cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18419/darus-863/10
title: knot_values.tab

description: Localized clear wood area ratios (CWAR). The relation with the knot area ratio (KAR) is: CWAR = 1 - KAR
DOI: 10.18419/darus-863/11
title: moe_autocorrelation.tar

description: Scripts for the autocorrelation analysis of localized MOE along boards.
DOI: 10.18419/darus-863/12
title: moe_simulation.tar

description: Scripts used for the simulation of MOE profiles along boards according to the model presented in the paper.
DOI: 10.18419/darus-863
title: Replication Data for: Simulation of the localized modulus of elasticity of hardwood boards by means of an autoregressive model

description: This repository contains the global and localized data gathered for a set of 52 oak boards with a cross-section of 175 x 24 mm. Modulus of elasticity, density and knot information were recorded.
description: The data for MOE and density was only measured on 47 boards, as a bug in the controlling software (later patched by the producer) broke five boards prematurely. The knot data is available for the original 52 boards, as well as the grading of each board according to DIN 4074-5 (2008).<br>

Scripts used for the analysis will be included later.
DOI: 10.18419/darus-863/13
title: knot_computation_and_grading.tar

description: These scripts are used to compute the KAR values of the boards and assign a grade according to EN 4074-5 (2008).
DOI: 10.18419/darus-1760/1
title: DSC09223.JPG
DOI: 10.18419/darus-1760/2
title: DSC09225.JPG
DOI: 10.18419/darus-1760/3
title: DSC09228.ARW
DOI: 10.18419/darus-1760/4
title: DSC09228.JPG
DOI: 10.18419/darus-1760/5
title: DSC09224.ARW
DOI: 10.18419/darus-1760/6
title: DSC09227.ARW
DOI: 10.18419/darus-1760/7
title: DSC09225.ARW
DOI: 10.18419/darus-1760/8
title: DSC09227.JPG
DOI: 10.18419/darus-1760/9
title: DSC09224.JPG
DOI: 10.18419/darus-1760/10
title: DSC09226.ARW
DOI: 10.18419/darus-1760/11
title: DSC09223.ARW
DOI: 10.18419/darus-1760
title: 2D pictures of MÜ76-18

description: &lt;p&gt;Dataset contains all pictures (.jpg; .arw) that were taken during the &lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=49056&version=DRAFT"&gt;2D digitisation strategy&lt;/a&gt; for this object.&lt;/p&gt; &lt;p&gt;Order of data: object was digitised according to a digitisation schema visible within the Dataset&lt;/p&gt;&lt;ul&gt;&lt;li&gt;characteristic view,&lt;/li&gt; &lt;li&gt;front view,&lt;/li&gt; &lt;li&gt;left view,&lt;/li&gt; &lt;li&gt;back view,&lt;/li&gt; &lt;li&gt;right view,&lt;/li&gt;&lt;li&gt;bottom view,&lt;/li&gt; &lt;li&gt;upward view, &lt;/li&gt;&lt;li&gt;details (details 1, details 2, ..., details n)&lt;/li&gt;&lt;/ul&gt;&lt;p&gt; This schema is indicated within the pictures by inventory label and then labels according to view.&lt;/p&gt;&lt;p&gt; Further information one can find in the document: Setting and workflow for basic development of 2D.&lt;/p&gt;&lt;p&gt;This dataset is part of the digital gyroscope collection. For more information about 
the collection and its creation, please see the documentation dataset:
&lt;i&gt;Niklaus, Maria, 2021, "Information about the Gyrolog data repository", 
&lt;a href="https://doi.org/10.18419/darus-821"&gt;doi: 10.18419/darus-821&lt;/a&gt;, DaRUS.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;To refer to the whole collection: Wagner, Jörg F.; Ceranski, Beate; Fritsch, Dieter; Mammadov, Gasim; Niklaus, Maria; Schweizer, Timo; Simon, Sven; Zhan, Kun, 2021, "Digital Gyroscope Collection Created by the Project 'Gyrolog'", &lt;a href="https://doi.org/10.18419/darus-gyrolog"&gt;doi: 10.18419/darus-gyrolog&lt;/a&gt;, DaRUS.&lt;/b&gt;&lt;/p&gt;
description: accessories for demonstrations
DOI: 10.18419/darus-1760/12
title: DSC09226.JPG
DOI: 10.18419/darus-1779/1
title: tex_line_plot.py
DOI: 10.18419/darus-1779/2
title: tex_tail.txt
DOI: 10.18419/darus-1779/3
title: tex_head.txt
DOI: 10.18419/darus-1779/4
title: common.hpp
DOI: 10.18419/darus-1779/5
title: Interpolation.hpp
DOI: 10.18419/darus-1779/6
title: CMakeLists.txt
DOI: 10.18419/darus-1779/7
title: performance_data.tab

description: Data generated by running test/performance.cpp (but here in tab-separated instead of csv format). The columns, as can be seen in test/performance.cpp, are: Dimension of the grid, bound for the level sum, number of points in the grid, median runtime in seconds.
DOI: 10.18419/darus-1779/8
title: main.cpp
DOI: 10.18419/darus-3786
title: Replication data of Estes group for: Reversible Oxidative Additions of Weak Acids to Pd(0) Complexes: "Effects on PdH Catalyzed Enyne Cycloisomerization"

description: All primary data files of measurements and processed data of the journal article mentioned under related publications from Estes group can be found here. The data (mainly NMR data) is structured via the names of used acids and Pd catalysts discussed in the publication. Additionally, a script for kinetic data modelling is added.
Data types: Bruker-NMR files, jupyter notebook script (python).
DOI: 10.18419/darus-3786/1
title: NMR.zip
DOI: 10.18419/darus-3786/2
title: pythonscript_kinetics.ipynb
DOI: 10.18419/darus-3786/3
title: Kinfit_test.pdf
DOI: 10.18419/darus-1779/9
title: Iterators.hpp
DOI: 10.18419/darus-1779/10
title: surf_plot.py
DOI: 10.18419/darus-1779/11
title: performance.cpp
DOI: 10.18419/darus-1779
title: Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework

description: This dataset contains supplementary code for the paper
&lt;a href=https://doi.org/10.1007/978-3-030-81362-8_4&gt;Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework&lt;/a&gt;. The code is also provided on &lt;a href=https://github.com/dholzmueller/fast_sparse_interpolation&gt;GitHub&lt;/a&gt;. Here, we additionally provide the runtime measurement data generated by the code, which was used to generate the runtime plot in the paper. For more details, we refer to the file &lt;em&gt;&lt;a href="https://darus.uni-stuttgart.de/file.xhtml?fileId=103434"&gt;README.md&lt;/a&gt;&lt;/em&gt;.
DOI: 10.18419/darus-1779/12
title: README.md
DOI: 10.18419/darus-1803/1
title: Figure_2_energy barriers of the C-N-C-O rotation.dat
DOI: 10.18419/darus-1803/2
title: 9-VS.zip
DOI: 10.18419/darus-1803/3
title: 9-OH.zip
DOI: 10.18419/darus-1803/4
title: 6-NH2.zip
DOI: 10.18419/darus-1803/5
title: 3-OH.zip
DOI: 10.18419/darus-1803/6
title: 4-VS.zip
DOI: 10.18419/darus-1803/7
title: 3-VS.zip
DOI: 10.18434/t4jp4q
title: NIST Recommended Rest Frequencies for Observed Interstellar Molecular Microwave
            Transitions, NIST Standard Reference Database 116
DOI: 10.3886/e182668v1
title: Data and code for: Predicting Cooperation with Learning Models

description: The data is compiled from 17 different experimental papers on the infinitely repeated prisoner's dilemma game. <br><br>Abstract: We use simulations of a simple learning model to predict cooperation rates in the experimental play of the indefinitely repeated prisoner’s dilemma. We suppose that learning and the game parameters only influence play in the initial round of each supergame, and that after these rounds play depends only on the outcome of the previous round. We find that our model predicts out-of-sample cooperation at least as well as models with more parameters and harder-to-interpret machine learning algorithms. Our results let us predict the effect of session length and help explain past findings on the role of strategic uncertainty.
DOI: 10.5281/zenodo.10567366
title: Data and code for 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality

description: This repository contains data and code for

Lento B., Segas E., Leconte V., Doat E., Danion F., Péteri R., Benois-Pineau J., de Rugy A. (2024). 3D-ARM-Gaze: a public dataset of 3D Arm Reaching Movements with Gaze information in virtual reality. doi:

It contains a dataset (DBAS22_DataOnline folder) of natural arm movements together with visual and gaze information when reaching objects in a wide reachable space from a precisely controlled, comfortably seated posture. More details could be find in the link publication (see Related identifiers section).

The DBAS22_DocOnline folder contains all the documentation files. The MainDataExplained file lists and describes the variables recorded during the experimental phases. In the SummaryOfFiles document, you will find descriptions for all the files within the DBAS22_DataOnline folder, and at the bottom, there is also a file tree that illustrates the file structure. The DBAS22FilesWorkflow document offers an overview of the workflow of experimental file creation during the experiment.

The DBAS22_CodeOnline folder contains all the scripts to perform data analysis, listed and described in the files CodeExplanations and DependenciesRelations. The GuideInstall file contains information needed to run the Python code files.

The DBAS22_CodeOnline folder also contains the DataPlayer Unity project. Instructions for running the project are provided in the DataPlayerGuide file and SupplementaryVideo2 (see Related identifiers section for more details). The folder DBAS22_DataPlayer_StandAloneApp contains the standalone version of the DataPlayer, which doesn't require any software installation.
description: This work was supported by a PhD grant from the French Direction Générale de l’Armement (DGA) awarded to B.L., and the ANR-PRCE grant I-Wrist (ANR-23-CE19-0031-01) awarded to A.d.R., R.P. and J.B.-P.
DOI: 10.5281/zenodo.12511781
title: Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"

description: Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (https://emcee.readthedocs.io/en/stable/).

For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes.

G-BO_script.ipynb implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.



QMC_sobol_samples: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.

nmae_all_128_ens_T_Rh: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to this link.
DOI: 10.5281/zenodo.13948391
title: Dataset package for the Manuscript "Absence of bulk charge density wave order in the normal state of UTe2"

description: The attached dataset contains raw data, normalized to the respective attenuater, reported in the manusript: 

"Absence of bulk charge density wave order in the normal state of UTe2".

The files "Figure4a.dat", "Figure4b.dat", and "Figure4c.dat" contain data that were presented in Figure 4a, Figure4b, and Figure4c of the manuscript. The first columns contain the x-axis values, the second columns the intensities, and the third column the errorbars.

The files "Figure3_N.dat" present the data in Figure 3 c. Here, N labels the (K,L)-coordinates. These are orivuded in "Figure3_KL.dat", where for a number N the N-th row presents the K and L values in the first and second column, respectively.

The files "Fig2a.dat" and "Fig2b.dat" contain the datapoints presented in Figure 2a and Figure 2b, where the first column corresponds to the x-axis coordinate and the second column to the recorded intensity.
DOI: 10.6084/m9.figshare.27618837
title: Supplementary Material for: Standardizing Nutritional Care for Cancer Patients: Implementation and Evaluation of a Malnutrition Risk Screening

description: Introduction
Cancer-related malnutrition is a highly prevalent, yet often overlooked concern in clinical practice. Although cancer-related management guidelines recommend standardized nutritional care, its implementation is scarce. The aim of this study was to investigate the prevalence of malnutrition and the medical need for nutrition counseling in cancer patients employing a novel standardized nutritional management program (containing malnutrition risk screening, nutritional assessment and counseling). Furthermore, differences of malnutrition parameters in different cancer patient cohorts were examined.

Methods
Cancer patients were screened for malnutrition using the Patient-Generated Subjective Global Assessment Short Form (PG-SGA SF) on the first day of their inpatient admission to the internal oncology or hematology wards. PG-SGA total score and classification into the three PG-SGA nutrition stages (A, B, C) were used to determine nutritional status. In case of a positive screening, nutritional assessment and individualized counseling by a nutritionist followed. For group comparisons, patients were divided into different groups (e.g., age, gender, tumor entity) and were evaluated accordingly.

Results
A total of 1,100 inpatients were included. 56.8 % of the patients had suspected or already existing malnutrition. The most common nutrition impact symptom was loss of appetite (26.7 %), followed by fatigue (16.5 %) and pain (16.0 %). Female (p &lt; 0.001), elderly (p &lt; 0.001) and patients with upper gastrointestinal tract tumors (p &lt; 0.001) showed an unfavorable nutritional status and higher need for counseling. Despite suffering from malnutrition, patients had Body Mass Indices within the upper end of the normal range.

Conclusion
This study shows a high prevalence of malnutrition in hospitalized cancer patients and highlights the need for a standardized nutritional management in the clinical setting. Therefore, it is recommended to provide a malnutrition risk screening for all cancer patients and a following adequate assessment and personalized nutritional care if needed.
DOI: 10.6084/m9.figshare.27637749
title: Supplementary Material for: Speech Perception Outcomes with the Anatomy-Based Fitting Map Among Experienced, Adult Cochlear Implant Users: A Longitudinal Study.

description: Introduction:
Anatomy-based fitting (ABF), a relatively new technique for cochlear implant programming, attempts to lessen the impact of the electrode insertion location-related frequency-to-place mismatch (FPM). This study aimed to compare vowels and consonant perception in quiet and in noise among experienced adult cochlear implant (CI) users using the ABF and the regular, conventional-based fitting (CBF) map (pre-ABF) over six months. 
Method:
Nine ears from eight experienced adult CI users were included in the experimental and longitudinal research. Using surgical planning software called Otoplan, post-operative computed CT scans were used to determine the locations of intracochlear electrodes and their angle of insertion. The anatomy-based frequency bands were produced by Maestro 9.0 CI fitting software using the Otoplan data. Nonsense syllables with consonant-vowel-consonant (CVC) recognition scores in quiet and noise (+5dB SNR) were compared at baseline, three, and six months after ABF. The vowels involved were /a, i, u/, while the consonants were voiced /b, d, g/ and voiceless /p, t, k/ plosives. Speech pieces were presented at 30 dB SL in a sound-treated room through a loudspeaker positioned at 0° azimuth. 
Results:
On average, the ABF maps shifted center frequency ranging from 0.46 semitones (0.04 octave) at (E12) to 23.94 semitones (1.99 octave) at (E1) as compared to the CBF maps. The mean vowel and consonant identification scores in quiet and in noise were significantly higher in ABF than in CBF (p&lt;0.05) with a large effect size and the trend of improvement was seen with time. Voiced consonants had better scores than the voiceless consonants. 
Conclusion:
The results demonstrated improved perception of vowels and consonants, particularly for sounds containing voicing cues after using the ABF maps. The results also suggested that ABF could be more effective for voice detection in noise. Overall, the findings indicate that correcting place mismatch with an ABF map may improve speech perception, at least among experienced adult cochlear implant users.
DOI: 10.15139/s3/nvoyh5
title: Replication Data for: Governing Through Gridlock: Bill Composition under Divided Government

description: What is the effect of divided government on issue attention in state legislatures? Much of the research on the effects of divided government examines how it affects the enactment of significant legislation but does not consider other effects on legislative behavior. In this paper, I propose a new theory of the relationship between divided government and legislative activity. Regardless of partisan control, reelection-minded legislators face pressure to deliver benefits to voters, yet divided government can make substantive policy change difficult. As a result of these competing pressures, under divided government legislators increasingly turn their focus to bills that benefit their local constituents, which are seen as easier to enact and allow them to engage in advertising, credit claiming, and position taking. I find that under divided government, legislators introduce bills at the same rate, but the type of legislation introduced shifts away from statewide policy changes and towards district-specific legislation.
DOI: 10.17632/bc3zmk2thy.1
title: TransPath Dataset: Generative Models for Grid-Based and Image-Based Pathfinding

description: Dataset for generative models for grid-based and image-based pathfinding
DOI: 10.18140/flx/1440071
title: FLUXNET2015 US-Ha1 Harvard Forest EMS Tower (HFR1)

description: This is the FLUXNET2015 version of the carbon flux data for the site US-Ha1 Harvard Forest EMS Tower (HFR1).
DOI: 10.18419/darus-1803/8
title: 6-NHTFA.zip
DOI: 10.18419/darus-1803/9
title: 10-VS.zip
DOI: 10.5066/p984mlkx
title: Topobathymetric Digital Elevation Model (TBDEM) of the Milwaukee River Estuary, MIlwaukee, WI and adjacent terrestrial and Lake Michigan nearshore coastal areas

description: This topobathymetric digital elevation model (TBDEM) mosaic represents the topography and bathymetry for the Milwaukee River Estuary in Milwaukee, Wisconsin and adjacent terrestrial and Lake Michigan nearshore coastal areas. The TBDEM was produced in support of modeling and for developing a physical habitat framework to help with understanding the effects from multidirectional currents and seiche effects associated with the mixing of river flows with Lake Michigan backwater. The TBDEM mosaic is built off existing terrestrial, nearshore, and estuary frameworks developed for other areas around the Great Lakes and the Milwaukee River Harbor. Ranging from 2008-2015, land elevations derived from lidar and historic topographic surveys and bathymetric multibeam sonar were used to generate the seamless Milwaukee River Estuary TBDEM from four different data frameworks: (1) 2015 Milwaukee River Estuary Bathymetry (Dow, 2018), (2) 2015 SEWRPC Topographic LIDAR for Southeast, Wisconsin (Office for Coastal Management (OCM) Partners, 2018), (3) 2012 NOAA Digital Coast Topographic and Bathymetric LIDAR (OCM, 2017), and (4) 2008 NOAA Digital Coast Topographic and Bathymetric LIDAR (OCM, 2008). The TBDEM is provided as a 0.6-meter resolution raster dataset in GeoTIFF format. References include: (1) Dow, Brennan,2018, Assessment and mapping of the Milwaukee Estuary Habitat, Theses and Dissertations. 1785: Milwaukee, University of Wisconsin - Milwaukee, M.S., 49 p., [Also available at https://dc.uwm.edu/etd/1785]. (2) Office for Coastal Management (OCM) Partners, 2018: 2015 SEWRPC Lidar: Southeast WI (Milwaukee, Ozaukee, Walworth, Washington, Waukesha Counties) (published 20180619): NOAA National Centers for Environmental Information, accessed January 25, 2021 at https://www.fisheries.noaa.gov/inport/item/58870 (3) Office for Coastal Management (OCM) Partners, 2017: 2012 USACE Great Lakes Topobathy Lidar: Lake Michigan (published 20170702): NOAA National Centers for Environmental Information, accessed January 25, 2021 at https://www.fisheries.noaa.gov/inport/item/49736. (4) Office for Coastal Management (OCM) Partners, 2008: 2008 USACE Great Lakes Topo/Bathy Lidar: Wisconsin (published 2008): NOAA National Centers for Environmental Information, accessed January 25, 2021, https://www.fisheries.noaa.gov/inport/item/50070.
DOI: 10.5281/zenodo.10083811
title: Equation of State, Structure, and Transport Properties of Iron Hydride Melts at Planetary Interior Conditions dataset

description: Datasets from https://doi.org/10.1029/2024JE008525

"Equation of State, Structure, and Transport Properties of Iron Hydride Melts at Planetary Interior Conditions"

Emma R. Stoutenburg1, Razvan Caracas2, Natalia V. Solomatova3, and Andrew J. Campbell1

1Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA, 2 Université Paris Cité, Institut de Physique du Globe de Paris, CNRS, 1 rue Jussieu, Paris, France, 3CNRS, Ecole Normale Supérieure de Lyon, Laboratoire de Géologie de Lyon LGLTPE UMR5276, Centre Blaise Pascal, Lyon, France

--- 

Please make sure you download data from the 30 October, 2024 version.  

--- 

ds01.xlsx

Contains pressure, density, volume, and temperature of the main dataset discussed in the paper (10 and 100 GPa). Also included are the diffusivities of iron and hydrogen in units of m2/s, viscosity in units of Pa⋅s, average Fe-Fe and Fe-H coordination numbers and bond lengths, as well as the molecular hydrogen abundance and the average number of Fe in H1, H2, and H3 clusters. Nominal pressures, Pnom are provided as these data were generalized within the text; their actual pressure, Pavg, are provided as well. (v1 vs v2: viscosity columns ([visc_Pas, visc_std] were updated, as a typo was discovered in the calculation of viscosity and it was a factor of 1/3 too small; May 2024: added molecular hydrogen data; October 2024, viscosity updated to include a neglected factor of 1/2).

ds02.xlsx

Contains all the data used in fitting the equation of state (v1-2 vs May 2024: P<10 GPa added to equation of state fit). The first sheet includes the molar volume, temperature, pressure (P), the magnetism corrected pressure (P_magcor115), and the residuals from the equation of state fits (both Birch Murnaghan and Vinet). The second sheet includes the comparison of spin polarized and non-spin polarized pressure in the iron hydrides at ~1 GPa. The third sheet includes the comparison of spin polarized and non-spin polarized pressure in pure iron. 

FeH-EOS_XfromrhoPT.py

added in Oct 2024 update, a simple python function that estimates the hydrogen content of an Fe-H melt with a given density, pressure, and temperature