cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3933039
title: RGB-W Dataset

description: <strong>Abstract</strong> Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional quasi-free modality, namely, the wireless signal emitted by individuals' cell phones, referred to as RGB-W. The received signal strength acts as a rough proxy for depth and a reliable cue on a person's identity. Although the measured signals are noisy, we demonstrate that the combination of visual and wireless data significantly improves the localization accuracy. We introduce a novel image-driven representation of wireless data which embeds all received signals onto a single image. We then evaluate the ability of this additional data to (i) locate persons within a sparsity-driven framework and to (ii) track individuals with a new confidence measure on the data association problem. Our solution outperforms existing localization methods. It can be applied to the millions of currently installed RGB cameras to better analyze human behavior and offer the next generation of high-accuracy location-based services. <strong>Conference Paper</strong> PDF: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Alahi_RGB-W_When_Vision_ICCV_2015_paper.pdf <strong>Metadata</strong> <pre><code>+----------------+-----------------+-----------+-----------+--------------+----------+ | Sequence Name | Length (mm:ss) | # Frames | # People | # W Devices | Download | +----------------+-----------------+-----------+-----------+--------------+----------+ | conference-1 | 01:53 | 1,697 | 5 | 5 | 116 MiB | | conference-2 | 05:18 | 4,782 | 12 | 12 | 379 MiB | | conference-3 | 23:31 | 21,165 | 1 | 2 | 1.3 GiB | | conference-4 | 06:27 | 4,832 | 1 | 2 | 357 MiB | | conference-5 | 06:03 | 4,525 | 2 | 2 | 290 MiB | | patio-1 | 07:22 | 6,636 | 4 | 4 | 474 MiB | | patio-2 | 04:36 | 4,144 | 2 | 2 | 258 MiB | | Full Dataset | 55:10 | 47,781 | -- | -- | 3.2 GiB | +----------------+-----------------+-----------+-----------+--------------+----------+</code></pre> <strong>Citation</strong> If you would like to cite our work, please use the following. <strong>Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. International Conference on Computer Vision (ICCV). Santiago, Chile. IEEE.</strong> <pre>@inproceedings{alahi2015rgb, title={RGB-W: When vision meets wireless}, author={Alahi, Alexandre and Haque, Albert and Fei-Fei, Li}, booktitle={International Conference on Computer Vision}, year={2015} }</pre>
description: {"references": ["Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. International Conference on Computer Vision."]}
DOI: 10.5281/zenodo.3933038
title: RGB-W Dataset

description: <strong>Abstract</strong> Inspired by the recent success of RGB-D cameras, we propose the enrichment of RGB data with an additional quasi-free modality, namely, the wireless signal emitted by individuals' cell phones, referred to as RGB-W. The received signal strength acts as a rough proxy for depth and a reliable cue on a person's identity. Although the measured signals are noisy, we demonstrate that the combination of visual and wireless data significantly improves the localization accuracy. We introduce a novel image-driven representation of wireless data which embeds all received signals onto a single image. We then evaluate the ability of this additional data to (i) locate persons within a sparsity-driven framework and to (ii) track individuals with a new confidence measure on the data association problem. Our solution outperforms existing localization methods. It can be applied to the millions of currently installed RGB cameras to better analyze human behavior and offer the next generation of high-accuracy location-based services. <strong>Conference Paper</strong> PDF: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Alahi_RGB-W_When_Vision_ICCV_2015_paper.pdf <strong>Metadata</strong> <pre><code>+----------------+-----------------+-----------+-----------+--------------+----------+ | Sequence Name | Length (mm:ss) | # Frames | # People | # W Devices | Download | +----------------+-----------------+-----------+-----------+--------------+----------+ | conference-1 | 01:53 | 1,697 | 5 | 5 | 116 MiB | | conference-2 | 05:18 | 4,782 | 12 | 12 | 379 MiB | | conference-3 | 23:31 | 21,165 | 1 | 2 | 1.3 GiB | | conference-4 | 06:27 | 4,832 | 1 | 2 | 357 MiB | | conference-5 | 06:03 | 4,525 | 2 | 2 | 290 MiB | | patio-1 | 07:22 | 6,636 | 4 | 4 | 474 MiB | | patio-2 | 04:36 | 4,144 | 2 | 2 | 258 MiB | | Full Dataset | 55:10 | 47,781 | -- | -- | 3.2 GiB | +----------------+-----------------+-----------+-----------+--------------+----------+</code></pre> <strong>Citation</strong> If you would like to cite our work, please use the following. <strong>Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. International Conference on Computer Vision (ICCV). Santiago, Chile. IEEE.</strong> <pre>@inproceedings{alahi2015rgb, title={RGB-W: When vision meets wireless}, author={Alahi, Alexandre and Haque, Albert and Fei-Fei, Li}, booktitle={International Conference on Computer Vision}, year={2015} }</pre>
description: {"references": ["Alahi A, Haque A, Fei-Fei L. (2015). RGB-W: When Vision Meets Wireless. International Conference on Computer Vision."]}
DOI: 10.5281/zenodo.3908737
title: A framework for pore-scale simulation of broadband electrical conductivity and permittivity of porous media

description: The data presented in a manuscript entitled "A framework for pore-scale simulation of electrical conductivity and permittivity of porous media in the frequency range from mHz to GHz" to be submitted to Journal of Geophysical Research: Solid Earth.
DOI: 10.5281/zenodo.3908736
title: A framework for pore-scale simulation of broadband electrical conductivity and permittivity of porous media

description: The data presented in a manuscript entitled "A framework for pore-scale simulation of electrical conductivity and permittivity of porous media in the frequency range from mHz to GHz" to be submitted to Journal of Geophysical Research: Solid Earth.
DOI: 10.5281/zenodo.3934643
title: A K-means Clustering Analysis of the Jovian and Terrestrial Magnetopauses

description: Jovian magnetopause crossings used in the referenced paper and a README file
DOI: 10.5281/zenodo.3758650
title: A K-means Clustering Analysis of the Jovian and Terrestrial Magnetopauses

description: Jovian magnetopause crossings used in the referenced paper and a README file
DOI: 10.7303/syn22240938
title: PARPi resistance
DOI: 10.5281/zenodo.3934682
title: Industrial, CBD, and feral hemp: how different are their essential oil profile and antimicrobial activity?

description: These are supplemental tables for the manuscript to be submitted to Molecules. These include:
description: These are the supplemental tables for the manuscript with the identical title submitted to the journal Molecules
DOI: 10.5281/zenodo.3934681
title: Industrial, CBD, and feral hemp: how different are their essential oil profile and antimicrobial activity?

description: These are supplemental tables for the manuscript to be submitted to Molecules. These include:
description: These are the supplemental tables for the manuscript with the identical title submitted to the journal Molecules
DOI: 10.5281/zenodo.3932230
title: Genetic basis of offspring number and body weight in Drosophila melanogaster

description: Data and analysis scripts for the manuscript entitled "Genetic basis of offspring number and body weight in <em>Drosophila melanogaster</em>" by Jamilla Akhund-Zade et al.
DOI: 10.5066/p91za4j4
title: FLOwPER Database: StreamFLOw PERmanence field observations, August 2019 - October 2019

description: IMPORTANT NOTE: More recent versions of these data release are available at this link. This dataset includes spatial locations where streamflow permanence observations (continuous flow, discontinuous flow, and dry) were recorded using the FLOwPER (FLOw PERmanence) field survey available in the Survey 123 and S1 mobile application. Additional information to describe the field conditions are included as part of the survey. Field observations in the FLOwPER Database have not been processed for quality control including spatial data accuracy or association with a stream network such as the National Hydrography Dataset. Streamflow permanence observations are collected from several governmental and non-governmental organizations on a continuing basis. This data release is formatted as a shapefile that includes streamflow permanence observations with associated information. Photographs associated with FLOwPER data points are included.
DOI: 10.5281/zenodo.3934822
title: Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)

description: Datasets for the Geoscientific Model Development publication: "Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)" Abstract: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958-2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~1º) to eddy-resolving (~0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the 20 extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are 25 significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve’’ most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.
DOI: 10.5281/zenodo.3685917
title: Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)

description: Datasets for the Geoscientific Model Development publication: "Impact of horizontal resolution on global ocean-sea-ice model simulations based on the experimental protocols of the Ocean Model Intercomparison Project phase 2 (OMIP-2)" Abstract: This paper presents global comparisons of fundamental global climate variables from a suite of four pairs of matched low- and high-resolution ocean and sea-ice simulations that are obtained following the OMIP-2 protocol (Griffies et al., 2016) and integrated for one cycle (1958-2018) of the JRA55-do atmospheric state and runoff dataset (Tsujino et al., 2018). Our goal is to assess the robustness of climate-relevant improvements in ocean simulations (mean and variability) associated with moving from coarse (~1º) to eddy-resolving (~0.1º) horizontal resolutions. The models are diverse in their numerics and parameterizations, but each low-resolution and high-resolution pair of models is matched so as to isolate, to the 20 extent possible, the effects of horizontal resolution. A variety of observational datasets are used to assess the fidelity of simulated temperature and salinity, sea surface height, kinetic energy, heat and volume transports, and sea ice distribution. This paper provides a crucial benchmark for future studies comparing and improving different schemes in any of the models used in this study or similar ones. The biases in the low-resolution simulations are familiar and their gross features – position, strength, and variability of western boundary currents, equatorial currents, and Antarctic Circumpolar Current – are 25 significantly improved in the high-resolution models. However, despite the fact that the high-resolution models “resolve’’ most of these features, the improvements in temperature or salinity are inconsistent among the different model families and some regions show increased bias over their low-resolution counterparts. Greatly enhanced horizontal resolution does not deliver unambiguous bias improvement in all regions for all models.
DOI: 10.11577/1616146
title: TBD

description: Please check the README.txt file from the supplementary files for more information about the dataset.
DOI: 10.15152/qdb.234
title: QDB archive #234

description: This QDB archive contains data and model(s) from the following publication: Garcia-Sosa, A. T.; Maran, U. Using combined docking, chemical fingerprints, and Bayesian classifiers for the androgen receptor binding activity of the CoMPARA data. Toxicological Sciences 2020.
DOI: 10.7910/dvn/auyerv/0kobff
title: 1. downloader.py

description: :unav
DOI: 10.7910/dvn/auyerv/wfh5ut
title: 2. errorlist_redownloader.py

description: :unav
DOI: 10.7910/dvn/auyerv/rkqx4y
title: 3. parser.py

description: :unav
DOI: 10.7910/dvn/auyerv/zvh507
title: 4. basicinfo.do

description: :unav
DOI: 10.7910/dvn/auyerv/kcgjui
title: 5. assignee.do

description: :unav
DOI: 10.7910/dvn/auyerv/uwnyo8
title: 6. invt_loc.do

description: :unav
DOI: 10.7910/dvn/auyerv/sqy75l
title: readme.txt

description: :unav
DOI: 10.7910/dvn/auyerv/7lh6gb
title: 1. downloader.py

description: :unav
DOI: 10.7910/dvn/auyerv/2ngfhm
title: 2. errorlist_redownloader.py

description: :unav
DOI: 10.7910/dvn/auyerv/gxcn7i
title: 3. parser.py

description: :unav
DOI: 10.7910/dvn/auyerv/5bwlos
title: 1. numbers.py

description: :unav
DOI: 10.7910/dvn/auyerv/qgufui
title: 2. numbers_dulicates.do

description: :unav
DOI: 10.7910/dvn/auyerv/rxjcyn
title: 3. citation_byappnumber.do

description: :unav
DOI: 10.7910/dvn/auyerv/vtko6d
title: readme.txt

description: :unav
DOI: 10.7910/dvn/auyerv/d2fekk
title: 1. downloader.py

description: :unav
DOI: 10.7910/dvn/auyerv/lhohsf
title: 2. parser.py

description: :unav
DOI: 10.7910/dvn/auyerv/nq0tgc
title: readme.txt

description: :unav
DOI: 10.7910/dvn/auyerv/uinx0c
title: 1. downloader.py

description: :unav
DOI: 10.7910/dvn/auyerv/tli4yy
title: 2. errorlist_redownloader.py

description: :unav
DOI: 10.7910/dvn/auyerv/g0inwv
title: 3. parser.py

description: :unav
DOI: 10.7910/dvn/auyerv/yydcj6
title: readme.txt

description: :unav
DOI: 10.7910/dvn/auyerv/uwqk4f
title: 1. basic.py

description: :unav
DOI: 10.7910/dvn/auyerv/ymoiji
title: 2. assg_location.py

description: :unav
DOI: 10.7910/dvn/auyerv/vvcpwv
title: 3. cite.py

description: :unav
DOI: 10.7910/dvn/auyerv/zyptcu
title: 4. invent.py

description: :unav
DOI: 10.7910/dvn/auyerv/dheaxt
title: 02-04.py

description: :unav
DOI: 10.7910/dvn/auyerv/2z7t5y
title: 06-12.py

description: :unav
DOI: 10.7910/dvn/auyerv/jn42fi
title: 13-14.py

description: :unav
DOI: 10.7910/dvn/auyerv/r4xmig
title: 15.py

description: :unav
DOI: 10.7910/dvn/auyerv/neligp
title: 16-17.py

description: :unav
DOI: 10.7910/dvn/auyerv/wtqwb8
title: 76-01.py

description: :unav
DOI: 10.7910/dvn/auyerv/t3txwr
title: 6. csv2dta_sample.do

description: :unav
DOI: 10.7910/dvn/auyerv/lfisrx
title: readme.txt

description: :unav
DOI: 10.7910/dvn/auyerv/saspbw
title: combabbrev.do

description: :unav
DOI: 10.7910/dvn/auyerv/jylkyu
title: corpentities.do

description: :unav
DOI: 10.7910/dvn/auyerv/vczgjv
title: corporates.do

description: :unav
DOI: 10.7910/dvn/auyerv/n7b1z4
title: derwent_standardisation_BHH.do

description: :unav
DOI: 10.7910/dvn/auyerv/xfe3xq
title: main_coname2.do

description: :unav
DOI: 10.7910/dvn/auyerv/h9xxpj
title: nameonly_main.do

description: :unav
DOI: 10.7910/dvn/auyerv/f4iupy
title: non_corporates.do

description: :unav
DOI: 10.7910/dvn/auyerv/4zxoxp
title: punctuation2.do

description: :unav
DOI: 10.7910/dvn/auyerv/fi17hy
title: standard_name.do

description: :unav
DOI: 10.7910/dvn/auyerv/ls2r1s
title: stem_name.do

description: :unav
DOI: 10.7910/dvn/auyerv/57ngpm
title: uspto_code.do

description: :unav
DOI: 10.7910/dvn/auyerv/zlgmen
title: 1. dataguide.tab

description: :unav
DOI: 10.7910/dvn/auyerv/lq0lcb
title: 2. matching corp_num to KIPRIS ID.py

description: :unav
DOI: 10.7910/dvn/auyerv/ncew6y
title: 3. dataguide_cleaning.do

description: :unav
DOI: 10.7910/dvn/auyerv/mncovf
title: 4. KIPRIS_cleaning.do

description: :unav
DOI: 10.7910/dvn/auyerv/e2wk9k
title: 5. corpnum_cleaned.do

description: :unav
DOI: 10.7910/dvn/auyerv/lalkkk
title: 1. family_makebasic.do

description: :unav
DOI: 10.7910/dvn/auyerv/gsyjed
title: 2. family.py

description: :unav
DOI: 10.7910/dvn/auyerv/p6vuwa
title: Matching_dg2k.py

description: :unav
DOI: 10.7910/dvn/auyerv/nvradz
title: Matching_k2dg.py

description: :unav
DOI: 10.7910/dvn/auyerv/gtlsog
title: Matching_uspto.py

description: :unav
DOI: 10.7910/dvn/auyerv/6py17d
title: 1. uspto_matches.do

description: :unav
DOI: 10.7910/dvn/auyerv/4om8xt
title: 2. all_matches.do

description: :unav
DOI: 10.7910/dvn/auyerv/eoghae
title: readme.txt

description: :unav
DOI: 10.7910/dvn/auyerv/pxt8pg
title: industry.tab

description: :unav
DOI: 10.7910/dvn/auyerv/kjghmt
title: matching_table.tab

description: :unav
DOI: 10.7910/dvn/auyerv/wdtizd
title: assignee_uspto_all.csv

description: :unav
DOI: 10.7910/dvn/auyerv/jbqjgz
title: assignee_uspto_kr.tab

description: :unav
DOI: 10.7910/dvn/auyerv/vumblv
title: basicinfo_uspto_all.tab

description: :unav
DOI: 10.7910/dvn/auyerv/yh0ahe
title: basicinfo_uspto_kr.tab

description: :unav
DOI: 10.7910/dvn/auyerv/zq699y
title: citation_uspto_all.tab

description: :unav
DOI: 10.7910/dvn/auyerv/26lfla
title: invt_loc_uspto_all.csv

description: :unav
DOI: 10.7910/dvn/auyerv/9tgucw
title: invt_loc_uspto_kr.tab

description: :unav
DOI: 10.7910/dvn/auyerv/i2y909
title: ipc_uspto_all.tab

description: :unav
DOI: 10.7910/dvn/auyerv/hgw8u5
title: Overview.pdf

description: :unav
DOI: 10.7303/syn22159468
title: Gazi Brains 2020 Dataset
DOI: 10.5519/qd.xz28fr8c
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1346371 records)
DOI: 10.3886/e120220v1
title: ambitious science teaching networked improvement community

description: Supplementary data for AERA Open publication: Launching Networked PLCs: Footholds Into Creating and Improving Knowledge of Ambitious and Equitable Teaching Practices in an RPP<br><br>Studio field notes. Research and coaches' observations of Studios from 2013-2016 across all schools in the NIC. <br><br>Classroom observations. 154 classroom observations, with data about how teachers implemented science teaching practices from 2015-2016. <br>NIC Survey. Annual teacher survey from 2017, describing which practices became commonplace in teacher's classrooms.
DOI: 10.3886/e120220
title: ambitious science teaching networked improvement community

description: Supplementary data for AERA Open publication: Launching Networked PLCs: Footholds Into Creating and Improving Knowledge of Ambitious and Equitable Teaching Practices in an RPP<br><br>Studio field notes. Research and coaches' observations of Studios from 2013-2016 across all schools in the NIC. <br><br>Classroom observations. 154 classroom observations, with data about how teachers implemented science teaching practices from 2015-2016. <br>NIC Survey. Annual teacher survey from 2017, describing which practices became commonplace in teacher's classrooms.
DOI: 10.3886/e108303v1
title: Who Did US Congress Retweet in 2017

description: This dataset includes the retweets posted on Twitter by accounts associated with members of the US Congress in 2017. The list of accounts combines two sources: <br>Justin Littman's list (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/UIVHQR)The United States project list (https://github.com/unitedstates/congress-legislators)Tweets were collected using Twitter's Search API through the twitter_user_collector Python script (https://github.com/casmlab/twitter_user_collector). <br>
description: other;
description: 462 Twitter accounts related to the US Congress<br>447 members of the United States Congress (MCs)15 accounts associated with (a) former or deceased MCs or (b) campaigns associated with MCsData about members of Congress (e.g., party, chamber, gender, birthdate)<br><br>.
DOI: 10.3886/e118161v1
title: Intellectual Disability Literacy and Stigma in Austria, Germany, and the UK

description: Article 8 of the UN Convention on the Rights of People with Disabilities requires governments around the globe to raise awareness on issues of disability in their societies, combatting stereotypes, prejudices and harmful practices. Little comparative data is available on lay people's social representations of intellectual disability and associated stigma, which could inform actions in line with the convention. This study aimed to compare intellectual disability literacy and stigma among adults in the general population in the UK, Austria and Germany (N=1046), and to examine the effects of providing an intellectual disability label on these outcomes. <br><br>For the assessment of ID literacy and stigma, the Intellectual Disability Literacy Scale (IDLS) by Scior and Furnham (2013) was used. This instrument consists of a vignette, describing a person with a mild ID, and a questionnaire. The questionnaire comprises three parts, namely recognition (1 item), causal and intervention beliefs (44 items), and social distance (4 items). In the first part of the IDLS, participants are presented with a diagnostically labelled or unlabelled vignette. Both vignette versions were identical other than inclusion/omission of a diagnostic label, consisting of ‘learning disability (mental handicap)’ in the UK and ‘intellektuelle Behinderung (geistige Behinderung)’ in Austria and Germany. Of the UK sample 265 (50.38%) participants received the labelled version, and 258 (49.62%) of the Austrian/German sample.<br><br> Additionally to the IDLS, we assessed prior contact to persons with ID via two items: We determined if the respondent ever had contact to a person with ID (yes/no), and how close this contact was (rated on a 9 point scale where 1 = not at all close, 5 = somewhat close and 9 = extremely close). As sociodemographis variables, we assessed age, gender, level of education, and religion.
DOI: 10.3886/e120221v1
title: Assessing Color-Neutral Racial Attitudes of Faculty at Hispanic-Serving Institutions (HSIs)

description: The purpose of this exploratory quantitative study was to assess the color-neutral racial attitudes of faculty at Hispanic-Serving Institutions (HSIs) and to investigate the connection to knowledge and skills for teaching minoritized students. HSIs enroll a large percentage of racially minoritized students and faculty must be able to assess their attitudes about institutional discrimination and blatant racial issues, which may affect how they teach students of color. We used The Color-Blind Racial Attitudes Scale (CoBRAS) and Multicultural Teaching Competency Scale (MTCS) to collect data from faculty at 10 HSIs across the United States. Results show that faculty in our sample have low levels of color-neutral racial attitudes, meaning they are aware of racial issues facing students, with slight differences by race, gender and academic discipline. Moreover, higher color-neutral racial attitudes are associated with lower knowledge and skills for teaching minoritized students. Implications for enhancing “servingness” at HSIs are offered.