cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23550013
title: Drinking Motives, Alcohol Misuse, and Internalizing and Externalizing Psychopathology across College: A Cross-Lagged Panel Study

description: <i>Background:</i> Drinking motives are strong proximal predictors of alcohol use behaviors and may represent a mediational mechanism by which different individual predispositions toward internalizing or externalizing psychopathology lead to the development of alcohol misuse. However, whether the association is due to a causal relationship or a shared etiology (i.e., confounding) is difficult to determine and may change across developmental periods. <i>Methods:</i> This study leveraged a cross-lagged panel design to disentangle the nature of the relationships between self-report measures of drinking motives, alcohol misuse, and internalizing and externalizing psychopathology in a 4-year longitudinal sample of college students (<i>N</i> = 9,889). <i>Results:</i> Results pointed to a putative causal effect of drinking motives on early binge drinking frequency, but the direction of effect later reversed, reflecting a possible developmental shift during college. On the other hand, the relationships between drinking motives and internalizing/externalizing psychopathology appeared to be driven by shared etiology rather than direct causal mechanisms. <i>Conclusions:</i> These findings highlight the distinct and important role of drinking motives in the etiology of alcohol misuse and have implications for the application of tailored prevention and treatment strategies.
DOI: 10.6084/m9.figshare.23538745.v1
title: “The Phone is my Lifeline”: Use of Mobile Phone Technology to Support Recovery among Individuals in Treatment for Substance Use Disorders

description: <b><i>Background:</i></b> People experiencing substance use disorders (SUD) face myriad challenges in maintaining changes in substance use after treatment. Mobile phones can play a role in supporting the recovery process. To date, research has not explored how individuals use mobile phones to seek social support as they enter SUD recovery. <b><i>Objectives:</i></b> We sought to understand how individuals in SUD treatment use mobile technology in support of their recovery. <b><i>Methods:</i></b> We conducted semi-structured interviews with thirty individuals in treatment for any SUD in northeastern Georgia and southcentral Connecticut. Interviews explored participants’ attitudes toward mobile technology and how they used mobile technology while using substances, in treatment, and in recovery. Qualitative data were coded and analyzed using thematic analysis. <b><i>Results:</i></b> We identified three major themes related to how participants: (1) adapted their use of mobile technology as they entered recovery, (2) relied on mobile technology for social support while in recovery, while (3) some found aspects of mobile technology triggering. Many individuals in SUD treatment reported using mobile phones to buy or sell drugs; as such, they took measures to adapt their use of mobile technology as they changed substance use behaviors. As they entered recovery, individuals relied on mobile phones for affiliational, emotional, informational, and instrumental support, though some did share they found some aspects of mobile phones triggering. <b><i>Conclusion:</i></b> These findings highlight the importance of treatment providers engaging in conversations around mobile phone use to help individuals avoid triggers and connect with social supports. These findings uncover new opportunities for recovery support interventions utilizing mobile phones as a delivery mechanism.
DOI: 10.5281/zenodo.8148555
title: Investor Decision making and Founder Social Capital

description: Data set with analysis of choice models that best describe investor decisions based on founder human and social capital
DOI: 10.5281/zenodo.8148554
title: Investor Decision making and Founder Social Capital

description: Data set with analysis of choice models that best describe investor decisions based on founder human and social capital
DOI: 10.6084/m9.figshare.23688981
title: Spatially explicit causes of extinction of the European bison from 21,000 years before present to 1500 CE

description: We performed spatially explicit, process-explicit simulations of the range collapse of the European bison (<em>Bison bonasus</em>) from 21,000 years ago to 1500 CE, validating our models against observed patterns of fossil occurrence and population persistence. We examined the top 25 models to find the cause of extinction in each grid cell where the European bison became extinct in our simulations. These raster data provide a map of these causes of extinction at a 86 by 75 km resolution. The spatial projection of the raster is Albers equal area, with a central meridian at 25E, and standard parallels at 42.5 and 72.5N. The values in the raster are dummy variables, which correspond to the following categories: <br> 0 - Range of the European bison in 1500 CE. These cells do not become extinct in the simulation. 1 - Local extinction due to climate change. These cells became climatically unsuitable for the European bison between 21,000 years BP and 1500 CE. 2 - Local extinction due to human drivers. European bison became extinct in these grid cells due to human hunting or land use change. 3 - No occupancy. European bison never occupied these grid cells at any point in the simulation.
DOI: 10.22003/xfel.eu-data-900341-00
title: Commissioning SQS Laser
DOI: 10.5519/qd.6d54cnqy
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (18 records)
DOI: 10.17632/8dzk4s7fwf.1
title: Micronanoparticled risedronate exhibits potent vaccine adjuvant effects

description: raw data for Micronanoparticled risedronate exhibits potent vaccine adjuvant effects
DOI: 10.17632/k4x675k5dm.1
title: Data of "Deep Learning for Chaos Detection"

description: Those are files contains the raw material and the filtered data used for the training and test of the networks used in the paper "Deep Learning for Chaos Detection". 

Namely, those starting with LM or Logistic_Map corresponds to the Logistic Map and those with LS and Lorenz_System corresponds to the Lorenz System. 

The files marked with Dataset in the name are the raw data (time series and lyapunov exponents) computed as presented in the paper. And those marked with TEST, VALIDATION and TRAIN are the curated data given to the ML systems. In the case of the Lorenz System the data is already normalized as describen in the paper.

Logistic Map: All files are ordered as follows
Initial condition, parameter a, first Lyapunov exponent, time series

Lorenz System: All files are ordered as follows
Initial conditions, parameters σ; r; b, first Lyapunov exponent, time series x(t); y(t); z(t); x(t+1); y(t+1); z(t+1)
DOI: 10.17632/k4x675k5dm.2
title: Data of "Deep Learning for Chaos Detection"

description: In this folder there are files that contain the raw material and the filtered data used for the training and test of the networks used in the paper "Deep Learning for Chaos Detection".

Namely, those starting with LM or Logistic_Map corresponds to the Logistic Map and those with LS and Lorenz_System corresponds to the Lorenz System. 

The files marked with Dataset in the name are the raw data (time series and Lyapunov exponents) computed as presented in the paper. And those marked with TEST, VALIDATION and TRAIN are the curated data given to the ML systems. In the case of the Lorenz System the data is already normalized as described in the paper.

Logistic Map: All files are ordered as follows
Initial condition, parameter a, first Lyapunov exponent, time series

Lorenz System: All files are ordered as follows
Initial conditions, parameters σ; r; b, first Lyapunov exponent, time series x(t); y(t); z(t); x(t+1); y(t+1); z(t+1)
DOI: 10.17632/k4x675k5dm.3
title: Data of "Deep Learning for Chaos Detection"

description: In this folder there are files that contain the raw material and the filtered data used for the training and test of the networks used in the paper "Deep Learning for Chaos Detection".

Namely, those starting with LM or Logistic_Map corresponds to the Logistic Map and those with LS and Lorenz_System corresponds to the Lorenz System. 

The files marked with Dataset in the name are the raw data (time series and Lyapunov exponents) computed as presented in the paper. And those marked with TEST, VALIDATION and TRAIN are the curated data given to the ML systems. In the case of the Lorenz System the data is already normalized as described in the paper.

Logistic Map: All files are ordered as follows
Initial condition, parameter a, first Lyapunov exponent, time series

Lorenz System: All files are ordered as follows except LS_3D.txt
Initial conditions, parameters σ; r; b, first Lyapunov exponent, time series x(t); y(t); z(t); x(t+1); y(t+1); z(t+1)

Each line of LS_3D.txt contains parameters σ; r; b, and then 0 o 1 depending on whether it is regular or chaotic.
DOI: 10.17632/rv9kgtcjpv.1
title: Essential roles of RNA cap-proximal ribose methylation in mammalian embryonic development and fertility

description: Raw data used for preparation of figures. Western blot scans, microscopy images of histological sections.
DOI: 10.17632/khb3vf66gj.1
title: A constant level of Pol III is maintained in the nucleus by nuclear retention and degradation

description: Pol III functions in the nucleus for transcription and cytoplasm as a viral-DNA sensor, but how cells maintain the cytoplasmic-to-nuclear partitioning of Pol III activity is unknown. Here, we found that Pol III forms transcription-dependent protein clusters in the nucleus and preferentially associates with assembly factors in the cytoplasm. Acute protein degradation of Pol III subunits followed by transient protein labeling showed that nuclear and cytoplasmic Pol III response differently after the loss of their subunits. 

In this file, two parts are involved. The first part is for image of western blot and another part is for imaging. 
The HSP_inhibit result refers to Celastrol (inhibitor of HSP90) or VER-155008 (inhibitor of HSP70), the cell was treated with HSP70/HSP90 inhibitor respectively and fractionated to the cytoplasm and the nucleus (CN), six RNA polymerase III subunits were blotted and Lamin B1 as an indicator of nuclear fractions and α-Tubulin as an indicator of cytoplasmic fractions. 
RPC2_Degradation_TimeCourse result refers to auxin induced degradation of RPC2. In this folder, we generated the RPC2-degron cell line, and treated cell with auxin to induce RPC2 degradation. 
LMB_MG132 result refers to Leptomycin B(LMB) and MG132 treatment of RPC1-degron.
IP_WB result refers to immunoprecipitate of RPC1, RPC2, RPC3. In this folder, we use RPB5-degron and RPC2-degron cells. The cells were treated with auxin to induce RPB5 or RPC2 degradation, and the cytoplasm or nuclear fractions were immunoprecipitated for western blot.

The another part is Figure_Imaging, which include 5 folders.  the Raw image was captured using Andor's Dragonfly microscope with .ims type, and it could be opened by imaris (oxford instrument) or ImageJ (FiJi).
Figure2C_6h_degradation result refers Live-cell imaging. 4 cell lines include RPC1-degron, RPC2-degron, RPAC1-degron and RPB5-degron were used here. The cells were with auxin induction for 6 h, and RPC1-Halotag, RPC2-Halotag ,RPC3-Halotag were imaged. 
Figure3D_HSP_inhibitor result refers Live-cell imaging of RPC1-degron; RPC2-HaloTag (C1DC2H). the C1DC2H cell was treat with auxin, Celastrol (inhibitor of HSP90) or VER-155008 (inhibitor of HSP70), and RPC1-GFP and RPC2-Halotag was imaged.
Figure3A+3C_LMB_MG132_treatment result refers Live-cell imaging of (C1DC2H. The C1DC2H cell was treat with auxin, LMB,MG132 and RPC1-GFP and RPC2-Halotag was imaged.
FigureS1C_TF_inhibitor result refers Live-cell imaging of C1DC2H. The C1DC2H cell was treat with auxin, a-amanitin(Pol II inhibitor), ML60218(Pol III inhibitoor), Actinomycin D (pan-transcription inhibitor),  and RPC1-GFP and RPC2-Halotag was imaged.
Figure4_Folder refers Live-cell imaging of C1DC2H. The C1DC2H cell was treat with auxin for RPC1-degradation, and we removed the auxin for RPC1 rescue. During that, RPC2-HaloTag was treated with JF646 ligand to label the preexisting RPC2 (old) and with JF549 ligand to label the newly synthesized RPC2 (new).
DOI: 10.17632/khb3vf66gj.2
title: A constant level of Pol III is maintained in the nucleus by nuclear retention and degradation

description: Pol III functions in the nucleus for transcription and cytoplasm as a viral-DNA sensor, but how cells maintain the cytoplasmic-to-nuclear partitioning of Pol III activity is unknown. Here, we found that Pol III forms transcription-dependent protein clusters in the nucleus and preferentially associates with assembly factors in the cytoplasm. Acute protein degradation of Pol III subunits followed by transient protein labeling showed that nuclear and cytoplasmic Pol III response differently after the loss of their subunits. 

In this file, two parts are involved. The first part is for image of western blot and another part is for imaging. 
The HSP_inhibit result refers to Celastrol (inhibitor of HSP90) or VER-155008 (inhibitor of HSP70), the cell was treated with HSP70/HSP90 inhibitor respectively and fractionated to the cytoplasm and the nucleus (CN), six RNA polymerase III subunits were blotted and Lamin B1 as an indicator of nuclear fractions and α-Tubulin as an indicator of cytoplasmic fractions. 
RPC2_Degradation_TimeCourse result refers to auxin induced degradation of RPC2. In this folder, we generated the RPC2-degron cell line, and treated cell with auxin to induce RPC2 degradation. 
LMB_MG132 result refers to Leptomycin B(LMB) and MG132 treatment of RPC1-degron.
IP_WB result refers to immunoprecipitate of RPC1, RPC2, RPC3. In this folder, we use RPB5-degron and RPC2-degron cells. The cells were treated with auxin to induce RPB5 or RPC2 degradation, and the cytoplasm or nuclear fractions were immunoprecipitated for western blot.

The another part is Figure_Imaging, which include 5 folders.  the Raw image was captured using Andor's Dragonfly microscope with .ims type, and it could be opened by imaris (oxford instrument) or ImageJ (FiJi).
Figure2C_6h_degradation result refers Live-cell imaging. 4 cell lines include RPC1-degron, RPC2-degron, RPAC1-degron and RPB5-degron were used here. The cells were with auxin induction for 6 h, and RPC1-Halotag, RPC2-Halotag ,RPC3-Halotag were imaged. 
Figure3D_HSP_inhibitor result refers Live-cell imaging of RPC1-degron; RPC2-HaloTag (C1DC2H). the C1DC2H cell was treat with auxin, Celastrol (inhibitor of HSP90) or VER-155008 (inhibitor of HSP70), and RPC1-GFP and RPC2-Halotag was imaged.
Figure3A+3C_LMB_MG132_treatment result refers Live-cell imaging of (C1DC2H. The C1DC2H cell was treat with auxin, LMB,MG132 and RPC1-GFP and RPC2-Halotag was imaged.
FigureS1C_TF_inhibitor result refers Live-cell imaging of C1DC2H. The C1DC2H cell was treat with auxin, a-amanitin(Pol II inhibitor), ML60218(Pol III inhibitoor), Actinomycin D (pan-transcription inhibitor),  and RPC1-GFP and RPC2-Halotag was imaged.
Figure4_Folder refers Live-cell imaging of C1DC2H. The C1DC2H cell was treat with auxin for RPC1-degradation, and we removed the auxin for RPC1 rescue. During that, RPC2-HaloTag was treated with JF646 ligand to label the preexisting RPC2 (old) and with JF549 ligand to label the newly synthesized RPC2 (new).
DOI: 10.17632/khb3vf66gj.3
title: Subcellular Localization Shapes the Fate of RNA Polymerase III

description: RNA Pol III plays a vital role in transcription and as a viral-DNA sensor, but how it is assembled and distributed within cells remain poorly understood. Here, we show that Pol III is assembled with chaperones in the cytoplasm and forms transcription-dependent protein clusters upon transport into the nucleus. The largest subunit (RPC1) depletion through an auxin-inducible degron leads to rapid degradation and disassembly of Pol III complex in the nucleus and cytoplasm, respectively. This generates a pool of partially assembled Pol III intermediates, which can be rapidly mobilized into the nucleus upon the restoration of RPC1. Our study highlights the critical role of subcellular localization in determining Pol III's fate and provide insight into the dynamic regulation of nuclear Pol III levels and the origin of cytoplasmic Pol III complexes involved in mediating viral immunity.

In this file, the result of Western blot and Cell Imaging was listed as Figures shown. A brief message of this folder was characterized as below:
The mES cells were treated with Doxycycline, auxin, Leptomycin B (LMB), MG132, Celastrol (an HSP90 inhibitor), VER-155008 (an HSP70 inhibitor), α-amanitin (a Pol II inhibitor), ML60218 (a Pol III inhibitor), and Actinomycin D (a pan-transcription inhibitor) at the common used concentration and treatment time. The details can be found in the methods section of our paper.
The Raw image was captured using Andor's Dragonfly microscope with .ims type, and it could be opened by imaris (oxford instrument) or ImageJ (FiJi).

In the folder, some abbreviation were indicated as： 
TC: time course; CN: cytoplasm and nuclear fraction; LMinh: LMB and/or MG132 inhibitor treatment; BIOT: halotag biotin ligand
DOI: 10.17632/pncysh3mhm.1
title: Tarfala_BSC_picoEGU2017

description: Interactive PowerPoint presentation file (with command buttons). It shows a preliminary work carried out in the framework of the Whycrust project, funded by the Italian National Antarctic Research Program. That project includes some comparisons between BSCs from Arctic and Antarctica.
Specific aim of the study  was to set up a protocol for 3D quantitative characterization of the internal structure of BSCs of polar environment by means of X-ray MicroCT scans of undisturbed crust samples and 3D image analysis procedures. In this interactive presentation the first results regarding four crust samples of the Tarfala valley arctic area are shown. The four samples were formerly selected for their diversity in term of fungal most abundant species in order to test the hypothesis of correlation between crust structure and fungal composition (it is worth to anticipate that such a correlation was not found…)
Navigation buttons to access the sections with more detailed information are located on the vertical stripe you will see on the right. Such information includes some tables, pictures and videos regarding data and visualization of solid phase (in grey), porosity (in red) and pore size distribution (represented in color scale). In the upper left corner you find the buttons to navigate inside the sections of the presentation.
DOI: 10.17632/drdsgg4f7v.1
title: Data for: Computational Study on the Adsorption of Small Molecules to Surface-Supported Ni-Porphyrins

description: Optimized structure files of NiTPP-ligand complexes in the gas phase (B3LYP-D3), and on a passivated and pure Cu-surface (both PBE+U-D3). The xyz-files of ORCA-calculations for isolated molecules and the CONTCAR-files of VASP-calculations for extended systems are gathered in VESTA-format using the atom colour code from the manuscript.
DOI: 10.17632/d3rzx39sy4.1
title: Preliminary assessment of adjuvant activities of Glycine Max (L.) Merr saponin extract in BALB/c mice immunized with hepatitis B virus vaccine

description: Alternative hypothesis: saponins from Glycine max (L). Merr have an adjuvant activity when in BALB/c mice immunized  with Hepatitis B virus vaccine. Findings: the saponin extracts did not have significant adjuvant effect on the immune system of the BALB/c mice. Immunization with the vaccine suppressed the expression levels of IL-6 gene, while promoting the expression of the TNF gene.
DOI: 10.17632/xrp39d3mbc.1
title: ESBL phage genomes

description: A list of phage genomes in the study: A collection of diverse bacteriophages for biocontrol of ESBL- and AmpC-beta-lactamase-producing E. coli
DOI: 10.17632/3bm34bp6p4.1
title: A Deep Learning-based taphonomical approach to distinguish the modifying agent in the late Pleistocene site of Toll Cave (Barcelona, Spain)

description: Database article images: "A Deep Learning-based taphonomical approach to distinguish the modifying agent in the late Pleistocene site of Toll Cave (Barcelona, Spain)"
DOI: 10.34740/kaggle/dsv/6136342
title: Dataset for multimodal transport analytics
DOI: 10.6084/m9.figshare.3761562.v2646
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.25345/c5z60cc2q
title: MassIVE MSV000092448 - Shotgun characterization of the circulating IgM-antigenome of an infectious pathogen by immunocapture-LC-MS/MS from dried serum spots
title: P. falciparum antigens recognized by human IgM of malaria-infected and non-infected individuals. Residual human proteins from immunoprecipitation are also present.
DOI: 10.34740/kaggle/ds/2885963
title: Dataset for multimodal transport analytics
DOI: 10.5519/qd.nbca1is7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5589787 records)
DOI: 10.5281/zenodo.8148645
title: Flow velocity from Bonnet Carre Spillway (Mississippi River freshwater diversion into Lake Pontchartrain) on April 16, 2008

description: <strong>Title: </strong>Flow velocity from Bonnet Carre Spillway (Mississippi River freshwater diversion into Lake Pontchartrain) on April 16, 2008 <strong>Author: </strong>Li, Chunyan <strong>Contact/PI: </strong>Li, C. (cli@lsu.edu) <strong>Description:</strong> The data provided here are measured flow velocity profiles across the Bonnet Carre Spillway which is a lower Mississippi River freshwater diversion connected to Lake Pontchartrain. The survey was done on April 16, 2008. Time is UTC. There are 11 files for current profiles from an ADCP mounted on a small boat running across the spillway inside Lake Pontchartrain. The instrument is a 1200 kHz RDI ADCP. All these files are ASCII files. These files are output from the RDI’s program WinRiver II. The file names are: LakePontBonnetCarre000_ASC.TXT LakePontBonnetCarre001_ASC.TXT LakePontBonnetCarre002_ASC.TXT LakePontBonnetCarre003_ASC.TXT LakePontBonnetCarre004_ASC.TXT LakePontBonnetCarre005_ASC.TXT LakePontBonnetCarre006_ASC.TXT LakePontBonnetCarre007_ASC.TXT LakePontBonnetCarre008_ASC.TXT LakePontBonnetCarre009_ASC.TXT LakePontBonnetCarre010_ASC.TXT
DOI: 10.5281/zenodo.8148644
title: Flow velocity from Bonnet Carre Spillway (Mississippi River freshwater diversion into Lake Pontchartrain) on April 16, 2008

description: <strong>Title: </strong>Flow velocity from Bonnet Carre Spillway (Mississippi River freshwater diversion into Lake Pontchartrain) on April 16, 2008 <strong>Author: </strong>Li, Chunyan <strong>Contact/PI: </strong>Li, C. (cli@lsu.edu) <strong>Description:</strong> The data provided here are measured flow velocity profiles across the Bonnet Carre Spillway which is a lower Mississippi River freshwater diversion connected to Lake Pontchartrain. The survey was done on April 16, 2008. Time is UTC. There are 11 files for current profiles from an ADCP mounted on a small boat running across the spillway inside Lake Pontchartrain. The instrument is a 1200 kHz RDI ADCP. All these files are ASCII files. These files are output from the RDI’s program WinRiver II. The file names are: LakePontBonnetCarre000_ASC.TXT LakePontBonnetCarre001_ASC.TXT LakePontBonnetCarre002_ASC.TXT LakePontBonnetCarre003_ASC.TXT LakePontBonnetCarre004_ASC.TXT LakePontBonnetCarre005_ASC.TXT LakePontBonnetCarre006_ASC.TXT LakePontBonnetCarre007_ASC.TXT LakePontBonnetCarre008_ASC.TXT LakePontBonnetCarre009_ASC.TXT LakePontBonnetCarre010_ASC.TXT