cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1423796
title: Manipulating Azobenzene Photoisomerization Through Strong Light-Molecule Coupling

description: Deposited data of the Non-adiabatic Dynamics of the cases presented in the paper.<br>
<br>
The formation of hybrid light-molecule states (polaritons) offers a new strategy to manipulate the photochemistry of molecules. To fully exploit its potential, one needs to build a toolbox of polaritonic phenomenologies that supplement those of standard photochemistry. By means of a state-of-the-art computational photochemistry approach extended to the strong-coupling regime, here we disclose various mechanisms peculiar of polaritonic chemistry: coherent population oscillations between polaritons, quenching by trapping in dead-end polaritonic states and the alteration of the photochemical reaction pathway and quantum yields. We focus on azobenzene photoisomerization, that encompasses the essential features of complex photochemical reactions such as the presence of conical intersections and reaction coordinates involving multiple internal modes. In the strong coupling regime, a polaritonic conical intersection arises and we characterize its role in the photochemical process. Our chemically detailed simulations provide a framework to rationalize how the strong coupling impacts the photochemistry of realistic molecules.
DOI: 10.7910/dvn/9oobqw/j7pfjt
title: petitions.xlsx
DOI: 10.7910/dvn/dhrue3/vrdf0p
title: petitions.xlsx
DOI: 10.7910/dvn/fhwpmz/6ojpne
title: petitions.xlsx
DOI: 10.7910/dvn/l78thg/wrdls9
title: petitions.xlsx
DOI: 10.7910/dvn/nzk4op/hg1n6r
title: petitions.xlsx
DOI: 10.7910/dvn/ti6tgn/u5zp4w
title: petitions.xlsx
DOI: 10.7910/dvn/xygeur/zdp05j
title: petitions.xlsx
DOI: 10.18169/papdeott007359
title: Storm Forecasting Intelligence for Outage Control (SFIFOC)
DOI: 10.5281/zenodo.1434701
title: Air quality data created by the hackAIR Horizon2020 project

description: The current datasets comprise of air quality data collected or created within the hackAIR project (https://platform.hackair.eu/) all around Europe from February 2018 until November 2018.

i) "measurements_arduino.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with stationary hackAIR sensors (https://www.hackair.eu/hackair-home-v2/). These sensing devices are based either on an Arduino or a Wemos board. The first column is the unique identifier for the measurement in the hackAIR database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.

ii) "measurements_bleair.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with mobile hackAIR sensors (https://www.hackair.eu/hackair-mobile/). The first column is the unique identifier for the measurement in the  database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.

iii) "measurements_sky_photos.xlsx": Air pollution estimations from photos depicting sky. The hackAIR platform estimates the particulate matter content in the air from Flickr photos, photos from webcams and sky photos that users upload on the hackAIR mobile application, based on the colour of the sky. This is expressed as Aerosol Optical Depth (AOD). In the current dataset, the timezone is UTC, while AOD is unitless.

The pollutant index is based on a scale created for the purposes of the hackAIR project.
DOI: 10.7910/dvn/1c91he/njwvzt
title: petitions.xlsx
DOI: 10.7910/dvn/3xeue0/ipoahq
title: petitions.xlsx
DOI: 10.7910/dvn/f3ahvc/gk8sme
title: petitions.xlsx
DOI: 10.7910/dvn/piagv8/utpsi6
title: petitions.xlsx
DOI: 10.7910/dvn/qvwhic/9k0eqc
title: petitions.xlsx
DOI: 10.7910/dvn/z0rjwj/bhhyih
title: petitions.xlsx
DOI: 10.13140/rg.2.2.29338.08643
title: Dutch Breast Implant Registry DBIR-2017, datasheet on explanted breast implants
DOI: 10.18169/papdeott007360
title: Rücksetzgeschützte Sperrung einer Ein-/Ausgabeschnittstelle
DOI: 10.18169/papdeott007378
title: Alkoholsensor
DOI: 10.18169/papdeott007379
title: Hydrogel-bildende Mehrkomponentenfaser
DOI: 10.5281/zenodo.1405372
title: Research Data Supporting "Engineering Anisotropic Muscle Tissue Using Acoustic Cell Patterning"

description: Raw research data supporting the publication:

Armstron, JPK et al., "Engineering anisotropic muscle tissue using acoustic cell paterning", Advanced Materials, DOI: 10.1002/adma.201802649 (2018)
DOI: 10.5281/zenodo.1405373
title: Research Data Supporting "Engineering Anisotropic Muscle Tissue Using Acoustic Cell Patterning"

description: Raw research data supporting the publication:

Armstron, JPK et al., "Engineering anisotropic muscle tissue using acoustic cell paterning", Advanced Materials, DOI: 10.1002/adma.201802649 (2018)
DOI: 10.5281/zenodo.1423651
title: Supplementary Data For "Computational Investigation Of Ro2 + Ho2 And Ro2 + Ro2 Reactions Of Monoterpene Derived First-Generation Peroxy Radicals Leading To Radical Recycling", Revised Version Submitted To J. Phys. Chem. A

description: log files (Gaussian 09) and out files (Orca 4.0)

(Note: Files_final.zip contains all the same log and out files as Files.zip, but also few additional structures added during the revision process, namely two additional RO2 from ocimene ozonolysis, and one additional transition state for a RO2 + HO2 reaction. Please ignore the file Files.zip.)
DOI: 10.5281/zenodo.1423652
title: Supplementary Data For "Computational Investigation Of Ro2 + Ho2 And Ro2 + Ro2 Reactions Of Monoterpene Derived First-Generation Peroxy Radicals Leading To Radical Recycling", Submitted To J. Phys. Chem. A

description: log files (Gaussian 09) and out files (Orca 4.0)
DOI: 10.5281/zenodo.1434702
title: Air Quality Data Created By The Hackair Horizon2020 Project

description: The current datasets comprise of air quality data collected or created within the hackAIR project (https://platform.hackair.eu/) all around Europe from February 2018 until August 2018.

i) "measurements_arduino.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with stationary hackAIR sensors (https://www.hackair.eu/hackair-home-v2/). These sensing devices are based either on an Arduino or a Wemos board. The first column is the unique identifier for the measurement in the hackAIR database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.

ii) "measurements_bleair.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with mobile hackAIR sensors (https://www.hackair.eu/hackair-mobile/). The first column is the unique identifier for the measurement in the  database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.

iii) "measurements_sky_photos.xlsx": Air pollution estimations from photos depicting sky. The hackAIR platform estimates the particulate matter content in the air from Flickr photos, photos from webcams and sky photos that users upload on the hackAIR mobile application, based on the colour of the sky. This is expressed as Aerosol Optical Depth (AOD). In the current dataset, the timezone is UTC, while AOD is unitless.

The pollutant index is based on a scale created for the purposes of the hackAIR project.
DOI: 10.5281/zenodo.1434736
title: Quantitative Comparison Of A Mobile And A Stationary Video-Based Eye-Tracker

description: Vision represents the most important sense of primates. To understand visual processing, various different methods are employed, e.g. electrophysiology, psychophysics or eye tracking. For the latter, researchers recently began to step outside the artificial environments of laboratory setups towards the more natural conditions we usually face in the real world. This approach was enabled by sophisticated video-based mobile eye-trackers. Yet, their mobility and light weight often comes at an expense of reduced performance and accuracy, compared to the stationary eye-trackers, which nowadays are considered a gold standard in vision science.

In order to get a better understanding of the advantages and limitations of both eye-tracking techniques, we quantitatively compared one of the most advanced mobile eye-trackers available, the EyeSeeCam (ESC, sampling at 230 Hz), with a commonly used laboratory eye-tracker, the EyeLink II (ELII, sampling at 500 Hz). We aimed to investigate whether or not fully mobile eye-trackers are capable of providing adequate data to allow for a direct comparison with data recorded with stationary eye-trackers. Therefore, we recorded three different, commonly used eye movements, i.e. fixation, saccades and smooth pursuit eye movements, with both eye trackers in successive standardized paradigms in a laboratory setting with eight human subjects.

Despite major technical differences, the values of most eye movement parameters were not statistically different between both systems. Differences could only be found in overall gaze accuracy and for time critical parameters like saccade duration, for which a higher sample frequency is especially useful. Although, the stationary ELII system proved to be superior, especially at a single subject or even at a single trial basis, the ESC showed a similar performance for averaged parameters across trials and subjects. We conclude that modern high-performance mobile eye-trackers are well suited to provide reliable oculomotor data at the required spatial and temporal resolution.

Here we provide the raw data recorded with both eye trackers in eights human subjects in the three different, commonly used, oculomotor tasks.
DOI: 10.5281/zenodo.1434737
title: Quantitative Comparison Of A Mobile And A Stationary Video-Based Eye-Tracker

description: Vision represents the most important sense of primates. To understand visual processing, various different methods are employed, e.g. electrophysiology, psychophysics or eye tracking. For the latter, researchers recently began to step outside the artificial environments of laboratory setups towards the more natural conditions we usually face in the real world. This approach was enabled by sophisticated video-based mobile eye-trackers. Yet, their mobility and light weight often comes at an expense of reduced performance and accuracy, compared to the stationary eye-trackers, which nowadays are considered a gold standard in vision science.

In order to get a better understanding of the advantages and limitations of both eye-tracking techniques, we quantitatively compared one of the most advanced mobile eye-trackers available, the EyeSeeCam (ESC, sampling at 230 Hz), with a commonly used laboratory eye-tracker, the EyeLink II (ELII, sampling at 500 Hz). We aimed to investigate whether or not fully mobile eye-trackers are capable of providing adequate data to allow for a direct comparison with data recorded with stationary eye-trackers. Therefore, we recorded three different, commonly used eye movements, i.e. fixation, saccades and smooth pursuit eye movements, with both eye trackers in successive standardized paradigms in a laboratory setting with eight human subjects.

Despite major technical differences, the values of most eye movement parameters were not statistically different between both systems. Differences could only be found in overall gaze accuracy and for time critical parameters like saccade duration, for which a higher sample frequency is especially useful. Although, the stationary ELII system proved to be superior, especially at a single subject or even at a single trial basis, the ESC showed a similar performance for averaged parameters across trials and subjects. We conclude that modern high-performance mobile eye-trackers are well suited to provide reliable oculomotor data at the required spatial and temporal resolution.

Here we provide the raw data recorded with both eye trackers in eights human subjects in the three different, commonly used, oculomotor tasks.
DOI: 10.7910/dvn/aduthj/nz7l0d
title: petitions.xlsx
DOI: 10.7910/dvn/bwwtpt/j3xznc
title: petitions.xlsx
DOI: 10.7910/dvn/ihpzlq/3vuj6t
title: petitions.xlsx
DOI: 10.7910/dvn/ns180y/asefsd
title: petitions.xlsx
DOI: 10.7910/dvn/o1p0x3/wfcd5x
title: Prj142_AHRChIPSEQ_Male_TCDD_2HR.tab
DOI: 10.7910/dvn/o1p0x3/x4a4yg
title: Prj142_AHRChIPSEQ_Male_TCDD_2HR.metadata
DOI: 10.7910/dvn/q6mnvy/8kdbpc
title: petitions.xlsx
DOI: 10.7910/dvn/qzjxbi/fxoyb8
title: petitions.xlsx
DOI: 10.7910/dvn/vgqwkh/yl8ftq
title: petitions.xlsx
DOI: 10.7910/dvn/7ck3aj/ctxxd4
title: petitions.xlsx
DOI: 10.7910/dvn/7lux46/ojxpxr
title: petitions.xlsx
DOI: 10.7910/dvn/8bnbrl/swzbwn
title: petitions.xlsx
DOI: 10.7910/dvn/emqjqw/hrjf2k
title: petitions.xlsx
DOI: 10.7910/dvn/ow0lt9/fgvqhs
title: petitions.xlsx
DOI: 10.7910/dvn/p5r6su/7qzdsl
title: petitions.xlsx
DOI: 10.7910/dvn/z7ircj/9pdqik
title: petitions.xlsx
DOI: 10.18112/openneuro.ds000113.v1.2.0
title: Forrest Gump
DOI: 10.23634/mmudr.00621497
title: Liverpool-Ireland Abortion Corridor: History, Activism and Medical Practice

description: This dataset contains transcripts of qualitative interviews conducted with health care professionals and activists in the Republic of Ireland, Northern Ireland and England as part of a study of the Liverpool-Ireland Abortion Corridor. The LIAC is a historically and culturally constituted pathway enabling women living on the island of Ireland access to abortion services. The research was conducted in 2016, at which time abortion was still heavily restricted in RoI/NI and the majority of women had to either travel elsewhere for services or import 'abortion pills' illegally. The interviews were semi-structured and conducted by two researchers.
DOI: 10.5281/zenodo.1423124
title: Characterizing Alternative Feeds For Rainbow Trout (O. Mykiss) By 1H-Nmr Metabolomics

description: The CPMG NMR representative spectra of alternative feeds used in the manuscript draft entiteld:

<strong>Characterizing alternative feeds for rainbow trout </strong><strong>(<em>O. mykiss</em>) by <sup>1</sup>H-NMR metabolomics</strong>

Simon Roques<sup>1,2,3</sup>, Catherine Deborde<sup>3,4</sup>, Nadège Richard<sup>2</sup>, Luce Sergent<sup>5</sup>, Francis Kurz<sup>6</sup>, Sandrine Skiba-Cassy<sup>1</sup>, Benoît Fauconneau<sup>1</sup>, Annick Moing<sup>3,4</sup>

 

<sup>1</sup> INRA, Univ Pau & Pays Adour, E2S UPPA, UMR 1419, Nutrition Métabolisme, Aquaculture, Saint Pée sur Nivelle, F-64310, France

<sup>2</sup> Phileo Lesaffre Animal Care, 59700 Marcq-en-Baroeul, France

<sup>3</sup> Bordeaux Metabolome Facility, MetaboHUB, CGFB, Centre INRA de Nouvelle Aquitaine Bordeaux, 33140 Villenave d’Ornon, France

<sup>4</sup> INRA, Univ. Bordeaux, UMR 1332 Fruit Biology and Pathology, Centre INRA de Nouvelle Aquitaine Bordeaux, 33140 Villenave d’Ornon, France

<sup>5</sup> Copalis Industrie, 62480 Le Portel, France

<sup>6</sup> Algae Natural Food, 67400 Illkirch-Graffenstaden, France

 

<em>Corresponding author: </em>simon.roques@inra.fr<em> </em>
DOI: 10.5281/zenodo.1434752
title: Horizon 2020 Er4Stem Workshop Data Year 1-3 Uk

description: This dataset was collected as part of the Educational Robotics for STEM (ER4STEM) project, funded by the European Commision's Horizon 2020 programme, grant agreement No. 665792. The dataset includes quantitative and qualitative data collected over 3 years of robotics workshops held in schools in the UK. Only data with informed consent to be shared via an open access repository is included. Only anonymised data is included and some data is excluded to project vulnerable participants.
DOI: 10.5281/zenodo.1434753
title: Horizon 2020 Er4Stem Workshop Data Year 1-3 Uk

description: This dataset was collected as part of the Educational Robotics for STEM (ER4STEM) project, funded by the European Commision's Horizon 2020 programme, grant agreement No. 665792. The dataset includes quantitative and qualitative data collected over 3 years of robotics workshops held in schools in the UK. Only data with informed consent to be shared via an open access repository is included. Only anonymised data is included and some data is excluded to project vulnerable participants.
DOI: 10.7910/dvn/7luz5f/awfj0q
title: petitions.xlsx
DOI: 10.7910/dvn/bemoat/m81pdy
title: petitions.xlsx
DOI: 10.7910/dvn/dm6ty7/bdvuul
title: petitions.xlsx
DOI: 10.7910/dvn/goylt7/dwq1ol
title: petitions.xlsx
DOI: 10.7910/dvn/m8golp/jwiha6
title: petitions.xlsx
DOI: 10.7910/dvn/sqjbwd/bycgcg
title: petitions.xlsx
DOI: 10.17632/584bmt6g8b.1
title: SHG India Dataset

description: Data set for SHG study done at a NGO in rural India.
DOI: 10.17632/5hr36rp3rz.2
title: Scale-invariant visual capabilities explained by topographic representations of luminance and texture in primate V1

description: See paper "Scale-invariant visual capabilities explained by topographic representations of luminance and texture in primate V1" Authors: Giacomo Benvenuti, Yuzhi Chen, Wilson S. Geisler, Eyal Seidemann Year : 2018
DOI: 10.17632/ghk2df4cvc.1
title: All Floors 30 days of data

description: Column Definitions Fall Risk Score This column displays the most recent fall risk assessment score for a patient in this encounter. Values from pended flowsheet data will display appended with a (P). /Age/Sex This column displays the patient's name, age, and sex. The information displays (Age M/F) format. This information is retrieved by extension 34203-IP Patient Name Age Sex. The age is calculated by subtracting the Date of Birth (I EPT 110) item from the current date. The Time of Birth (I EPT 111) item can also be used in the calculation to find age in hours. The sex is retrieved from the Sex (I EPT 130) item. Primary Prob This column displays the patient's primary problem. Extension record 34078-IP/RWB Primary Problem retrieves this information from the Patient (EPT) and Problem List (LPL) master files. Other factors are assessment score given by the nurses and patients existing health condition.
DOI: 10.5287/bodleian:xme4xbaoz
title: Unexplained Death in Infancy by deprivation and ethnicity

description: The final paper can be viewed at: http://dx.doi.org/10.1136/jech-2018-210453

We report a nearly five-fold disparity in risk of Unexplained Death in Infancy (UDI) across ethnic groups in England and Wales, and demonstrate that this disparity is not explained by deprivation. Formal adjustment for deprivation (IMD quintiles) does not even slightly reduce the ethnic variation (see Table 2 of the cited paper). A simple scatter plot of ethnic groups illustrates the lack of a relationship between deprivation and risk, with a virtually horizontal overall trend line (as shown in this Dataset). For example, Black Caribbean babies have nearly triple the UDI risk of Black African babies, but similar levels of deprivation. The Indian, Pakistani and Bangladeshi ethnic groups each have around half the risk of White British babies; the White British and Indian groups have similar (relatively low) levels of deprivation, and the Pakistani and Bangladeshi groups are the most deprived in England and Wales. In the cited paper we discuss various potential mediators of the ethnic differences, including sleep practices, breastfeeding and tobacco use, based on the ethnic-specific prevalence of these factors in prior survey data. We suggest that careful comparison of ethnic patterns of exposure and outcome might lead to a better understanding of the aetiology of these very distressing deaths.
DOI: 10.7910/dvn/c7jmkl/0zvdze
title: petitions.xlsx
DOI: 10.7910/dvn/czycky/2tf0p7
title: petitions.xlsx
DOI: 10.7910/dvn/glujgx/4wnknl
title: petitions.xlsx
DOI: 10.7910/dvn/le6am1/vjhcdq
title: petitions.xlsx
DOI: 10.7910/dvn/nx9upj/zqcvto
title: petitions.xlsx
DOI: 10.7910/dvn/o1p0x3/q2iudt
title: Prj142_AHRChIPSEQ_Male_TCDD_2HR_METADATA.xlsx
DOI: 10.7910/dvn/on2trt/k5dhwr
title: petitions.xlsx
DOI: 10.15482/usda.adc/1471686
title: Annotations of Unigenes Assembled from Schizaphis graminum and Sipha flava
DOI: 10.17632/b58tmn3xmj.1
title: Trabecular data of the capitate and third metacarpal through ontogeny in chimpanzees and gorillas
DOI: 10.17632/jtr29xbgwg.1
title: Release of ubiquitinated and non-ubiquitinated nascent chains from stalled mammalian ribosomal complexes by ANKZF1 and Ptrh1. Kuroha et al

description: Source data for “Release of ubiquitinated and non-ubiquitinated nascent chains from stalled mammalian ribosomal complexes by ANKZF1 and Ptrh1" (Kuroha et al.)
DOI: 10.5281/zenodo.1433753
title: Rir Samples Darmstadt And Helsinki, Summer-Autumn 2018

description: This dataset contains Room Impulse Response (RIR) measurements that we collected for evaluating the DoubleEcho proximity verification system. The package contains the original RIR sample recordings, processed audio files after deconvolution with the original signal and cropping the RIR, and precomputed features in CSV files. All the audio files are 16bit mono PCM Wave files.<br>
<br>
The original.wav file is the generated excitation signal that was emitted in the rooms.<br>
Other files follow the naming convention recorded_ROOM_emitter_EMITTER-ID_RECORDER-ID_TIMESTAMP{_cutrir}.wav<br>
The files where .wav follows directly the timestamp are the original recordings; the ones with <em>cutrir</em> in their name are the cropped RIR samples.

The CSV files contain extracted features like the RT60 value for the entire signal and for specific frequency ranges. An example of the extracted features of a single sample:

<pre><code>Filter	Band	RT60	EDT	D/R	C10	C35	C50	C80
		0.566	0.401	9.544	-0.590	5.064	7.300	11.096
Oct	125Hz	0.093	1.195	9.548	-9.351	-4.757	-2.745	-1.001
Oct	250Hz	0.430	3.367	9.552	-11.705	-8.236	-6.218	-4.841
Oct	500Hz	0.692	0.809	9.564	-3.952	-0.989	0.377	4.227
Oct	1kHz	0.640	0.582	9.559	-1.836	1.438	3.581	7.143
Oct	2kHz	0.636	0.484	9.543	-0.357	3.971	5.744	9.415
Oct	4kHz	0.589	0.370	9.549	0.451	6.333	8.095	11.789
Oct	8kHz	0.518	0.398	9.545	-1.018	4.701	7.201	11.088
Oct	16kHz	0.471	0.318	9.548	-1.463	5.970	8.914	13.408
1/3	125Hz	0.042	0.415	9.605	-2.504	-0.749	0.129	22.719
1/3	160Hz	0.122	0.633	9.573	-2.150	3.079	4.293	4.603
1/3	200Hz	0.274	2.361	9.545	-13.815	-9.676	-8.875	-6.187
1/3	250Hz	0.156	2.175	9.554	-9.833	-6.198	-5.467	-5.086
1/3	315Hz	0.046	0.572	9.557	-4.743	-2.653	3.075	7.797
1/3	400Hz	0.184	0.859	9.545	-6.032	-0.763	3.208	4.707
1/3	500Hz	0.688	0.976	9.555	-4.107	-0.176	1.401	2.865
1/3	630Hz	0.664	0.805	9.558	-6.423	-2.636	-1.604	3.992
1/3	800Hz	0.672	0.771	9.546	-3.799	-1.284	0.163	4.211
1/3	1kHz	0.620	0.644	9.546	-3.718	-0.355	0.606	4.326
1/3	1.3kHz	0.632	0.566	9.551	-3.745	0.293	3.464	8.282
1/3	1.6kHz	0.704	0.520	9.550	-1.082	3.188	5.015	7.854
1/3	2kHz	0.643	0.518	9.554	-0.331	2.060	5.214	8.615
1/3	2.5kHz	0.643	0.516	9.568	-1.302	4.395	5.647	8.990
1/3	3.2kHz	0.589	0.456	9.546	0.773	4.569	5.872	10.156
1/3	4kHz	0.586	0.351	9.549	-0.148	7.023	8.817	12.605
1/3	5kHz	0.603	0.435	9.545	-0.709	5.325	7.042	10.348
1/3	6.3kHz	0.567	0.385	9.546	-2.436	4.609	6.797	11.164
1/3	8kHz	0.502	0.414	9.545	-0.812	4.478	7.015	10.773
1/3	10kHz	0.438	0.328	9.554	-1.899	5.855	8.598	13.376
1/3	12.5kHz	0.321	0.237	9.598	-1.060	8.030	12.542	17.905
1/3	16kHz	0.541	0.341	9.546	0.635	6.372	8.562	11.948
1/3	20kHz	0.539	0.205	9.587	1.624	9.700	11.512	13.696</code></pre>