cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10286903
title: Mechanical alphabet, TM1981 (Low res.)

description: Christopher Polhem (1661-1751) created the so-called mechanical alphabet in the years just before 1700. The wooden models illustrated the fundamentals of physics and mechanics and were intended to serve as an educational tool in teachings at the Laboratorium mechanicum established in 1697. After his death, the collection continued to serve as a didactic instrument and became part of the Swedish Royal Model Chamber. In 1925, the collection of 31 models was deposited at the newly established National Museum of Science and Technology. 

[TM1981](https://digitaltmuseum.se/021026544982/mekaniskt-alfabet-modell?aq=text%3A%22polhem%22+owner%3A%22S-TEK%22&i=23"tm1981") (TEKS0021248)

Created in RealityCapture by Fredrik Olsson from 363 images in 02h:08m:42s. 
Please note: No post processing have been done, holes may occur in mesh.

Read more about [Polhem]( https://en.wikipedia.org/wiki/Christopher_Polhem"polhem") on Wikipedia.

Source: Objaverse 1.0 / Sketchfab
DOI: 10.5281/zenodo.3948745
title: ODP Leg 204, Hole 1251H - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 204: Drilling Gas Hydrates on Hydrate Ridge, Cascadia Continental Margin
DOI: 10.5281/zenodo.3948744
title: ODP Leg 204, Hole 1251H - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 204: Drilling Gas Hydrates on Hydrate Ridge, Cascadia Continental Margin
DOI: 10.5281/zenodo.3941829
title: IODP Expedition 310, Hole M0021B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 310: Tahiti Sea Level
DOI: 10.5281/zenodo.3941828
title: IODP Expedition 310, Hole M0021B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 310: Tahiti Sea Level
DOI: 10.6084/m9.figshare.21096169
title: Studies of Muscle's Electron Microscopy

description: Studies of Muscle's Electron Microscopy
DOI: 10.6084/m9.figshare.21096169.v1
title: Studies of Muscle's Electron Microscopy

description: Studies of Muscle's Electron Microscopy
DOI: 10.5281/zenodo.3941831
title: IODP Expedition 310, Hole M0023B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 310: Tahiti Sea Level
DOI: 10.5281/zenodo.3941830
title: IODP Expedition 310, Hole M0023B - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 310: Tahiti Sea Level
DOI: 10.25921/7w2g-4m06
title: ES60 Water Column Sonar Data Collected During AI10OE
DOI: 10.5281/zenodo.3939592
title: IODP Expedition 336, Hole U1383C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 336: Mid-Atlantic Ridge Microbiology
DOI: 10.5281/zenodo.3939591
title: IODP Expedition 336, Hole U1383C - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 336: Mid-Atlantic Ridge Microbiology
DOI: 10.5281/zenodo.7076334
title: Romaine-2_dataset

description: These data were used for the paper entitled "Characteristic Time Scales of Reservoir Evaporation in a Subarctic Climate" and submitted to Hydrological Processes on the 2022-09-13. Table has the following variables : "Temps" : Time "T_air (°C)" : air temperature "q (kg/kg)" : specific humidity "Patmo (Pa)" : atmospheric pressure "e (Pa)" : pressure "es (Pa)" : pressure at saturation "Zeta (-)" : stability "WS (m/s)" : wind speed "H_gf (W/m2)" : sensible heat flux "LE_gf (W/m2)" : latent heat flux "R_net (W/m2)" : net radiation "Tw (°C)" : water temperature "Hs_70m (kJ/m2)" : heat storage
DOI: 10.5281/zenodo.7076333
title: Romaine-2_dataset

description: These data were used for the paper entitled "Characteristic Time Scales of Reservoir Evaporation in a Subarctic Climate" and submitted to Hydrological Processes on the 2022-09-13. Table has the following variables : "Temps" : Time "T_air (°C)" : air temperature "q (kg/kg)" : specific humidity "Patmo (Pa)" : atmospheric pressure "e (Pa)" : pressure "es (Pa)" : pressure at saturation "Zeta (-)" : stability "WS (m/s)" : wind speed "H_gf (W/m2)" : sensible heat flux "LE_gf (W/m2)" : latent heat flux "R_net (W/m2)" : net radiation "Tw (°C)" : water temperature "Hs_70m (kJ/m2)" : heat storage
DOI: 10.5281/zenodo.7076361
title: LANL data for the 2008-01-19 event

description: The electron and ion fluxes of LANL spacecraft on 2008-01-19
DOI: 10.5281/zenodo.7076360
title: LANL data for the 2008-01-19 event

description: The electron and ion fluxes of LANL spacecraft on 2008-01-19
DOI: 10.5281/zenodo.7036198
title: CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy

description: This is the models as well as results for CLTS-GAN, a deep learning model that disentangles color and lighting and texture and specular information. The results for the model as well as an augmented polyp dataset are contained here. <strong>Abstract:</strong> Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. You can find the code and additional detail about CLTS-GAN via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP
DOI: 10.5281/zenodo.7036197
title: CLTS-GAN: Color-Lighting-Texture-Specular Reflection Augmentation for Colonoscopy

description: This is the models as well as results for CLTS-GAN, a deep learning model that disentangles color and lighting and texture and specular information. The results for the model as well as an augmented polyp dataset are contained here. <strong>Abstract:</strong> Automated analysis of optical colonoscopy (OC) video frames (to assist endoscopists during OC) is challenging due to variations in color, lighting, texture, and specular reflections. Previous methods either remove some of these variations via preprocessing (making pipelines cumbersome) or add diverse training data with annotations (but expensive and time-consuming). We present CLTS-GAN, a new deep learning model that gives fine control over color, lighting, texture, and specular reflection synthesis for OC video frames. We show that adding these colonoscopy-specific augmentations to the training data can improve state-of-the-art polyp detection/segmentation methods as well as drive next generation of OC simulators for training medical students. You can find the code and additional detail about CLTS-GAN via our Computation Endoscopy Platform at https://github.com/nadeemlab/CEP
DOI: 10.6084/m9.figshare.21094999
title: heliamphora_exappendiculata_decontaminated_FSCR.fasta.gz

description: ........
DOI: 10.6084/m9.figshare.21094999.v1
title: heliamphora_exappendiculata_decontaminated_FSCR.fasta.gz

description: ........
DOI: 10.3886/icpsr38050.v1
title: Kickstarter Data, Global, 2009-2020
title: Version 1

description: Launched on April 28, 2009, Kickstarter is a Public Benefit Corporation based in Brooklyn, New York. It is a global crowdfunding platform that helps to fund new creative projects and ideas through direct support from individuals (backers) from around the world who pledge money to bring these projects and ideas to life. Kickstarter supports many different kinds of projects. Everything from films, games, and music to art, design, and technology. Funding on Kickstarter is based on the all-or-nothing model. Backers who pledge their support towards a particular project won't be charged unless the funding goal has been reached. Successfully funded projects reward their backers with one-of-a-kind experiences, e.g., limited editions, or copies of the creative work being produced. The study includes two datasets: 1) Kickstarter Project dataset and 2) Backer Location dataset. The Kickstarter Project dataset contains detailed information about all Kickstarter projects starting in 2009 through 2020, including the project title, project category and subcategory, project location (city, state (for U.S. based projects), and country), funding goal in original and U.S. currencies, pledged amount (in dollars), number of backers for each project, etc. The Kickstarter Project dataset includes data on 506,199 successful and unsuccessful funded projects. The Backer Location dataset includes information about backers' country and state, and the total amount pledged for each geographic location.
description: Kickstarter Project Public-Use Data (DS1): This file includes 506199 cases and 22 variables about projects on Kickstarter including: project category, location, launch and end dates, the currency used to fund the project, project goals and amount raised in the original currency as well as USD, number of backers, and project status. Kickstarter Project Restricted-Use Data (DS2): This file includes 506199 cases and 22 variables about projects on Kickstarter including: project name, summary, category, location, launch and end dates, the currency used to fund the project, project goals and amount raised in the original currency as well as USD, number of backers, project status, and the project URL. Backer Location (DS3):This file contains 1048575 cases and 6 variables about the demographics of people that donated to Kickstarter projects, including: location, number of backers, and total amount donated in USD.
description: Datasets: DS0: Study-Level Files DS1: Kickstarter Project Public-Use Data DS2: Kickstarter Project Restricted-Use Data DS3: Backer Location
description: Creative projects that seek crowdfunding from backers through Kickstarter. Smallest Geographic Unit: City
description: record abstractsUsers should note that Kickstarter Project Restricted-Use Data (DS2) is not currently available for download and dissemination and will be added to the collection in a future update.
DOI: 10.6084/m9.figshare.21094618
title: patientNotNorthBrazil_subset.vcf

description: Variant Calling Format (VCF) files associated with the publication entitled: "<strong>Selective whole-genome amplification reveals population genetics of Leishmania braziliensis directly from patient skin biopsies"</strong>
DOI: 10.6084/m9.figshare.21094618.v1
title: patientNotNorthBrazil_subset.vcf

description: Variant Calling Format (VCF) files associated with the publication entitled: "<strong>Selective whole-genome amplification reveals population genetics of Leishmania braziliensis directly from patient skin biopsies"</strong>
DOI: 10.6084/m9.figshare.21096919
title: Supplementary Material

description: Table S1 and Table S2.
DOI: 10.17608/k6.auckland.21096898
title: Spatial surface describing the probability of forest loss in prehistoric New Zealand

description: This is a spatial surface (.asc raster layer) with the predicted mean probability of forest loss during New Zealand's prehistoric period. The method used to produce the surface is described in detail by Perry et al. (2012; Global Ecol Biogeog 21, 1029-1041). The layer is projected as NZTM 2000 at a 1-km resolution.
DOI: 10.5517/ccdc.csd.cc27k1lg
title: CCDC 2071965: Experimental Crystal Structure Determination

description: Related Article: Ziyang Jia, Zongsu Han, Kunyu Wang, Tianze Zhou, Hui Min, Tiankai Sun, Yunteng Liao, Liang Wang, Peng Cheng, Wei Shi|2022|Inorg.Chem.|61|14313|doi:10.1021/acs.inorgchem.2c01984
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27k2lh
title: CCDC 2071996: Experimental Crystal Structure Determination

description: Related Article: Ziyang Jia, Zongsu Han, Kunyu Wang, Tianze Zhou, Hui Min, Tiankai Sun, Yunteng Liao, Liang Wang, Peng Cheng, Wei Shi|2022|Inorg.Chem.|61|14313|doi:10.1021/acs.inorgchem.2c01984
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27nrzn
title: CCDC 2075542: Experimental Crystal Structure Determination

description: Related Article: Ziyang Jia, Zongsu Han, Kunyu Wang, Tianze Zhou, Hui Min, Tiankai Sun, Yunteng Liao, Liang Wang, Peng Cheng, Wei Shi|2022|Inorg.Chem.|61|14313|doi:10.1021/acs.inorgchem.2c01984
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.48324/dandi.000147/0.220913.2243
title: PPC_Finger: human posterior parietal cortex recordings during attempted finger movements

description: This dataset contains neural activity and trial information from a tetraplegic human participant performing a brain-computer interface (BCI) finger press task. Neural activity was recorded from a 96-channel Utah array (Blackrock) implanted in the left posterior parietal cortex (PPC) at the junction of the postcentral and intraparietal sulci (PC-IP). On each trial, a finger cue, at a pseudorandom location on a screen, was selected. The participant immediately looked at the cue and pressed the cued finger until decoder feedback was shown (1.5 seconds after the cue).

This dataset includes sorted unit spiking times, finger cue, in-session classified finger, cue location, and trial timing.
DOI: 10.5281/zenodo.6558337
title: The gut microbiome of wild American marten in the Upper Peninsula of Michigan

description: Directory Information for The gut microbiome of wild American marten in the Upper Peninsula of Michigan<br> ####### "R Code" contains: --- "marten_phyobj.rds" is the phyloseq object that can be directly imported for statistical analysis if the user prefers not to go entire QIIME2 pipeline. The phyloseq object was created from QIIME2 artifacts from the "QIIMEpipe.html" pipeline: the cleaned rooted tree, the cleaned taxonomy table and the cleaned ASV table. <strong>This requires the command "readRDS()" to import. <em>The "load()" command will not work.</em></strong> <br> --- "Stat.Rmd" Markdown file for "Stat.html" --- "Stat.html" knitted statistical analysis file to view the studies outputs quickly --- "Stat.R" R code if user prefers over Rmarkdown ####### "QIIME" contains:<br> --- "martendemux.qza" demultiplexed QIIME2 artifact --- "martendemux.qzv" visualization output of demultiplexed sequence that can be viewed at qiimeview.org --- "MartenMeta.tsv" metadata file for QIIME2 pipeline and statistical analysis --- "QIIMepipe.html" code for bioinformatic pipeline for downstream analysis - "Sequences" folder:<br> --- "R1_demultiplxed_pairedend_marten.fastq.gz" forward reads of demultiplexed, EMP paired end sequences (Illumina Miseq) if the user prefers to use another bioinformatic platform besides QIIME2. --- "R2_demultiplxed_pairedend_marten.fastq.gz" reverse reads of demultiplexed, EMP paired end sequences (Illumina Miseq) if the user prefers to use another bioinformatic platform besides QIIME2.<br>
DOI: 10.5281/zenodo.6558336
title: The gut microbiome of wild American marten in the Upper Peninsula of Michigan

description: Directory Information for The gut microbiome of wild American marten in the Upper Peninsula of Michigan<br> ####### "R Code" contains: --- "marten_phyobj.rds" is the phyloseq object that can be directly imported for statistical analysis if the user prefers not to go entire QIIME2 pipeline. The phyloseq object was created from QIIME2 artifacts from the "QIIMEpipe.html" pipeline: the cleaned rooted tree, the cleaned taxonomy table and the cleaned ASV table. <strong>This requires the command "readRDS()" to import. <em>The "load()" command will not work.</em></strong> <br> --- "Stat.Rmd" Markdown file for "Stat.html" --- "Stat.html" knitted statistical analysis file to view the studies outputs quickly --- "Stat.R" R code if user prefers over Rmarkdown ####### "QIIME" contains:<br> --- "martendemux.qza" demultiplexed QIIME2 artifact --- "martendemux.qzv" visualization output of demultiplexed sequence that can be viewed at qiimeview.org --- "MartenMeta.tsv" metadata file for QIIME2 pipeline and statistical analysis --- "QIIMepipe.html" code for bioinformatic pipeline for downstream analysis - "Sequences" folder:<br> --- "R1_demultiplxed_pairedend_marten.fastq.gz" forward reads of demultiplexed, EMP paired end sequences (Illumina Miseq) if the user prefers to use another bioinformatic platform besides QIIME2. --- "R2_demultiplxed_pairedend_marten.fastq.gz" reverse reads of demultiplexed, EMP paired end sequences (Illumina Miseq) if the user prefers to use another bioinformatic platform besides QIIME2.<br>
DOI: 10.6084/m9.figshare.21097213
title: Dataset of Incomplete Immunization Status of Health Workers' Children at a Tertiary Referral Hospital in Surabaya.

description: The dataset of the incomplete immunization status of health workers' children consists of participants' age, children's age, socio-demographic data, the parents' knowledge regarding the immunization status of their children, and other variables studied.
DOI: 10.6084/m9.figshare.21097207
title: Tabla de extracción.docx

description: Tabla extraccin
DOI: 10.6084/m9.figshare.21097207.v1
title: Tabla de extracción.docx

description: Tabla extraccin
DOI: 10.25921/7bet-w394
title: ES60 Water Column Sonar Data Collected During AI10SS
DOI: 10.6084/m9.figshare.21097303
title: Manifest.16S.txt

description: manifest for 16S
DOI: 10.6084/m9.figshare.21097303.v1
title: Manifest.16S.txt

description: manifest for 16S
DOI: 10.3886/e127401v1-127040
title: Data and code for: The Selection of Talent: Experimental and Structural Evidence from Ethiopia

description: We study how search frictions in the labour market affect firms' ability to recruit talented workers. In a field experiment in Ethiopia, we show that an employer can attract more talented applicants by offering a small monetary incentive for making a job application. Estimates from a structural model suggest that the intervention is effective because the cost of making a job application is large, and positively correlated with jobseeker ability. We provide evidence that this positive correlation is driven by dynamic selection. In a second experiment, we show that local recruiters underestimate the positive impacts of application incentives.
DOI: 10.25921/aadc-k307
title: ES60 Water Column Sonar Data Collected During GOA09PE
DOI: 10.5517/ccdc.csd.cc2btxmq
title: CCDC 2169864: Experimental Crystal Structure Determination

description: Related Article: Claire Tonnelé, Manon Catherin, Michel Giorgi, Gabriel Canard, David Casanova, Frédéric Castet, Elena Zaborova, Frédéric Fages|2022|Dyes Pigm.|207|110677|doi:10.1016/j.dyepig.2022.110677
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21097474
title: graphium_colonna_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21097474.v1
title: graphium_colonna_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21097597
title: papilio_bianor_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.21097597.v1
title: papilio_bianor_zanfona_1.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.20621664.v2
title: The ability of Anopheles funestus and An. arabiensis to penetrate LLINs and its effect on their mortality

description: The deposited data are recording on the observed number of different mosquito penetrating different types of new nets (Safi Net; DawaPlus Net, Olyset Net, and PermaNet). The data also recorded the mosquito feeding success on the bednet occupant/sleeping volunteer, and its survival as results on insecticides on the net 24 to 48 hours post monitoring. The observation was conducted under the semi-field settings using two species of mosquitoes Anopheles arabiensis (the laboratory reared strain) and Anopheles funestus (F1 of the fresh field collected mosquitoes). All data were analysed in R v3.1-131.