cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3890234
title: Bridging the Gap between Autonomous and Predetermined Paradigms: The Role of Sampling in Evaluative Learning

description: While most evaluative learning paradigms remove participants’ autonomy over the information they receive, other research traditions have demonstrated that information sampling has an important role in learning. We investigate the impact of information sampling on a central evaluative learning paradigm: evaluative conditioning. We compare a traditional evaluative conditioning paradigm with a paradigm in which participants have autonomy over the stimulus pairings they receive. Participants in the high-autonomy condition show a strong preference for positively paired CSs. Nevertheless, we obtained evaluative conditioning effects in all conditions. High-autonomy participants, but not their low-autonomy counterparts, also show effects of the sampling decision on evaluations. Specifically, sampled stimuli become more positive, whereas ignored stimuli become more negative over the learning phase. Thus, the present research provides a cornerstone for integrating several research traditions within and beyond the evaluative learning literature, providing a foundation for new insights and more comprehensive theories of learning.
description: This work was supported by an Emmy Noether grant (HU 1978/4-1) and a Heisenberg grant (HU 1978/7-1) awarded to Mandy Hütter by the German Research Foundation.
DOI: 10.23708/citkfu
title: Food environment benchmarking of policies to prevent nutrition-related non-communicable diseases in Kenya

description: This study assessed government action, implementation gaps, and priorities to improve the healthiness of food environments (FE) in Kenya. Using the Healthy Food-Environment Policy Index (Food-EPI), a panel (n=16) of government and independent experts rated the extent of government action to improve the healthiness of FE in Kenya against international best practices and according to steps within a policy cycle. The Food-EPI tool used was based on that developed by the International Network for Food and Obesity/NCDs Research Monitoring and Action Support (INFORMAS- https://www.informas.org/food-epi/). 43 practice indicators of FE policy and infrastructure support were used. Ratings were informed by documented evidence, validated by government officials. Following the rating exercise, the experts proposed and prioritized actions for implementation by the Kenyan government. Two documents are provided: 1. Technical report of evidence of policy activity (‘evidence pack’) 2. Technical report of healthy food environment policy index (Food-EPI) assessment (‘report findings’)
DOI: 10.5281/zenodo.3840135
title: Offshore wind competitiveness in mature markets without subsidy - Supplementary Data

description: This is the data set named "Supplementary Data 1" for the research paper "Offshore wind competitiveness in mature markets without subsidy". This data set also contain the raw data for reproducing Figure 1 through to Figure 4. The paper is currently under review and access is for peer-review purposes only.
description: Bundesministerium für Bildung und Forschung under project reference FKZ 01LA1821A. BTU Cottbus-Senftenberg for a postgraduate scholarship (GradV).
DOI: 10.5281/zenodo.3887206
title: Associated Dataset for DeepCAT program

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3887205
title: Associated Dataset for DeepCAT program

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3890249
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3890248
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.25919/5ee264e27b12f
title: Parkes observations for project P1041 semester 2020APRS_07

description: We recently discovered five millisecond pulsars (MSPs) in the globular cluster Omega Centauri. This is the first pulsar discovery in this globular cluster. Four of these pulsars are isolated with spin periods of 4.1, 4.2, 4.6 and 6.8ms. The fifth has a spin period of 4.8ms and is in an eclipsing binary system with an orbital period of 2.1 hours. We propose to carry out a timing campaign of these new pulsars in order to study the dynamics in the core of Omega Centauri, to reveal the origin of Gamma-ray emission from the core, and to probe the ionised interstellar medium in Omega Centauri. Our proposed observations will also enable us to carry out deeper searches of pulsars in the cluster.
DOI: 10.5281/zenodo.3890265
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3890264
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.25919/5ee26524d1e3c
title: Parkes observations for project P958 semester 2020APRS_15

description: Fast radio bursts (FRBs) continue to raise more questions than they answer. Until last year, only one burst – the only one known to repeat - had been localized to a bright radio nebula (either a young supernova remnant or pulsar wind nebulae) in a distant dwarf galaxy. Over the last three years searches with the Australian Square Kilometre Array Pathfinder (ASKAP) have detected 35 bursts, eight of which have been localised to host galaxies at redshifts ranging from 0.1 to 0.5. These bursts and their hosts are very unlike the repeater, suggesting there may be a dichotomy in the population. However one of the arcminute-localised ASKAP FRBs has recently been found to repeat. Here we propose to search and study repetitions in this FRB population. Our comprehensive sample and a dense monitoring campaign of well localised bursts, at a fluence limit more than 60 times lower than that of their detections to characterise the fraction of bursts that repeat. For bursts that repeat the unique wide bandwidth observations will be used to test leading models of burst emission.
DOI: 10.5281/zenodo.3890274
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3890216
title: Results of experiments in paper Simulated Hyperparameter Optimization for Statistical Tests in Machine Learning Benchmarks

description: Results of the experiments in paper Simulated Hyperparameter Optimization for Statistical Tests in Machine Learning Benchmarks
DOI: 10.5281/zenodo.3890215
title: Results of experiments in paper Simulated Hyperparameter Optimization for Statistical Tests in Machine Learning Benchmarks

description: Results of the experiments in paper Simulated Hyperparameter Optimization for Statistical Tests in Machine Learning Benchmarks
DOI: 10.18112/openneuro.ds002879.v1.0.1
title: Brain Correlates of Multisensory Lexical Processing in Adults
DOI: 10.21227/0x89-vq60
title: mmWave Radar face signatures

description: This database includes data measured by Qualcomm's 60GHz mmWave Radar.It includes:Face signature data base: radar face scan data of 206 individuals for face recognition.
DOI: 10.21227/tah5-8772
title: mmWave Radar face signatures

description: This database includes data measured by Qualcomm's 60GHz mmWave Radar.It includes:Face signature data base: radar face scan data of 206 individuals for face recognition.
DOI: 10.18434/m32252
title: A Tool to Model the Fate and Transport of Indoor Microbiological Aerosols

description: The web-based tool Fate and Transport of Indoor Microbiological Aerosols (FaTIMA) allows the determination of the fate of indoor microbiological aerosols associated with ventilation, filtration, deposition, and inactivation mechanisms. FaTIMA provides a representation of a single, well-mixed zone that is served by a mechanical ventilation system and incorporates source and removal mechanisms for an aerosol with a single, user-defined representative particle size. The simple mechanical ventilation system model allows specification of supply, return, and outdoor air intake rates to represent either a positive, negative or balanced ventilation system. Aerosol sources are provided to enable any combination of continuous, e.g., breathing-related emissions, or intermittent, e.g., coughing-related emissions. Aerosol removal mechanisms include filters within the ventilation system, room air cleaners, and deposition onto floors, walls, and ceilings. Simulations can be run for a 24-h period, with the results including the time history of the airborne concentration and surface loading, as well as integrated exposure of an occupant. Exposure is based on a uniform aerosol concentration within the zone and does not account for spatial differences in aerosol concentrations such as those due to momentum effects of respiratory emissions.
DOI: 10.21227/7s1n-hm82
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 170+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data, which currently does not exist or is private.
DOI: 10.5519/qd.54xaihyb
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2113 records)
DOI: 10.15139/s3/yaynes
title: LISN dataset test

description: Este es un dataset de prueba
DOI: 10.23721/110/1353777
title: BGP Logs (2012-01-01 to 2013-11-15)

description: The University of Wisconsin maintains an external BGP peering session with the campus border router. Updates from this session are archived. We also periodically collect the BGP routing table snapshots from this monitor.
DOI: 10.23721/110/1353976
title: Internet Atlas (2011-09-01 to 2017-08-30)

description: Internet Atlas is a visualization and analysis portal for diverse Internet measurement data. The starting point for Atlas is a geographically anchored representation of the physical Internet including (i) nodes (e.g., hosting facilities and data centers), (ii) conduits/links that connect these nodes, and (iii) relevant meta data (e.g., source provenance). Data are added to the repository on an on-going basis. Access is via credentials issued by UWisc to approved PREDICT researchers.
DOI: 10.23721/110/1354180
title: Network Time Protocol (NTP) packet traces (2015-02-13 to 2015-02-14)

description: This dataset is comprised of NTP log data collected from the NTP server infrastructure at the Wisconsin. Each entry in the dataset includes details of contents for all IPv4 packets sent and received by the servers over the collection period. The total number of valid measurements included in the dataset is around 11MM. All of the IP addresses in the traces are anonymized in a persistent fashion.
DOI: 10.23721/110/1504247
title: Internet event log (2019-01-01 to 2019-05-15)

description: This dataset contains details about the events detected by the BigBen internet-wide event monitoring system developed at the University of Wisconsin - Madison. BigBen detects events based on analyzing Network Time Protocol data contributed from NTP server operators. This dataset contains events identified in data provided by 16 NTP servers in 7 US locations, during the period of January 2019 to May 2019. The size of the file is 1GB. It includes a list of /24 IPv4 networks and /96 IPv6 networks in which events were detected. The method for detecting events is described in the paper: Meena Syamkumar, Sathiya Mani, Ram Durairajan, Paul Barford and Joel Sommers. "Wrinkles in Time: Detecting Internet-wide Events via NTP", In Proceedings of the IFIP Networking, Zurich, Switzerland, May, 2018.
DOI: 10.5519/qd.zfmdqfmy
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (62 records)
DOI: 10.5066/p9axlosm
title: SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Pacific Region of the United States, 2012 Base Year

description: The U.S. Geological Survey's (USGS) SPAtially Referenced Regression On Watershed attributes (SPARROW) model was used to aid in the interpretation of monitoring data and simulate streamflow and water-quality conditions in streams across the Pacific Region of the Unites States. SPARROW is a hybrid empirical/process-based mass balance model that can be used to estimate the major sources and environmental factors that affect the long-term supply, transport, and fate of contaminants in streams. The spatially explicit model structure is defined by a river reach network coupled with contributing catchments. The model is calibrated by statistically relating watershed sources and transport-related properties to monitoring-based water-quality load estimates. This USGS data release includes input and output files associated with 2012 SPARROW simulations of streamflow, total nitrogen, total phosphorus and suspended-sediment load in streams of the Pacific region. Model construction, calibration and results are described in Wise (2019, https://doi.org/10.3133/sir20195112).
DOI: 10.6092/unibo/amsacta/6400
title: OLEUM project. Rapid screening tool of quality grades of virgin olive oils. FGC in tandem with chemometrics

description: This data set contains the underlying data of the scientific publication: Barbieri S. et al., 2020. FGC in tandem with chemometrics: a rapid screening tool of quality grades of virgin olive oils. This publication will be submitted to a scientific journal. This research aims to develop a classification model based on an untargeted elaboration of volatile fraction fingerprints of virgin olive oils (n=331) analyzed by Flash Gas-Chromatography in order to predict the commercial category of samples (extra virgin olive oil, EVOO; virgin olive oil, VOO; lampante olive oil, LOO). The raw data related to volatile profiles were considered as independent variables, while the quality grades provided by the sensory assessment were defined as reference parameter. This data matrix was elaborated using a linear technique, Partial Least Squares-Discriminant Analysis (PLS-DA), applying, in sequence, two classification models with two categories (EVOO vs noEVOO followed by VOO vs LOO and LOO vs noLOO followed by VOO vs EVOO). Results from this large set of samples provide satisfactory results in terms of percentages of correctly classified samples, ranging from 72 to 85%, in external validation. This confirms the reliability of this approach as rapid screening of quality grades and that it represents a solution for supporting the sensory panels, increasing the efficiency of the controls, applicable also to the industrial sector. This data set contains the raw data obtained from the analysis of all the sample by Flash Gas-Chromatography and used for the building of the PLS-DA data matrix.
DOI: 10.17632/fp3798tnhw.1
title: Photobleaching of chlorophyll in light-harvesting complex II
DOI: 10.5281/zenodo.3890339
title: Up-to-date characterization factors for EXIOBASE3

description: Characterization matrix linking environmental extensions of EXIOBASE3 to characterization factors of IMPACT World+, an up-to-date lifecycle impact assessment method. Version of IW+ used: 10.5281/zenodo.3521034
description: {"references": ["Bulle, C., Margni, M., Patouillard, L., Boulay, A. M., Bourgault, G., De Bruille, V., \u2026 Jolliet, O. (2019). IMPACT World+: a globally regionalized life cycle impact assessment method. International Journal of Life Cycle Assessment, 1653\u20131674. https://doi.org/10.1007/s11367-019-01583-0"]}
DOI: 10.5281/zenodo.3890367
title: Characterization matrix linking ecoinvent3.5 to IMPACT World+

description: Characterization matrix linking the elementary flows of ecoinvent3.5 to 36 impact categories of IMPACT World+. The dataset includes the characterization matrix itself and another matrix linking UUIDs of the elementary flows to their metadata (i.e., units, compartments, sub-compartments). Version of IW+ used: 10.5281/zenodo.3521034
DOI: 10.5281/zenodo.3890366
title: Characterization matrix linking ecoinvent3.5 to IMPACT World+

description: Characterization matrix linking the elementary flows of ecoinvent3.5 to 36 impact categories of IMPACT World+. The dataset includes the characterization matrix itself and another matrix linking UUIDs of the elementary flows to their metadata (i.e., units, compartments, sub-compartments). Version of IW+ used: 10.5281/zenodo.3521034
DOI: 10.5281/zenodo.3890377
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3890286
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3890273
title: Associated Dataset for DeepCAT

description: Deep Learning Method to Identify Cancer Associated TCRs
DOI: 10.5281/zenodo.3828028
title: Code and dataset for: Cell position fates and collective fountain flow in bacterial biofilms revealed by light-sheet microscopy

description: Dataset and codes for: Cell position fates and collective fountain flow in bacterial biofilms revealed by light-sheet microscopy. Published online via First Release, <strong>11 June 2020</strong> B. Qin et al., Science, 10.1126/science.abb8501 (2020).
DOI: 10.5281/zenodo.3828027
title: Code and dataset for: Cell position fates and collective fountain flow in bacterial biofilms revealed by light-sheet microscopy

description: Dataset and codes for: Cell position fates and collective fountain flow in bacterial biofilms revealed by light-sheet microscopy. Published online via First Release, <strong>11 June 2020</strong> B. Qin et al., Science, 10.1126/science.abb8501 (2020).
DOI: 10.14272/wslmoortnotzco-uhfffaoysa-n.1
title: 2-iodo-N-methylaniline
title: InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3

description: This dataset contains experimental data from a sample (2-iodo-N-methylaniline, InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3). The datasets from the following experiments are available: 1H nuclear magnetic resonance spectroscopy (1H NMR) 13C nuclear magnetic resonance spectroscopy (13C NMR) distortionless enhancement with polarization transfer (DEPT) distortionless enhancement with polarization transfer (DEPT) mass spectrometry (MS) infrared absorption spectroscopy (IR)
DOI: 10.14272/wslmoortnotzco-uhfffaoysa-n/chmo0000596
title: distortionless enhancement with polarization transfer (DEPT) (2-iodo-N-methylaniline)
title: InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (101 MHz, CDCl3, ppm) δ = 138.9 (CH), 129.5 (CH), 118.5 (CH), 110.0 (CH).
DOI: 10.14272/wslmoortnotzco-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (2-iodo-N-methylaniline)
title: InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, 50 °C): 308 (31), 232 (20) [M]+, 203 (22), 105 (29), 97 (30), 83 (33), 77 (71), 71 (20), 70 (20), 69 (28), 57 (100), 55 (26). HRMS (C7H8IN): Calcd 232.9702, Found 232.9702.
DOI: 10.14272/wslmoortnotzco-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (2-iodo-N-methylaniline)
title: InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, CDCl3 [7.26 ppm], ppm) δ = 7.66 (dd, J = 7.8, 1.5 Hz, 1H), 7.26–7.22 (m, 1H), 6.57 (dd, J = 8.1, 1.5 Hz, 1H), 6.46 (td, J = 7.5, 1.5 Hz, 1H), 4.23 (br. s, 1H), 2.89 (s, 3H).
DOI: 10.14272/wslmoortnotzco-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (2-iodo-N-methylaniline)
title: InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, CDCl3 [77.16 ppm], ppm) δ = 148.3, 139.0 (+, CH), 129.6 (+, CH), 118.6 (+, CH), 110.2 (+, CH), 85.3, 31.1 (+, CH3).
DOI: 10.14272/wslmoortnotzco-uhfffaoysa-n/chmo0000596.1
title: distortionless enhancement with polarization transfer (DEPT) (2-iodo-N-methylaniline)
title: InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3

description: dataset for distortionless enhancement with polarization transfer (DEPT) 13C NMR (101 MHz, CDCl3, ppm) δ = 138.9 (+, CH), 129.5 (+, CH), 118.5 (+, CH), 110.0 (+, CH), 31.0 (+, CH3).
DOI: 10.14272/wslmoortnotzco-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (2-iodo-N-methylaniline)
title: InChI=1S/C7H8IN/c1-9-7-5-3-2-4-6(7)8/h2-5,9H,1H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2955 (m), 2921 (vs), 2854 (s), 2176 (vw), 1717 (w), 1589 (m), 1571 (w), 1506 (w), 1490 (m), 1468 (s), 1435 (m), 1377 (w), 1353 (w), 1310 (w), 1251 (w), 1237 (w), 1139 (w), 1129 (w), 1103 (w), 1078 (w), 1035 (w), 1014 (m), 798 (w), 761 (m), 724 (m) cm–1.
DOI: 10.5281/zenodo.3890420
title: IO Islamic 1992. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma'add bin 'Adnân

description: IO Islamic 1992. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma’add bin ‘Adnân
DOI: 10.5281/zenodo.3890419
title: IO Islamic 1992. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma'add bin 'Adnân

description: IO Islamic 1992. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma’add bin ‘Adnân
DOI: 10.5281/zenodo.3890423
title: IO Islamic 062. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma'add bin 'Adnân

description: IO Islamic 062. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma’add bin ‘Adnân
DOI: 10.5281/zenodo.3890422
title: IO Islamic 062. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma'add bin 'Adnân

description: IO Islamic 062. Nigâristân, Anecdotes and Curious Narratives of Celebrated Men from the Time of Nizâr bin Ma’add bin ‘Adnân