cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10473583
title: [Data] Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions

description: The primary objective in creating this dataset is to map dissimilar process parameter spaces that yield the same Laser Powder Bed Fusion (LPBF) process regimes – Lack of Fusion (LoF), conduction mode, and keyhole pores – using the acoustic signatures derived from LPBF, all without supervision. To achieve this goal, two cuboid samples (width: 20 mm, length: 20 mm) were manufactured from two powder batches, namely D1 and D2. The powder underwent sieving using a mesh 325 vibrating siever (with a 45 μm gap between wires) to obtain two distributions, D1 (upper range of the sieved distribution) and D2 (powder sieved with a diameter < 45 μm). The creation of two powder size distributions aimed to investigate how changes in particle size distribution influence the characteristics of acoustic spectra when interacting with a laser source. The intentional induction of two process spaces involved using two 316L stainless steel powder distributions (> 45 µm and < 45 µm) and processing them with two sets of laser parameters. In addition to altering particle size distribution, laser power parameters were slightly offset across each regime between the two cubes to introduce a shift in the distribution of acoustic signature datasets. The three distinct processing regimes were achieved in each cuboid (D1 and D2) using different combinations of laser power and scanning speed. Verification of the occurrence of these three regimes was conducted by examining workpiece cross-sections through optical microscopy. These cross-sections were cut perpendicular to the scan tracks and further ground and polished in accordance with metallographic preparation standards. The datasets within the .zip file are labeled as: [a]_classspace_5000, [a]_rawspace_5000.npy, were 'a' can be either D1 or D2, representing two datasets with domain shifts. Each row in rawspace.npy corresponds to a ground truth in classpace.npy.
DOI: 10.57745/n8i6m0
title: Fig1.png

description: :unav
DOI: 10.57745/hzjomf
title: Figure2a_XRD_DAC1_Measured.dat
DOI: 10.57745/icuntd
title: Figure2a_XRD_DAC1_Calculated.dat
DOI: 10.57745/u6wj8n
title: Figure2bInset_XRD_DAC1_Measured_Spot1.dat
DOI: 10.57745/7hdtfj
title: Figure2bInset_XRD_DAC1_Measured_Spot2.dat
DOI: 10.57745/wjgp3v
title: Figure2bInset_XRD_DAC1_Measured_Spot3.dat
DOI: 10.57745/zdce6g
title: Figure2bInset_XRD_DAC1_Measured_Spot4.dat
DOI: 10.57745/wl6du1
title: Figure2bInset_XRD_DAC1_Measured_Spot5.dat
DOI: 10.57745/gtsgas
title: Figure2b_XRD_DAC1_Calculated.dat
DOI: 10.57745/juyfoj
title: Figure2cInset_XRD_DAC1_Calculated_Fm-3mPhase.dat
DOI: 10.57745/sahbrs
title: Figure2b_XRD_DAC1_Measured.dat
DOI: 10.57745/p7jdox
title: Figure2cInset_XRD_DAC1_Calculated_Ia-3Phase.dat
DOI: 10.57745/kyu2db
title: Figure2c_XRD_DAC1_Calculated.dat
DOI: 10.57745/6rtoc7
title: Figure2c_XRD_DAC1_Measured.dat
DOI: 10.57745/by4sma
title: Figure3aInset_Raman_DAC1_0GPa.dat
DOI: 10.57745/dqoeyi
title: Figure3aInset_Raman_DAC1_1.9GPa.dat
DOI: 10.57745/q2ikzm
title: Figure3a_Raman_DAC1_0GPa.dat
DOI: 10.57745/3mstoq
title: Figure3a_Raman_DAC1_1.9GPa.dat
DOI: 10.57745/7zdw89
title: Figure3b_Raman_1.9GPa_DAC1_ThisWork.dat
DOI: 10.57745/luhko3
title: Figure3c_Raman_DAC1_1.9GPa_ThisWork.dat
DOI: 10.57745/289yu5
title: README.txt
DOI: 10.57745/ien8up
title: FigureS1_Synthesis_HydrogenAbsorption.dat
DOI: 10.57745/baxvzd
title: FigureS10a_EDS_Lu3keV.dat
DOI: 10.57745/fe3oby
title: FigureS10a_EDS_RecoveredSample15keV.dat
DOI: 10.57745/fqqmu3
title: FigureS10a_EDS_RecoveredSample3keV.dat
DOI: 10.57745/dzxbey
title: FigureS10a_EDS_RecoveredSample5keV.dat
DOI: 10.57745/vki57z
title: FigureS10c_WDS_5keV.dat
DOI: 10.57745/xu6nxh
title: FigureS10b_WDS_5keV.dat
DOI: 10.57745/csoopa
title: FigureS11_Raman_CarbonTape.dat
DOI: 10.57745/gwwgm3
title: FigureS11_Raman_Sample0Gpa.dat
DOI: 10.57745/2w2ai4
title: FigureS2a_XRD_Measured.dat
DOI: 10.57745/wk79ti
title: FigureS2a_XRD_Calculated.dat
DOI: 10.57745/wbyvtk
title: FigureS2b_XRD_20h.dat
DOI: 10.57745/178aqd
title: FigureS2b_XRD_3h.dat
DOI: 10.57745/tkujy8
title: FigureS2b_XRD_6months.dat
DOI: 10.57745/uiptur
title: FigureS2b_XRD_9days.dat
DOI: 10.57745/gey28e
title: FigureS3aInset_Raman_DAC1_0GPaParallel.dat
DOI: 10.57745/idg49v
title: FigureS3aInset_Raman_DAC1_0GPaPerpendicular.dat
DOI: 10.57745/fkgfp0
title: FigureS3a_Raman_DAC1_0GPaParallel.dat
DOI: 10.57745/65ksvq
title: FigureS3bInset_Raman_Center.dat
DOI: 10.57745/cdugyh
title: FigureS3a_Raman_DAC1_0GPaPerpendicular.dat
DOI: 10.57745/ww8gil
title: FigureS3b_Raman_DAC1_0GPa.dat
DOI: 10.57745/nylvtj
title: FigureS3b_Raman_DAC1_0GPa_AfterDepressurising.dat
DOI: 10.57745/hvibuo
title: FigureS3b_Raman_DAC2_2.0GPa.dat
DOI: 10.57745/mg6qec
title: FigureS3b_Raman_DAC1_1.9GPa.dat
DOI: 10.57745/axlqum
title: FigureS3b_Raman_DAC3_3.5GPa.dat
DOI: 10.57745/q59c1y
title: FigureS4a_XRD_DAC2_0GPa.dat
DOI: 10.57745/mj9rzc
title: FigureS4a_XRD_DAC2_11.9GPa.dat
DOI: 10.57745/z1uv2i
title: FigureS4a_XRD_DAC2_1.1GPa.dat
DOI: 10.57745/bmq0wl
title: FigureS4a_XRD_DAC2_2.7GPa.dat
DOI: 10.57745/4tqevu
title: FigureS4a_XRD_DAC2_6.8GPa.dat
DOI: 10.57745/soaodl
title: FigureS4b_DAC2_aAxisParameter.dat
DOI: 10.57745/xcpoy4
title: FigureS4a_XRD_DAC2_9.7GPa.dat
DOI: 10.57745/mzujhj
title: FigureS4b_DAC2_cAxisParameter.dat
DOI: 10.57745/jh5jso
title: FigureS4c_DAC2_Birch-MurnaghanEOS.dat
DOI: 10.57745/yix2nr
title: FigureS4c_DAC2_Volume.dat
DOI: 10.57745/gdpfop
title: FigureS5_XRD_DAC3_3.5GPa_AfterHeating.dat
DOI: 10.57745/vzic1l
title: FigureS5_XRD_DAC3_0GPa_AfterHeatingAndDecompression.dat
DOI: 10.57745/7t9p15
title: FigureS5_XRD_DAC3_3.5GPa_BeforeHeating.dat
DOI: 10.57745/eousum
title: FigureS6a_Raman_DAC1_0GPa_Parallel.dat
DOI: 10.57745/vbbjna
title: FigureS6a_Raman_DAC1_0GPa_Perpendicular.dat
DOI: 10.57745/rwwwhi
title: FigureS6b_Raman_DAC1_1.9GPa_1stParallel.dat
DOI: 10.57745/waw4wa
title: FigureS6b_Raman_DAC1_1.9GPa_1stPerpendicular.dat
DOI: 10.57745/z63xxk
title: FigureS6b_Raman_DAC1_1.9GPa_2ndParallel.dat
DOI: 10.57745/brwncm
title: FigureS6b_Raman_DAC1_1.9GPa_2ndPerpendicular.dat
DOI: 10.57745/5s4myy
title: FigureS6b_Raman_DAC1_1.9GPa_3rdParallel.dat
DOI: 10.57745/pfybza
title: FigureS6b_Raman_DAC1_1.9GPa_3rdPerpendicular.dat
DOI: 10.57745/anaabo
title: FigureS6c_Raman_DAC1_1.9GPa_AfterHeating.dat
DOI: 10.57745/iafyvm
title: FigureS6c_Raman_DAC1_1.9GPa_BeforeHeating.dat
DOI: 10.57745/dmw99w
title: FigureS6d_Raman_DAC1_DayOfOpening.dat
DOI: 10.57745/pgnp78
title: FigureS6e_Raman_OneDayAfter.dat
DOI: 10.57745/lrprt9
title: FigureS6f_DAC1_Raman_FiveDaysAfter.dat
DOI: 10.57745/7k6bfa
title: FigureS7a_Raman_DayOfOpening.dat
DOI: 10.57745/uyvjre
title: FigureS7c_Raman_FiveDaysAfter.dat
DOI: 10.57745/w5a7q1
title: FigureS7b_Raman_OneDayAfter.dat
DOI: 10.57745/6ehpwt
title: FigureS7d_Raman_DAC1_TwelveDaysAfter.dat
DOI: 10.57745/nwyz2p
title: FigureS9a_XRD_DAC1_Spot2Calculated.dat
DOI: 10.57745/fmfzga
title: FigureS9b_XRD_DAC1_Spot4Calculated.dat
DOI: 10.57745/zxhedk
title: FigureS9a_XRD_DAC1_Spot2Measured.dat
DOI: 10.57745/7kifde
title: FigureS9b_XRD_DAC1_Spot4Measured.dat
DOI: 10.57745/1mdpzv
title: FigureS9c_XRD_DAC1_Spot6Calculated.dat
DOI: 10.57745/uza35b
title: FigureS9c_XRD_DAC1_Spot6Measured.dat
DOI: 10.57903/uj/ffrvj4
title: ZintegrUJ geometry samples - Fresnel effect

description: Fresnel Effect
DOI: 10.18750/volumechange.2023.r2023
title: Swiss Glacier Volume Change (release 2023)

description: The comparison of digital elevation models derived from aerial remote sensing allows the computation of glacier ice volume changes over periods of years to decades. Ice volume changes have been converted to geodetic mass balances assuming a density of volume change of 850 kg m-3. The dataset contains all available observations for the most important Swiss glaciers up to present and is delivered as a .csv file.
DOI: 10.6084/m9.figshare.24981000
title: Section3.1.zip

description: Macros and data for Section3.1
DOI: 10.48606/46
title: Infiltration into PDMS - Light Microscopy Data

description: This data package is part of the parent data package “Datasets for “Penetration Coefficients of Commercial Nanolimes and a Liquid Mineral Precursor into Pore-Imitating Test Systems”” (DOI 10.48606/49) belonging to the open access journal publication “Penetration Coefficients of Commercial Nanolimes and a Liquid Mineral Precursor into Pore-Imitating Test Systems - Predictability of Infiltration Behavior", which was published in MDPI Materials in 2023 (DOI 10.3390/ma16062506). It contains all data related to the infiltration of water, ethanol, iso-propanol, four different commercial nanolime formulations, and a liquid mineral precursor based on complex coacervation into polydimethylsiloxane (PDMS) microfluidic devices. In particular, original light microcopy infiltration time series data in .czi file format, as well as videos of the infiltrations as .avi files, and .csv files of the meniscus position vs. time data extracted from the .czi files using ImageJ (FIJI; Schindelin, J., Arganda-Carreras, I., Frise, E. et al. Fiji: an open-source platform for biological-image analysis. Nat Methods 9, 676–682 (2012), DOI 10.1038/nmeth.2019) and an ImageJ (FIJI) Makro provided by the Bio-Imaging Center of the University of Konstanz (Stöckl, M.T. Liquid Meniscus Tracking ImageJ Macro. Zenodo 2022, DOI 10.5281/zenodo.6385179). Please see the readme file for more details.
DOI: 10.3886/e197241
title: Supplementary data for “Wealth Inequality and Endogenous Growth”

description: Advanced economies have been experiencing productivity slowdowns, rising inequality, and low consumption-to-wealth ratios in recent decades. Using an analytically tractable
endogenous growth model with heterogeneous households, I emphasize a channel that
connects inequality with productivity growth through aggregate consumption demand and
the returns to R&D. Given realistic increases in wealth (inclusive of income) inequality,
the calibrated model generates transition dynamics featuring productivity slowdowns, low
aggregate demand, and low real interest rates, consistent with the empirical trends. The
welfare cost of rising inequality is substantial and is nearly equally split between changes
in the consumption distribution and slow growth.
DOI: 10.3886/e197241v1
title: Supplementary data for “Wealth Inequality and Endogenous Growth”

description: Advanced economies have been experiencing productivity slowdowns, rising inequality, and low consumption-to-wealth ratios in recent decades. Using an analytically tractable
endogenous growth model with heterogeneous households, I emphasize a channel that
connects inequality with productivity growth through aggregate consumption demand and
the returns to R&D. Given realistic increases in wealth (inclusive of income) inequality,
the calibrated model generates transition dynamics featuring productivity slowdowns, low
aggregate demand, and low real interest rates, consistent with the empirical trends. The
welfare cost of rising inequality is substantial and is nearly equally split between changes
in the consumption distribution and slow growth.
DOI: 10.5281/zenodo.10450344
title: CHIIR 2016-2023 publication citation contexts

description: This dataset accompanies the ACM CHIIR 2024 full paper "The Impact of CHIIR: A Study of Eight Years of CHIIR Publications". It includes citation contexts of the CHIIR publications that have been annotated with citation motivations.
DOI: 10.5281/zenodo.10450343
title: CHIIR 2016-2023 publication citation contexts

description: This dataset accompanies the ACM CHIIR 2024 full paper "The Impact of CHIIR: A Study of Eight Years of CHIIR Publications". It includes citation contexts of the CHIIR publications that have been annotated with citation motivations.
DOI: 10.22008/fk2/3qbkq7
title: Geologisk kort over Danmark, 1:50 000. 1514 I Helsingør + del af 1514 IV Tisvildeleje + del af 1414 I Hesselø. Geologisk basisdatakort

description: Well data, hydrogeology, Danian, Selandian, Weichselian
DOI: 10.22008/fk2/pyk2ma
title: Geologisk kort over Danmark, 1:50 000. 1514 III Frederiksværk. Geologisk basisdatakort

description: Well data, hydrogeology, Danian, Selandian, Weichselian
DOI: 10.5281/zenodo.10488667
title: HRDEM drainage crossings

description: This dataset is a LiDAR-derived HRDEM culvert location data and bounding boxes local coordinates for watersheds in Nebraska and Illinois.
DOI: 10.5281/zenodo.10488666
title: HRDEM drainage crossings

description: This dataset is a LiDAR-derived HRDEM culvert location data and bounding boxes local coordinates for watersheds in Nebraska and Illinois.
DOI: 10.5281/zenodo.10489241
title: Umol_no_pocket_params
DOI: 10.5281/zenodo.10489242
title: Umol_no_pocket_params
DOI: 10.5281/zenodo.10408729
title: The data and code for "Original Speech and Its Echo are Segregated and Separately Processed in the Human Brain"

description: This dataset is associated with the manuscript "Original Speech and Its Echo are Segregated and Seperately Processed in the Human Brain", and provides the preprocessed MEG response (resampling to 100 Hz), auditory stimulus, individual quantitative observations underlying the data summarized in figures, and the analysis codes.
DOI: 10.5281/zenodo.10408730
title: The data and code for "Original Speech and Its Echo are Segregated and Separately Processed in the Human Brain"

description: The data and analysis codes for the "Original Speech and Its Echo are Segregated and Seperately Processed in the Human Brain":

Experiment_Stimuli: The stimuli used in each experiment.

exp1_PreData_1-2: The preprocessed MEG response in experiment 1.

exp2_PreData_1-2: The preprocessed MEG response in experiment 2.

exp3_PreData_1-3: The preprocessed MEG response in experiment 3.

exp1_behave_result: The order of conditions and the behavior result in experiment 1.

exp2_behave_result: The order of conditions and the behavior result in experiment 2.

exp3_behave_result: The order of conditions and the behavior result in experiment 3.

data_availability: Individual quantitative observations underlying the data summarized in Figs 1-6, S1-S3, and S5.
DOI: 10.5281/zenodo.10472483
title: The data and code for "Original Speech and Its Echo are Segregated and Separately Processed in the Human Brain"

description: This dataset is associated with the manuscript "Original Speech and Its Echo are Segregated and Seperately Processed in the Human Brain", and provides the preprocessed MEG response (resampling to 100 Hz), auditory stimulus, individual quantitative observations underlying the data summarized in figures, and the analysis codes.
DOI: 10.5281/zenodo.10489293
title: Assessing microbiome population dynamics using wild-type isogenic standardized hybrid (WISH)-tags

description: Microbiomes feature recurrent compositional structures under given environmental conditions. However, these patterns may conceal diverse underlying population dynamics that require intra-strain resolution. Here, we developed a genomic tagging system, termed wild-type isogenic standardized hybrid (WISH)-tags, that can be combined with quantitative PCR and next-generation sequencing for microbial strain enumeration. We experimentally validated the performance of 62 tags and showed they can be differentiated with high precision. WISH-tags were introduced into model and non-model bacterial members of the mouse and plant microbiota. Intra-strain priority effects were tested using one species of isogenic barcoded bacteria in the murine gut and the Arabidopsis phyllosphere, both with and without microbiota context. We observed colonization resistance against late arriving strains of Salmonella typhimurium in the mouse gut, whereas the phyllosphere accommodated Sphingomonas latecomers in a proportional manner to their presence at the late inoculation timepoint. This demonstrates that WISH-tags are a resource for deciphering population dynamics underlying microbiome assembly across biological systems.
DOI: 10.5281/zenodo.8369611
title: Assessing microbiome population dynamics using wild-type isogenic standardized hybrid (WISH)-tags

description: Microbiomes feature recurrent compositional structures under given environmental conditions. However, these patterns may conceal diverse underlying population dynamics that require intra-strain resolution. Here, we developed a genomic tagging system, termed wild-type isogenic standardized hybrid (WISH)-tags, that can be combined with quantitative PCR and next-generation sequencing for microbial strain enumeration. We experimentally validated the performance of 62 tags and showed they can be differentiated with high precision. WISH-tags were introduced into model and non-model bacterial members of the mouse and plant microbiota. Intra-strain priority effects were tested using one species of isogenic barcoded bacteria in the murine gut and the Arabidopsis phyllosphere, both with and without microbiota context. We observed colonization resistance against late arriving strains of Salmonella typhimurium in the mouse gut, whereas the phyllosphere accommodated Sphingomonas latecomers in a proportional manner to their presence at the late inoculation timepoint. This demonstrates that WISH-tags are a resource for deciphering population dynamics underlying microbiome assembly across biological systems.
DOI: 10.5281/zenodo.8369612
title: Wild-type Isogenic Standardized Hybrid (WISH)-tags for assessing population dynamics within microbiomes

description: Microbiomes feature recurrent compositional structures under given environmental conditions. However, these patterns may conceal diverse underlying population dynamics that are not captured when recording strain abundances using conventional amplicon sequencing and require intra-strain resolution. To approach this, we have chosen host microbiomes as an example and developed a novel genomic tagging system, termed WISH-tags that allows enumeration by both quantitative PCR and next-generation sequencing. We experimentally validated the performance of 62 tags and showed that they enable their differentiation with a dynamic range of at least five orders of magnitude with high precision and without background DNA amplification. We introduced WISH-tags into a set of model and non-model bacteria of the mouse and plant microbiota, respectively, and confirmed equal fitness of barcoded strains compared to untagged wild type strains. We then investigated intra-strain priority effects using isogenic barcoded bacteria in the murine gut and the <em>Arabidopsis</em> phyllosphere, both with and without microbiota context. Our findings reveal a resistance to colonization by late arriving strains of <em>Salmonella</em> Typhimurium in the mouse gut, whereas the phyllosphere accommodated latecomers of <em>Sphingomonas</em> in a proportional manner to their presence at the late inoculation timepoint. This illustrates how WISH-tags can serve as a valuable resource for deciphering the population dynamics underlying microbiome assembly, including bottlenecks, and can be applied across biological systems.
DOI: 10.5281/zenodo.7523479
title: List of crowd accidents from 1900 to 2019

description: This list contains information about crowd accidents that occurred worldwide between 1900 and 2019. The files included in this dataset are described as follows:



	
accident_data_raw.csv - CSV file containing information for all accidents as given in the header. Date, country or location are provided using commonly used standard. Information such as fatalities or the number of people injured are reported using expressions found in the employed references (for example "a dozens of people" is given as "dozens"). References are given in the form of text files as reported below.
	
references.zip - ZIP file containing the references used to collect information on each accident. The date of the accident is used as filename (and reported in the last column of the list above). Sources for each accident are given on each line of the relative text file.
	
accident_data_numeric.csv - CSV file containing only numeric values corresponding to word expressions. The conversion scheme used here is provided below. Alternative approaches are possible.
	
number_conversion.csv - CSV file providing the conversion scheme used to convert word expressions into number.
DOI: 10.5281/zenodo.7523480
title: List of crowd accidents from 1900 to 2019

description: This list contains information about crowd accidents that occurred worldwide between 1900 and 2019. The files included in this dataset are described as follows:



	
accident_data_raw.csv - CSV file containing information for all accidents as given in the header. Date, country or location are provided using commonly used standard. Information such as fatalities or the number of people injured are reported using expressions found in the employed references (for example "a dozens of people" is given as "dozens"). References are given in the form of text files as reported below.
	
references.zip - ZIP file containing the references used to collect information on each accident. The date of the accident is used as filename (and reported in the last column of the list above). Sources for each accident are given on each line of the relative text file.
	
accident_data_numeric.csv - CSV file containing only numeric values corresponding to word expressions. The conversion scheme used here is provided below. Alternative approaches are possible.
	
number_conversion.csv - CSV file providing the conversion scheme used to convert word expressions into number.