS ------------------------------ Root directory: EVS Description of data: 1. Multiscale representation: &gt; INPUT FILES: The first digit of the MNIST dataset was converted using an EVS (pyDVS) emulator and stored as (neuron id, time stamps) pairs. These were stored as text and compressed using the bzip2 Python package, resulting in the file mnist_img_00000_class_5.txt.bz2 . 2. Motion sensing: &gt; INPUT FILES: Images representing a boucing ball are stored in the bouncing_ball_sequence_w_064_h_064_bw_05.zip archive file, these were processed with an EVS emulator and results in the input file for the experiment bouncing_ball_sequence_w_064_h_064_bw_05___spikes.txt.bz2 . The files correct_0_0_3.txt and incorrect_0_0_3.txt are to be processed to generate voltage curves shown in the paper, they contain membrane voltage and slow neurotransmitter levels through the steps of a simulation. &gt; OUTPUT FILES: Motion sensing results can be found in motion_outputs___2018-02-27-12-47.pickle.bz2 , this contains a dictionary of spike arrays for each population measured during the experiments. They were stored using the standard Python pickle format and later compressed using the bzip2 Python package. ====================== MNIST Structural plasticity ------------------------------ Root directory: MNIST Structural plasticity &gt; INPUT FILES: the input MNIST Digits which are loaded into the mnist_topographic_map.py or mnist_topographic_map_rate_based.py scripts (as exemplified in open_rate_files.ipynb) &gt; RESULT FILES: contains the connectivity, spiking activity and parameters used for training and for testing The results are contained within numpy .npz files and are contained within the RESULT FILES folder. If the files' names start with "testing" then the file contains results from the testing phase of the network, conversely, the ones with prefix (starting with mnist_case_*...) contain post-training network information. Case 1 refers to networks in which both synaptic plasticity and structural plasticity operate at the same time, while in case 3 synapses have static weight, but the connectivity changes in real time. Case 2 is case 3 without lateral connectivity. The structure of the .npz data files can be interrogated in python. To this end, the iPython (Jupyter) notebooks (.ipynb) show how this connectivity/activity can be extracted and analysed. The data which contains the description "rate_based" in the name is to be analysed with the notebook "Large-scale MNIST network - rate based.ipynb", while the ones with description "cs_on_off " is to be analysed with the notebook "Large-scale MNIST network 2 source inputs.ipynb". ====================== Maximum entropy sampling ------------------------------ Root directory: Maximum entropy &gt; input: pre-processed image and label data from the MNIST training set required by the C code ======================
DOI: 10.17632/j6nr62yzs3.1
title: Code and coding sheet and coding manual for analyses

description: This is a meta-analysis examining the effectiveness of tDCS that targets the DLPFC in modulating executive function. Studies that target one DLPFC with anodal stimulation, one DLPFC with cathodal stimulation, and both left and right DLPFC are examined separately. A number of potential moderators are examined, including methodological considerations and domain of executive function. Moderators are only examined for unilateral anodal studies.
DOI: 10.17632/sx23zmtcxv.1
title: MAMA : MAternal Mood Assessment survey for healthcare professionals

description: The Maternal Mood Assessment (MAMA) is a comprehensive survey tool designed for healthcare professionals to assess their knowledge, attitudes, and experiences regarding perinatal depression. The survey is divided into several sections, each focusing on a different aspect of perinatal depression.
The first section collects socio-demographic information about the healthcare professionals, including their age, gender, professional qualification, year of graduation, years of service, and the location and context of their current work.
The second section focuses on the healthcare professionals' training and experience in dealing with perinatal depression. This includes questions about whether there is a specific pathway for perinatal depression in their facility, whether they have participated in specific training on perinatal depression, and how often they have encountered mothers with a diagnosis of perinatal depression.
The third section of the survey aims to assess the healthcare professionals' understanding and perception of perinatal depression. This includes questions about the definition of perinatal depression, the perceived prevalence, risk factors, and the most appropriate reactions to maintain a good relationship with a mother showing symptoms of perinatal depression.
The fourth section focuses on the healthcare professionals' experiences and feelings when dealing with cases of perinatal depression. This includes questions about their reactions upon learning that a patient is suffering from perinatal depression, their interactions with the family, and their opinions on the most effective approaches to support women with symptoms of perinatal depression.
The final section of the survey asks about the healthcare professionals' opinions on various aspects of managing perinatal depression, such as breastfeeding, the role of the father, the involvement of different professional figures, and the usefulness of having guidelines on the management of perinatal depression.
The MAMA survey provides a rich source of information about the perspectives and experiences of healthcare professionals in dealing with perinatal depression, which can be valuable for informing interventions and training.
DOI: 10.5281/zenodo.10001334
title: Mutational Profiling in Epithelial Ovarian Cancer: A Meta-Analysis of 435 Patients Dataset

description: Data Description

0 represents no mutation, 1 represents a mutation

Survive relates to the survival outcome of the patient. SurviveTime is in days (7300 chosen for alive patients).

Ovarian Cancer Patients



361 count

TP53, BRCA1, BRCA2, KMT2C, CDK12, CSMD3, TTN, MUC16, NF1, PALB2, RB1, PTEN, FOXM1, BRIP1, KRAS, RASA1, CHEK2, RAD50, RAD51C/D, MRE11A, NBN, FANCE, FANCD2, ATR, ATM, MSH2, PMS2, MLH1, MSH6, FAT1, FAT2, FAT3, FAT4


Uterine Cancer Patients



437 count

MSH6, CDK12, PMS2, BRIP1, FAT1, CSMD3, FAT4, BRCA2, MUC16, PALB2, KRAS, BRCA1, MSH2, PTEN, TP53, NF1, KMT2C, FAT3, FANCD2, ATM, NBN, ATR, CHEK2, FANCE, MLH1


Cervical Cancer Patients



174 count

CSMD3, CDK12, FAT1, BRIP1, RB1, FAT4, BRCA2, MUC16, PALB2, KRAS, TP53, PTEN, KMT2C, FAT3, BRCA1, NF1, ATM, FANCD2, NBN, ATR, MLH1
DOI: 10.5281/zenodo.10573493
title: MalwareBench: Malware samples are not enough

description: The prevalent use of third-party components in modern software development, coupled with rapid modernization and digitization, has significantly amplified the risk of software supply chain security attacks. Popular large registries like npm and PyPI are highly targeted malware distribution channels for attackers due to their heavy growth and dependence on third-party components. Industry and academia are working towards building tools to detect malware in the software supply chain. However, a lack of benchmark datasets containing both malware and neutral packages hampers the evaluation of the performance of these malware detection tools. The goal of our study is to aid researchers and tool developers in evaluating and improving malware detection tools by contributing a benchmark dataset built by systematically collecting malicious and neutral packages from the npm and PyPI ecosystems. 

MalwareBench is a labeled dataset aimed at aiding researchers and tool developers in evaluating and improving malware detection tools. It comprises 20,792 packages (of which 6,659 are malicious) collected systematically from the npm and PyPI ecosystems. The dataset is constructed by amalgamating pre-existing malware datasets with Socket's internal benchmark data and incorporating both popular and newly released packages.

 

Description of the data and file structure

MalwareBench included malicious and neutral packages containing package names, versions, release types, and the ground truth of the package. We annotated the ground truth label of packages in two groups:



Malware: Packages that are written intentionally to carry out harmful actions and intended to perform an unauthorized process that will have an adverse impact on the confidentiality, integrity, or availability of a system.

Neutral: Packages with no discovered malware.


In addition, we included additional metadata for packages, including the file path, file size, total number of files, file size, package size, file extension, and package group.

Sharing/Access information

We added a sample of our CSV file here. However, a complete dataset is hosted on GitHub. Since our dataset contains malware from the real world, some packages may contain sensitive information. We will distribute the dataset upon reasonable request based on ethical considerations of the purpose for using the data. After evaluating the reason for using the dataset, we will provide GitHub access. Please send an email to the authors to access the dataset. 

The data was derived from the following sources:



Backstabber’s knife collection: A review of open source software supply chain attacks.

Towards measuring supply chain attacks on package managers for interpreted languages.

An Empirical Study of Malicious Code In PyPI Ecosystem.

Open-Source Dataset of Malicious Software Packages

Socket Internal benchmark Dataset
DOI: 10.5281/zenodo.7621354
title: Model output for "Catchment coevolution and the geomorphic origins of variable source area hydrology"

description: Model output supporting "Catchment coevolution and the geomorphic origins of variable source area hydrology" for submission to Water Resources Research. The Python package DupuitLEM v1.1-alpha (DOI: 10.5281/zenodo.7620978) contains the models and scripts used to generate and post-process output.
DOI: 10.6084/m9.figshare.5768301.v1
title: Improved Methods for Making Inferences About Multiple Skipped Correlations

description: Data, code and notebooks to reproduce the examples and the simulations in:Improved Methods for Making Inferences About Multiple Skipped Correlations<br>Rand R. Wilcox, Cyril R. Pernet, Guillaume A. Rousselet<br>in preparation
DOI: 10.15156/bio/786373
title: Full UNITE+INSD dataset for eukaryotes

description: This FASTA file ("UNITE+INSDC") comprises “all” eukaryote ITS sequences of the UNITE and INSDC databases present in UNITE Species Hypotheses, updated and released some four times a year. Locked UNITE sequences and low quality (and overly short) INSDC sequences are however excluded.
DOI: 10.15468/dl.hv8hxw
title: Occurrence Download

description: A dataset containing 44979 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pantopoda"
 ]
}

 The dataset includes 44979 records from 251 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0097187-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/rhydar
title: NCBI Taxonomy

description: The NCBI taxonomy database is not a primary source for taxonomic or phylogenetic information. Furthermore, the database does not follow a single taxonomic treatise but rather attempts to incorporate phylogenetic and taxonomic knowledge from a variety of sources, including the published literature, web-based databases, and the advice of sequence submitters and outside taxonomy experts. Consequently, the NCBI taxonomy database is not a phylogenetic or taxonomic authority and should not be cited as such.
DOI: 10.1594/pangaea.848612
title: Daily MODIS composites of thin-ice thickness and ice-surface temperatures for the Southern Weddell Sea

description: Based upon high-resolution thermal-infrared Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with ERA-Interim atmospheric reanalysis data, we derived long-term polynya parameters such as polynya area, thin-ice thickness distribution and ice-production rates from daily cloud-cover corrected thin-ice thickness composites. Our study is based on a thirteen year investigation period (2002-2014) for the austral winter (1 April to 30 September) in the Antarctic Southern Weddell Sea. The focus lies on coastal polynyas which are important hot spots for new-ice formation, bottom-water formation and heat/moisture release into the atmosphere. MODIS has the capability to resolve even very narrow coastal polynyas. Its major disadvantage is the sensor limitation due to cloud cover. We make use of a newly developed and adapted spatial feature reconstruction scheme to account for cloud-covered areas. We find the sea-ice areas in front of Ronne and Brunt Ice Shelf to be the most active with an annual average polynya area of 3018 ± 1298 and 3516 ± 1420 km2 as well as an accumulated volume ice production of 31 ± 13 and 31 ± 12 km**3, respectively. For the remaining four regions, estimates amount to 421 ± 294 km**2 and 4 ± 3 km**3 (Antarctic Peninsula), 1148 ± 432 km**2 and 12 ± 5 km**3 (Iceberg A23A), 901 ± 703 km**2 and 10 ± 8 km**3 (Filchner Ice Shelf) as well as 499 ± 277 km**2 and 5 ± 2 km**3 (Coats Land). Our findings are discussed in comparison to recent studies based on coupled sea-ice/ocean models and passive-microwave satellite imagery, each investigating different parts of the Southern Weddell Sea.
description: This data set contains daily MODIS composites from April to September for the time interval from 2002 to 2014. Thin-ice thickness data up to 0.2m are derived from the MOD/MYD29 ice-surface temperature product of the National Snow and Ice Data Center (NSIDC) using a simple surface-energy-balance and thermodynamic sea-ice model. This data is corrected for influence of cloud cover and available for the complete coast line of the Southern Weddell Sea. For details on the procedures, please consult the given reference. This study was funded by the Deutsche Forschungs Gemeinschaft in the framework of the priority program SPP1158 "Antarctic Research with comparative investigations in Arctic ice areas" by grant HE2740/12.
description: Supplement to: Paul, Stephan; Willmes, Sascha; Heinemann, Günther (2015): Long-term coastal-polynya dynamics in the Southern Weddell Sea from MODIS thermal-infrared imagery. The Cryosphere, 9(6), 2027-2041
DOI: 10.1594/pangaea.902334
title: Irradiance from SUIT measurements during POLARSTERN cruise PS89

description: Measured under-ice irradiance and derived transmittance for each haul. The associated ice draft values are the values from the corresponding TSC file resampled at the frequency of the RAMSES irradiance measurements.
DOI: 10.5066/p96e4uck
title: Canine distemper virus antibody titer results for grizzly bears and wolves in the Greater Yellowstone Ecosystem 1984-2014

description: We investigated the dynamics of canine distemper virus (CDV) in grizzly bears (Ursus arctos) and wolves (Canis lupus) of the Greater Yellowstone Ecosystem using serological data collected from 1984 to 2014. 565 sera samples were obtained from 425 unique grizzly bears (134 females and 291 males) from 1984 to 2014 and 319 sera samples were obtained from 285 unique wolves (130 females and 155 males) from 1996 to 2014. Here we provide the unique identifier for each individual, the species and sex, the date the individual was captured, the estimated birth year and age, and the CDV antibody titer results based upon serum neutralization assays. (GYE)
DOI: 10.5281/zenodo.10570890
title: mzhangrocks/KarakoramFault: Data files for hydrothermal degassing from the Karakoram fault

description: Tables S1–S2 and Data Sets S1–S2 for geochemical study on hydrothermal degassing from the Karakoram fault in western Tibetan Plateau
DOI: 10.5281/zenodo.10579661
title: Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples - Code and Data

description: This repository contains the code and data for reproducibility of the paper 'Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples'. 

The following files are included:



Plots: Additional plots not in the paper;

Code: Python scripts to generate trajectories and perform classification/regression;

best_algo.csv : Labels for the classification;

performances.csv : Performances used for the regression;

SA_parameters.csv : SA parameters for all machine learning tasks;

irace_scenario.txt : scenario used for the tuning.
DOI: 10.5281/zenodo.10659150
title: Co-Designing a Wiki-Based Community Knowledge Management System for Personal Science - Supplementary Materials

description: This folder contains supplementary materials for the article "Co-Designing a wiki-based community knowledge management system for personal science" by Katharina Kloppenborg, Mad Price Ball, Steven Jonas, Gary Isaac Wolf, and Bastian Greshake Tzovaras.
DOI: 10.5281/zenodo.10881960
title: Basal force fluctuations and granular rheology: Linking macroscopic descriptions of granular flows to bed forces with implications for monitoring signals

description: Herein lies user defined functions, submission scripts, and intitial particle data necessary to build a discrete element solver (using MFiX-DEM version 21.1.1: https://mfix.netl.doe.gov/products/mfix/) and run a monodisperse pure granular flow with 5mm beads in a shear cell configuration. Contained within this dataset are particle velocity and force data for the entire collection of particles ('VELOCITY'* and 'FORCES*.DAT', respectively). This dataset also contains particle-force data recorded at the bottom force plate, writen at 10 kHz. Two MATLAB scripts are included for post processing the force plate data.
DOI: 10.5281/zenodo.4449934
title: Exploratory Landscape Analysis Feature Values for the 24 Noiseless BBOB Functions

description: The dataset contains feature values for the so-called "cheap" features available in the R package <em>flacco</em> [https://github.com/kerschke/flacco] on the 24 noiseless BBOB functions of the COCO benchmark environment [https://github.com/numbbo/coco]. Precisely, the data set covers <strong>raw data</strong> and <strong>median</strong> feature values for the following <strong>Feature Sets: </strong><em>dispersion, \(y\)-distribution, meta model, information content, nearest better clustering</em> and <em>principal component analysis.</em> <strong>Functions: </strong>24 noiseless BBOB functions of the COCO benchmark environment [https://github.com/numbbo/coco]. <strong>Instances: </strong>First five instances of each function. <strong>Dimension \(d\): </strong>6 different dimensions, \(d \in \{5,10,15,20,25,30\}\). <strong>Sample size ("budget") \(b\) for Feature Extraction: </strong>7 different options per dimension, \(b \in \{ 30d, 50d, 100d, 250d, 650d, 800d, 1000d\}\). <strong>Sampling Distribution:</strong> Features are computed using Sobol' low-discrepancy sequence as sampling strategy. <strong>Repetitions: </strong>100 independent repetitions are available. Caused by the heavy computation times, <strong> some data records are missing:</strong> The <em>dispersion</em> feature values are not available for \((d=25,b=1000d), (d=30,b=800d) \) and \((d=30,b=1000d)\); Instances 2 to 5 are not available for: \(1000 d\) budgets with \(d \geq 10\) \(800d\) budgets with \(d \geq 15\) \(650d\) budgets with \(d \geq 20\) \(250d\) budgets with \(d = 30\)