cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4121/b5001ef2-b2f4-4535-92aa-f3988914b22e
title: Matlab fig files for the paper 'Experimental gas-fluidized bed drying study on the segregation and mixing dynamics for binary and ternary solids'

description: This is a set of Matlab .fig files corresponding to the figures in the paper 'Experimental gas-fluidized bed drying study on the segregation and mixing dynamics for binary and ternary solids' published in Chemical Engineering Journal. In this study, experiments in a pseudo-2D fluidized bed setup were performed to obtain insight in the complex and changing bed hydrodynamics and its interplay with mass and heat transfer. The data was obtained by using a combined Particle Image Velocimetry (PIV), Digital Image Analysis (DIA) and Infrared (IR) technique. Furthermore, a machine learning algorithm was applied in order to determine the segregation and mixing dynamics.<br>The dataset consists of the following .fig files whereof a short description is given below:<br>Figure 3: Minimum fluidization velocity determination using the mean pressure drop over the bed. Figure 4: Precision-recall analysis<br>Figure 8: Segregation index based on the average height of the medium and large-sized solids.Figure 9: Segregation index based on the average height of the medium and large-sized solids.Figure 10 A, B, C and D: Time-averaged solids volume fluxes for the u0=0.975 m/s drying case at four different times.Figure 11 A, B, C and D: Time-averaged solids volume fluxes for the u0=1.17 m/s drying case at four different times.Figure 12 A, B, C and D: Time-averaged solids volume fluxes for the u0=1.365 m/s drying case at four different times.Figure 13 A and B: Mean particle temperature and standard deviation over time for the three different superficial gas velocities.<br>Figure 17 A and B: Segregation indices based on the average height of the small, medium and large-sized solids.Figure 18 A, B, C and D: Time-averaged solids volume fluxes for the u0=0.7875 m/s drying case at four different times.Figure 19 A, B, C and D: Time-averaged solids volume fluxes for the u0=0.945 m/s drying case at four different times.Figure 20 A, B, C and D: Time-averaged solids volume fluxes for the u0=1.1025 m/s drying case at four different times.Figure 21 A and B: Mean particle temperature and standard deviation over time for the three different superficial gas velocities.
DOI: 10.4121/b5001ef2-b2f4-4535-92aa-f3988914b22e.v2
title: Matlab fig files for the paper 'Experimental gas-fluidized bed drying study on the segregation and mixing dynamics for binary and ternary solids'

description: This is a set of Matlab .fig files corresponding to the figures in the paper 'Experimental gas-fluidized bed drying study on the segregation and mixing dynamics for binary and ternary solids' published in Chemical Engineering Journal. In this study, experiments in a pseudo-2D fluidized bed setup were performed to obtain insight in the complex and changing bed hydrodynamics and its interplay with mass and heat transfer. The data was obtained by using a combined Particle Image Velocimetry (PIV), Digital Image Analysis (DIA) and Infrared (IR) technique. Furthermore, a machine learning algorithm was applied in order to determine the segregation and mixing dynamics.<br>The dataset consists of the following .fig files whereof a short description is given below:<br>Figure 3: Minimum fluidization velocity determination using the mean pressure drop over the bed. Figure 4: Precision-recall analysis<br>Figure 8: Segregation index based on the average height of the medium and large-sized solids.Figure 9: Segregation index based on the average height of the medium and large-sized solids.Figure 10 A, B, C and D: Time-averaged solids volume fluxes for the u0=0.975 m/s drying case at four different times.Figure 11 A, B, C and D: Time-averaged solids volume fluxes for the u0=1.17 m/s drying case at four different times.Figure 12 A, B, C and D: Time-averaged solids volume fluxes for the u0=1.365 m/s drying case at four different times.Figure 13 A and B: Mean particle temperature and standard deviation over time for the three different superficial gas velocities.<br>Figure 17 A and B: Segregation indices based on the average height of the small, medium and large-sized solids.Figure 18 A, B, C and D: Time-averaged solids volume fluxes for the u0=0.7875 m/s drying case at four different times.Figure 19 A, B, C and D: Time-averaged solids volume fluxes for the u0=0.945 m/s drying case at four different times.Figure 20 A, B, C and D: Time-averaged solids volume fluxes for the u0=1.1025 m/s drying case at four different times.Figure 21 A and B: Mean particle temperature and standard deviation over time for the three different superficial gas velocities.
DOI: 10.4121/e4324d0c-2a82-4f03-8cbb-64dd207cd522
title: Research data for the paper Triangulating the Future: Developing scenarios of cyclist-automated vehicle interactions from literature, expert perspectives, and survey data

description: Research data for the paper Berge, S. H., de Winter, J., Cleij, D., & Hagenzieker, M. (2023). Triangulating the future: Developing scenarios of cyclist-automated vehicle interactions from literature, expert perspectives, and survey data (preprint).In this study, we conducted a systematic literature review on cyclists and automated vehicles, held group interviews with eight traffic safety and automation experts, and gathered questionnaire data. The resulting scenario collection features 20 prototypical scenarios, categorised based on the road users' direction of movement. The survey results helped identify high-risk situations and areas of particular relevance for studying automated vehicle communication solutions.The data set contains a .gdpx project file with anonymised transcripts, the full thematic analysis of the interview data, and a .xlsx file with the descriptive analysis and raw data from the questionnaire.
DOI: 10.4121/e4324d0c-2a82-4f03-8cbb-64dd207cd522.v1
title: Research data for the paper Triangulating the Future: Developing scenarios of cyclist-automated vehicle interactions from literature, expert perspectives, and survey data

description: Research data for the paper Berge, S. H., de Winter, J., Cleij, D., & Hagenzieker, M. (2023). Triangulating the future: Developing scenarios of cyclist-automated vehicle interactions from literature, expert perspectives, and survey data (preprint).In this study, we conducted a systematic literature review on cyclists and automated vehicles, held group interviews with eight traffic safety and automation experts, and gathered questionnaire data. The resulting scenario collection features 20 prototypical scenarios, categorised based on the road users' direction of movement. The survey results helped identify high-risk situations and areas of particular relevance for studying automated vehicle communication solutions.The data set contains a .gdpx project file with anonymised transcripts, the full thematic analysis of the interview data, and a .xlsx file with the descriptive analysis and raw data from the questionnaire.
DOI: 10.5281/zenodo.7907535
title: Murreviikko: an Annotated and Normalized Corpus of Dialectal Finnish Tweets

description: Murreviikko (literally 'Dialect week') is a campaign founded in the University of Eastern Finland to promote the use of Finnish dialects in social media. It started in 2020 and takes place mid-October. The original data was collected from Twitter with the search word murreviikko ('dialect week') and hashtag #murreviikko separately for 2020, 2021 and 2022. The current dataset combines all the original collections. The tweets are dialectologically annotated on two levels: following the East-West division of Finnish dialects, and following a seven-way division of Finnish dialects (South-West, Häme, Southern Ostrobothnia, Central and Northern Ostrobothnia, Far North, Savo, and South-East), appended with the Helsinki slang. There is also a class for dialectal tweets, which are not discernible (NA) because of contrasting or scarce dialectal features. The original tweets are normalized to a phonetic standard, but word order is not altered, or grammar rules of standard Finnish followed otherwise. This means that for instance standard Finnish possessive suffixes (minun kirja-ni 'my book-my') are not added if they are not present in the original tweet (minun kirja). Likewise, dialect words are not corrected to the standard alternative, even if such words would exist (pruukata &gt; pruukata instead of standard tavata). Following the rules of the Twitter API, this repository only includes the tweet id's, dialect annotations and normalizations. The original tweets are available for scientific use by request, as granted by the European Union’s Digital Single Market directive (2019/790).
description: {"references": ["Olli Kuparinen. 2023. Murreviikko - A Dialectologically Annotated and Normalized Dataset of Finnish Tweets. In Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023), pages 31\u201339, Dubrovnik, Croatia. Association for Computational Linguistics."]}
DOI: 10.5281/zenodo.7907534
title: Murreviikko: an Annotated and Normalized Corpus of Dialectal Finnish Tweets

description: Murreviikko (literally 'Dialect week') is a campaign founded in the University of Eastern Finland to promote the use of Finnish dialects in social media. It started in 2020 and takes place mid-October. The original data was collected from Twitter with the search word murreviikko ('dialect week') and hashtag #murreviikko separately for 2020, 2021 and 2022. The current dataset combines all the original collections. The tweets are dialectologically annotated on two levels: following the East-West division of Finnish dialects, and following a seven-way division of Finnish dialects (South-West, Häme, Southern Ostrobothnia, Central and Northern Ostrobothnia, Far North, Savo, and South-East), appended with the Helsinki slang. There is also a class for dialectal tweets, which are not discernible (NA) because of contrasting or scarce dialectal features. The original tweets are normalized to a phonetic standard, but word order is not altered, or grammar rules of standard Finnish followed otherwise. This means that for instance standard Finnish possessive suffixes (minun kirja-ni 'my book-my') are not added if they are not present in the original tweet (minun kirja). Likewise, dialect words are not corrected to the standard alternative, even if such words would exist (pruukata &gt; pruukata instead of standard tavata). Following the rules of the Twitter API, this repository only includes the tweet id's, dialect annotations and normalizations. The original tweets are available for scientific use by request, as granted by the European Union’s Digital Single Market directive (2019/790).
description: {"references": ["Olli Kuparinen. 2023. Murreviikko - A Dialectologically Annotated and Normalized Dataset of Finnish Tweets. In Tenth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2023), pages 31\u201339, Dubrovnik, Croatia. Association for Computational Linguistics."]}
DOI: 10.22008/fk2/ibhizl/b39nzb
title: Pannertooq_72v2s_1,5k_180522_V4a_WEB.pdf

description: :unav
DOI: 10.22008/fk2/ibhizl/wus6zt
title: GeologicalMapofGreenland_G100_Pannertooq_72v2s.pdf
DOI: 10.5066/p9jg3j7b
title: Gulf Islands National Seashore 2020 Workshop: Attendee Survey Results

description: The National Park Service (NPS), in collaboration with the U.S. Geological Survey (USGS), recognizes the need to quantify the sediment budget of the barrier islands within the Gulf Islands National Seashore (GINS) to understand the coastal processes affecting island resiliency. To achieve this goal, identifying and quantifying the physical parameters that drive long-term change is necessary to model the processes that are both generative and terminal in island evolution and capture island response to long-term human alteration and climatic patterns. To address this need, scientists sought to advance sediment budget modeling efforts by conducting a 'Needs Assessment Workshop' for the GINS. The purpose of the workshop was to identify and prioritize the specific research and data needs regarding the sediment budget at the GINS that can enhance the NPS efforts to conserve the islands natural resources, cultural resources, and the facilities and infrastructure that support both conservation and visitor use of those resources. The 'Gulf Islands Sediment Budget Needs Assessment Workshop' was held, virtually, April 23-24 and May 27-28, 2020.The workshop series was organized by researchers from North Carolina State University in collaboration with NPS and USGS staff and was facilitated by National Oceanographic and Atmospheric Administration (NOAA) personnel. This data release (Forde and Flocks, 2023) includes survey results from two questionnaires submitted to attendees during the 2020 GINS sediment budget workshop. For further information pertaining to workshop details and results, refer to the associated USGS Open-File Report (Seekamp and others, 2023). Seekamp, E., Flocks, J., Hotchkiss, C., York, L., and Irick, K., 2023, Gulf Islands National Seashore regional sediment budget research and data needs-Workshop series summary: U.S. Geological Survey Open-File Report 2022-1087, 46 p., https://doi.org/10.3133/ofr20221087.
DOI: 10.34740/kaggle/dsv/5632843
title: 2021 World Population (updated daily)
DOI: 10.5066/p91p1t88
title: Sediment Data From Vibracores and Sand Augers Collected in 2021 and 2022 From Fire Island, New York

description: In 2021 and 2022, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) and the USGS New York Water Science Center (NYWSC), on behalf of SPCMSC, conducted sediment sampling and ground penetrating radar (GPR) surveys at Point O' Woods and Ho-Hum Beach (NYWSC, 2021) and Watch Hill, Long Cove, and Smith Point (SPCMSC, 2022), Fire Island, New York. These data complement previous SPCMSC GPR and sediment sampling surveys conducted at Fire Island in 2016. This data release serves as an archive of vibracore and sand-auger sediment cores collected from back-barrier environments on Fire Island from March 29-April 3, 2021 and April 19-22, 2022 (USGS Field Activity Number 2021-312-FA). GPR data collected during the same survey are available as a separate data release (Forde and others, 2022). Sedimentologic data from these cores, including descriptive core logs, grain-size data, and the results of radiocarbon (14C) analyses, are provided to characterize and date shallow subsurface stratigraphic units such beach, dune, or washover deposits and support analyses presented by Ciarletta and others (2022). Data acquisition and processing methods are modified from USGS Data Series 1037 and 1100. Forde, A.S., Bernier, J.C., and Ciarletta, D.J., 2022, Ground penetrating radar and global positioning system data collected in 2021 from Fire Island, New York: U.S. Geological Survey data release, https://doi.org/10.5066/P97YW2UL Buster, N.A., Bernier, J.C., Brenner, O.T., Kelso, K.W., Tuten, T.M., and Miselis, J.L., 2018, Sediment data from vibracores collected in 2016 from Fire Island, New York: U.S. Geological Survey Data Series 1100, https://doi.org/10.3133/ds1100. Bernier, J.C., Kelso, K.W., Tuten, T.M., Stalk, C.A., and Flocks, J.G., 2017, Sediment data collected in 2014 and 2015 from around Breton and Gosier Islands, Breton National Wildlife Refuge, Louisiana: U.S. Geological Survey Data Series 1037, https://doi.org/10.3133/ds1037.
DOI: 10.35003/h0qf3h/xfinww
title: ElfrunLehmannHarryBeckerFreieUniversitaetBerlin_Talk_EGU_2022_final_2.pdf

description: Powerpoint presentation
DOI: 10.6084/m9.figshare.22778945
title: noisy-kgs-benchmark

description: Authors: Andrea Lenzi, Stefano Faralli, Paola Velardi<br> Affiliation: Computer Science Department, Sapienza University of Rome, Rome, Italy<br> Description: this is a repository for preserving, sharing, and maintaining a benchmark of Knowledge Graph Emabedding-based link prediction, deletion, and pruning systems with noisy data.
DOI: 10.6084/m9.figshare.22778945.v1
title: noisy-kgs-benchmark

description: Authors: Andrea Lenzi, Stefano Faralli, Paola Velardi<br> Affiliation: Computer Science Department, Sapienza University of Rome, Rome, Italy<br> Description: this is a repository for preserving, sharing, and maintaining a benchmark of Knowledge Graph Emabedding-based link prediction, deletion, and pruning systems with noisy data.
DOI: 10.25345/c5ms3kb53
title: MassIVE MSV000091879 - Identification of PTPD meA splicing variants
title: Leukocyte common antigen-related receptor protein tyrosine phosphatases (LAR-RPTPs) are evolutionarily conserved presynaptic cell-adhesion molecules that orchestrate multifarious synaptic adhesion pathways. Extensive alternative splicing of LAR-RPTP mRNAs, similar to neurexins, may produce innumerable LAR-RPTP isoforms that act as regulatory codes for determining the identify and strength of specific synapse signaling. However, no direct evidence for the hypothesis exists, apart from the role of LAR-RPTP splice variants in specifying region-specific, cell type-specific and neural circuit-specific properties in vivo. Here, using deep RNA sequencing (RNA-seq), we targeted Ptprs, Ptprd, and Ptprf mRNAs in diverse cell types across adult mouse brain areas. In addition to identifying novel alternatively spliced exons of Ptprd, we found pronounced cell-type-specific patterns of two microexons, meA and meB, in brain Ptprd mRNAs. Quantitative targeted proteomics uncovered profiles of LAR-RPTP proteoforms containing or lacking meA that are in line with RNA-seq results. Moreover, diverse neural circuits targeting the same neuronal popluations were dictated by the expression of different Ptprd variants characterized by distinct inclusion patterns of meA and/or meB. Remarkably, fear-related learning induced upregulation of Ptprd meA+ variants in hippocampal memory-activated dentate gyrus neurons. Furthermore, conditional ablation of Ptprd meA+ variants at presynaptic loci of distinct hippocampal circuits resulted in impairment of distinct modes of synaptic transmission and different cognitive tasks. Our data provide the first evidence of cell-type- and/or circuit-specific expression patterns and physiological functions of LAR-RPTP microexons that are dynamically regulated and likely to dictate diverse synaptic properties. In particular, we propose Ptprd splicing as a key mechanism that mediates activity-dependent neural circuit specification
DOI: 10.35003/rmme51
title: Replication Data for: Re‐examination of the Population, Stratigraphy, and Sequence of Mercurian Basins: Implications for Mercury's Early Impact History and Comparison With the Moon

description: This data set contains the derived data products for the published manuscript Orgel et al. (2020) "Re-examination of the population, stratigraphy, and sequence of mercurian basins: Implications for Mercury´s early impact history and comparison with the Moon" at Journal of Geophysical Research Planets. This data package includes GIS-ready shapefiles of Mercurian basins, crater counting measurements, and Craterstats files.
DOI: 10.35003/rmme51/vvvthn
title: Orgel_etal_2020_Mercury basins.zip

description: :unav
DOI: 10.22008/fk2/nvro0e
title: Caledonian Orogen, East Greenland 70° – 82°N. Geological map 1:1 000 000

description: Geological map, East, North-East and eastern North Greenland
DOI: 10.35003/hvtjqd
title: Replication Data for: Quantification of impact-induced melt production in numerical modelling revisited

description: • We provide an improved method to quantify impact-induced melt production for rocks. • We quantify impact-induced melt production and separate between heating due to shock compression and plastic work. • Melting due to frictional heating (plastic work) dominates over shock melting for impact velocities below 7-13 km/s depending on strength.
DOI: 10.35003/hvtjqd/5a2o2d
title: dunite_.input

description: :unav
DOI: 10.35003/hvtjqd/kh8y90
title: material.inp

description: :unav
DOI: 10.35003/hvtjqd/wi3ny7
title: gameta_scaling_no_ref_jet.py

description: :unav
DOI: 10.35003/hvtjqd/2mnkss
title: asteroid.inp

description: :unav
DOI: 10.35003/hvtjqd/u470ez
title: material.inp

description: :unav
DOI: 10.35003/hvtjqd/n6vh7v
title: asteroid.inp

description: :unav
DOI: 10.35003/hvtjqd/d4b32r
title: tracer_help.F90

description: :unav
DOI: 10.35003/hvtjqd/xrixun
title: Melt_Method_Paper_Submission_AGU_reworked_preliminiary.docx

description: :unav
DOI: 10.35003/hvtjqd/sz3k3x
title: material.inp

description: :unav
DOI: 10.35003/hvtjqd/1u70q5
title: asteroid.inp

description: :unav
DOI: 10.35003/hvtjqd/moo9xz
title: asteroid.inp

description: :unav
DOI: 10.35003/hvtjqd/cnagmz
title: Note.docx

description: :unav
DOI: 10.35003/hvtjqd/8re5ra
title: material.inp

description: :unav
DOI: 10.35003/hvtjqd/zntafr
title: asteroid.inp

description: :unav
DOI: 10.35003/hvtjqd/fbfgte
title: material.inp

description: :unav
DOI: 10.35003/hvtjqd/fmx9ib
title: update_energy.F90

description: :unav
DOI: 10.35003/hvtjqd/xrgkez
title: References.docx

description: :unav
DOI: 10.35003/hvtjqd/jpv9g5
title: Figures 1-6 and captions.docx

description: :unav
DOI: 10.35003/hvtjqd/r7es7k
title: Figures 1-6 and captions.docx

description: :unav
DOI: 10.35003/hvtjqd/qnlx7k
title: gameta_scaling.py

description: :unav
DOI: 10.35003/hvtjqd/ask2bo
title: Note.docx

description: :unav
DOI: 10.35003/7wowqi
title: Replication Data for: Melt production and ejection from lunar intermediate sized impact craters: Where is the molten material deposited?

description: Differently aged impact melt in lunar samples is key to unveiling the early bombardment history of the Moon. Due to the mixing of melt products ejected from distant craters, the interpretations of the origin of lunar samples are difficult. We use numerical modelling for a better quantitative understanding of the production of impact-induced melt and in particular its distribution in ejecta blankets for lunar craters with sizes ranging from 1.5 to 50 km. We approximate the lunar stratigraphy with a porosity gradient, which represents the gradual transition from upper regolith via megaregolith to solid crustal material. For this lunar setting, we quantify melt production relative to crater volume and derive parameters describing its increasing trend with increasing transient crater size. We found about 30%-40% of the produced melt is ejected from the crater. The melt concentration in the ejecta blanket increases almost linearly with distance from the crater center, whilst the thickness of the ejecta blanked decreases following a power-law. Our study demonstrates that if the concentration of distant-derived melt of a certain age in lunar samples is rather high (&gt;30%), it could originate from large craters (&gt;10 km) located hundreds of kilometers away.
DOI: 10.35003/7wowqi/p5wudq
title: fig5.rtf

description: :unav
DOI: 10.35003/7wowqi/7dsory
title: fig1.tab

description: :unav
DOI: 10.35003/7wowqi/c3pkkx
title: fig3.txt

description: :unav
DOI: 10.35003/7wowqi/y39lrf
title: fig4.txt

description: :unav
DOI: 10.35003/7wowqi/jpx6yq
title: asteroid_2km18v_noA.inp

description: :unav
DOI: 10.35003/7wowqi/gmecpr
title: asteroid_1km18v_noA.inp

description: :unav
DOI: 10.35003/7wowqi/nyeezo
title: asteroid_3km18v_noA.inp

description: :unav
DOI: 10.35003/7wowqi/jh71fq
title: asteroid_2km16Tr_noA.inp

description: :unav
DOI: 10.35003/7wowqi/rbpmf5
title: asteroid_1km16Tr.inp

description: :unav
DOI: 10.35003/7wowqi/xadtrs
title: asteroid_3km16Tr_noA.inp

description: :unav
DOI: 10.35003/7wowqi/wb4ku1
title: material_tl2.inp

description: :unav
DOI: 10.35003/7wowqi/0q5nfc
title: asteroid_01km16Tr.inp

description: :unav
DOI: 10.35003/7wowqi/npkdpy
title: asteroid_1km18v.inp

description: :unav
DOI: 10.35003/7wowqi/adfjmw
title: asteroid_05km18v_noA.inp

description: :unav
DOI: 10.35003/7wowqi/ptdavd
title: asteroid_01km18v.inp

description: :unav
DOI: 10.35003/7wowqi/prruhc
title: asteroid_2km16Tr.inp

description: :unav
DOI: 10.35003/7wowqi/w3nkxb
title: asteroid_05km16Tr.inp

description: :unav
DOI: 10.35003/7wowqi/ptu2gl
title: asteroid_05km18v.inp

description: :unav