cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.4210863.v71
title: ‘Soundless answer. Firm safety.’ ERPs showing perceptual simulation since actual 160 ms (Bernabeu, Willems, & Louwerse, in prep.)

description: Images: experiment overview; stimuli; EEG montage used; waveforms; difference topographies, critical statistics; fixed effects of final models; entire modeling; raw data. For a detailed view of the waveforms per group and electrode, see https://pablobernabeu.shinyapps.io/export_files/.The entire data set is available at osf.io/97unm.<b>Abstract. </b>The engagement of sensory systems during word comprehension has been extensively documented, but the precise relevance of it is yet unclear. We probed into this with an event-related potential (ERP) experiment which implemented the conceptual modality switch. This paradigm works as follows. In each trial, participants judge whether a property word can describe a concept word. However, the critical manipulation is the conceptual modality of successive trials—e.g., haptic then visual—, as enabled by modality-normed stimuli. Switching across trials in different modalities, compared to maintaining a modality, incurs a switching cost. Previous experiments measured this from ERPs time-locked to the second word of target trials, and then from response times. In the current follow-up, we tackled more precisely the time frame of lexical and semantic access by time-locking ERPs to the first word of target trials, which also helped to avoid confound influence on the target word. Next, the experiment featured different types of switch—from auditory to visual, and from haptic to visual—, which were compared to the non-switch—visual to visual. Further, we had a quick response group (<i>n</i> = 21), and a self-paced group (<i>n</i> = 21), alongside a few participants with no speed instructions (<i>n</i> = 5). The results, analyzed with mixed effects models, reveal ERP effects of modality-switching in four typical time windows set between 160 and 750 ms after word onset. The overall effect, which increases over time, is broadly characterized by a negativity for modality-switching compared to not switching. It arises with both types of switch, and influences both participant groups within anterior as well as posterior brain regions. To the extent that this effect spans the time course of lexico-semantic retrieval, it suggests that perceptual simulation contributes fundamentally to the comprehension of words.ReferencesHald, L. A., Marshall, J.-A., Janssen, D. P., & Garnham, A. (2011). Switching modalities in a sentence verification task: ERP evidence for embodied language processing. <i>Frontiers in Psychology, 2.</i> Hauk, O., Coutout, C., Holden, A., & Chen, Y. (2012). The time-course of single-word reading: Evidence from fast behavioral and brain responses. <i>Neuroimage</i>, <i>60</i>, 2, 1462-1477.Mahon, B. Z. (2015). What is embodied about cognition? <i>Language, Cognition and Neuroscience, 30</i>, 4, 420-429.
DOI: 10.7910/dvn/v1rjpf/peinop
title: cbb20150522_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gpytqo
title: cbb20151222_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210863.v72
title: ‘Soundless answer. Firm safety.’ ERPs showing perceptual simulation since actual 160 ms (Bernabeu, Willems, & Louwerse, in prep.)

description: Images: experiment overview; stimuli; EEG montage used; waveforms; difference topographies, critical statistics; fixed effects of final models; entire modeling; raw data. For a detailed view of the waveforms per group and electrode, see https://pablobernabeu.shinyapps.io/export_files/.The entire data set is available at osf.io/97unm.<b>Abstract. </b>The engagement of sensory systems during word comprehension has been extensively documented, but the precise relevance of it is yet unclear. We probed into this with an event-related potential (ERP) experiment which implemented the conceptual modality switch. This paradigm works as follows. In each trial, participants judge whether a property word can describe a concept word. However, the critical manipulation is the conceptual modality of successive trials—e.g., haptic then visual—, as enabled by modality-normed stimuli. Switching across trials in different modalities, compared to maintaining a modality, incurs a switching cost. Previous experiments measured this from ERPs time-locked to the second word of target trials, and then from response times. In the current follow-up, we tackled more precisely the time frame of lexical and semantic access by time-locking ERPs to the first word of target trials, which also helped to avoid confound influence on the target word. Next, the experiment featured different types of switch—from auditory to visual, and from haptic to visual—, which were compared to the non-switch—visual to visual. Further, we had a quick response group (<i>n</i> = 21), and a self-paced group (<i>n</i> = 21), alongside a few participants with no speed instructions (<i>n</i> = 5). The results, analyzed with mixed effects models, reveal ERP effects of modality-switching in four typical time windows set between 160 and 750 ms after word onset. The overall effect, which increases over time, is broadly characterized by a negativity for modality-switching compared to not switching. It arises with both types of switch, and influences both participant groups within anterior as well as posterior brain regions. To the extent that this effect spans the time course of lexico-semantic retrieval, it suggests that perceptual simulation contributes fundamentally to the comprehension of words.ReferencesHald, L. A., Marshall, J.-A., Janssen, D. P., & Garnham, A. (2011). Switching modalities in a sentence verification task: ERP evidence for embodied language processing. <i>Frontiers in Psychology, 2.</i> Hauk, O., Coutout, C., Holden, A., & Chen, Y. (2012). The time-course of single-word reading: Evidence from fast behavioral and brain responses. <i>Neuroimage</i>, <i>60</i>, 2, 1462-1477.Mahon, B. Z. (2015). What is embodied about cognition? <i>Language, Cognition and Neuroscience, 30</i>, 4, 420-429.
DOI: 10.7910/dvn/v1rjpf/mezu46
title: cbb20150509_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ahu3l4
title: cbb20150831_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210863.v74
title: ‘Soundless answer. Firm safety.’ ERPs showing perceptual simulation since actual 160 ms (Bernabeu, Willems, & Louwerse, in prep.)

description: Images: experiment overview; stimuli; EEG montage used; waveforms; difference topographies, critical statistics; fixed effects of final models; entire modeling; raw data. For a detailed view of the waveforms per group and electrode, see https://pablobernabeu.shinyapps.io/export_files/.The entire data set is available at osf.io/97unm.<b>Abstract. </b>The engagement of sensory systems during word comprehension has been extensively documented, but the precise relevance of it is yet unclear. We probed into this with an event-related potential (ERP) experiment which implemented the conceptual modality switch. This paradigm works as follows. In each trial, participants judge whether a property word can describe a concept word. However, the critical manipulation is the conceptual modality of successive trials—e.g., haptic then visual—, as enabled by modality-normed stimuli. Switching across trials in different modalities, compared to maintaining a modality, incurs a switching cost. Previous experiments measured this from ERPs time-locked to the second word of target trials, and then from response times. In the current follow-up, we tackled more precisely the time frame of lexical and semantic access by time-locking ERPs to the first word of target trials, which also helped to avoid confound influence on the target word. Next, the experiment featured different types of switch—from auditory to visual, and from haptic to visual—, which were compared to the non-switch—visual to visual. Further, we had a quick response group (<i>n</i> = 21), and a self-paced group (<i>n</i> = 21), alongside a few participants with no speed instructions (<i>n</i> = 5). The results, analyzed with mixed effects models, reveal ERP effects of modality-switching in four typical time windows set between 160 and 750 ms after word onset. The overall effect, which increases over time, is broadly characterized by a negativity for modality-switching compared to not switching. It arises with both types of switch, and influences both participant groups within anterior as well as posterior brain regions. To the extent that this effect spans the time course of lexico-semantic retrieval, it suggests that perceptual simulation contributes fundamentally to the comprehension of words.ReferencesHald, L. A., Marshall, J.-A., Janssen, D. P., & Garnham, A. (2011). Switching modalities in a sentence verification task: ERP evidence for embodied language processing. <i>Frontiers in Psychology, 2.</i> Hauk, O., Coutout, C., Holden, A., & Chen, Y. (2012). The time-course of single-word reading: Evidence from fast behavioral and brain responses. <i>Neuroimage</i>, <i>60</i>, 2, 1462-1477.Mahon, B. Z. (2015). What is embodied about cognition? <i>Language, Cognition and Neuroscience, 30</i>, 4, 420-429.
DOI: 10.7910/dvn/v1rjpf/kumuli
title: cbb20150730_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/pui6jy
title: cbb20151119_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xn55hy
title: cbb20150714_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tqvmlg
title: cbb20150531_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/heaaea
title: cbb20150120_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/n2bax0
title: cbb20150410_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/gdfzxz
title: cbb20151117_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/lfaxan
title: cbb20150223_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/qfz8z0
title: cbb20150102_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210308
title: hem.html

description: Heme
DOI: 10.6084/m9.figshare.4210308.v1
title: hem.html

description: Heme
DOI: 10.6084/m9.figshare.4210302
title: Portal Rodent Trapping Forecasts for November 2016

description: This is a forecast made for the upcoming November 4 - 6 rodent trapping at the long-term Portal research site in southern Arizona. Four models were used to achieve forecasts with 90% prediction intervals. Forecasts are for the site as a whole (total means across all plots, all treatment types).<br>Two naive models using the defaults in the forecast package in R were used to create forecasts for the total granivore captures (species="Total") over the next 12 trapping periods, the first being tomorrow.<br>The "Forecast" modelforecast(abundances$total,h=12,level=0.95,BoxCox.lambda(0),allow.multiplicative.trend=T)<br>The "AutoArima" modelforecast(auto.arima(abundances$total,lambda = 0),h=12,level=0.95,fan=T)<br>Species-level predictions, as well as another total captures prediction, were made using the tscount package in R.<br>The "NegBinom Time Series" model, by species (species codes are the first letter of the genus and species)<br>model=tsglm(abundances[[s]],model=list(past_obs=1,past_mean=11),distr="nbinom")<br>predict(model,12,level=0.9) <br>The "Poisson Environmental" model, by species (species codes are the first letter of the genus and species), using monthly mean temperature and total precipitation from our on-site weather station<br>model=tsglm(abundances[[s]],model=list(past_obs=1,past_mean=11),distr="poisson",xreg=weather)<br>predict(model,12,level=0.9,newdata=weatherforecast)
DOI: 10.7910/dvn/v1rjpf/97xuol
title: cbb20150320_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210302.v1
title: Portal Rodent Trapping Forecasts for November 2016

description: This is a forecast made for the upcoming November 4 - 6 rodent trapping at the long-term Portal research site in southern Arizona. Four models were used to achieve forecasts with 90% prediction intervals. Forecasts are for the site as a whole (total means across all plots, all treatment types).<br>Two naive models using the defaults in the forecast package in R were used to create forecasts for the total granivore captures (species="Total") over the next 12 trapping periods, the first being tomorrow.<br>The "Forecast" modelforecast(abundances$total,h=12,level=0.95,BoxCox.lambda(0),allow.multiplicative.trend=T)<br>The "AutoArima" modelforecast(auto.arima(abundances$total,lambda = 0),h=12,level=0.95,fan=T)<br>Species-level predictions, as well as another total captures prediction, were made using the tscount package in R.<br>The "NegBinom Time Series" model, by species (species codes are the first letter of the genus and species)<br>model=tsglm(abundances[[s]],model=list(past_obs=1,past_mean=11),distr="nbinom")<br>predict(model,12,level=0.9) <br>The "Poisson Environmental" model, by species (species codes are the first letter of the genus and species), using monthly mean temperature and total precipitation from our on-site weather station<br>model=tsglm(abundances[[s]],model=list(past_obs=1,past_mean=11),distr="poisson",xreg=weather)<br>predict(model,12,level=0.9,newdata=weatherforecast)
DOI: 10.7910/dvn/v1rjpf/acgac8
title: cbb20150226_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210299
title: Average rainfall rates from KGYX radar over Maine

description: Radar derived average rainfall rate as a function of time over the Portland, Maine region for over 10 years. VERY initial data, not quality controlled.<br>Data derived from NOAA/FAA/DoD NEXRAD radar. <br>
DOI: 10.7910/dvn/v1rjpf/ioxz2q
title: cbb20151220_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5qdi1r
title: cbb20150824_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/43kjke
title: cbb20151103_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xavlz0
title: cbb20150107_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fyre46
title: cbb20150519_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ayrwqr
title: cbb20150427_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210284
title: Life & Physical Sciences Training Workshop 2

description: Life & Physical Sciences Training
DOI: 10.7910/dvn/v1rjpf/opyc15
title: cbb20150607_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210284.v1
title: Life & Physical Sciences Training Workshop 2

description: Life & Physical Sciences Training
DOI: 10.7910/dvn/v1rjpf/i5jtdv
title: cbb20150830_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210281
title: Life & Physical Sciences Training Workshop 1

description: Life & Physical Sciences Training
DOI: 10.6084/m9.figshare.4210281.v1
title: Life & Physical Sciences Training Workshop 1

description: Life & Physical Sciences Training
DOI: 10.7910/dvn/v1rjpf/urnyzz
title: cbb20150415_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xq9hqf
title: cbb20151015_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/autbq6
title: cbb20150424_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210275
title: Social Science Training Workshop 2

description: Social Science Training Workshop 2
DOI: 10.7910/dvn/v1rjpf/sfvsrj
title: cbb20150610_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ozicea
title: cbb20150319_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210275.v1
title: Social Science Training Workshop 2

description: Social Science Training Workshop 2
DOI: 10.6084/m9.figshare.4210272
title: Social Science Training Workshop 1

description: Social Science Training Workshop 1
DOI: 10.7910/dvn/v1rjpf/gewxgo
title: cbb20150214_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210272.v1
title: Social Science Training Workshop 1

description: Social Science Training Workshop 1
DOI: 10.6084/m9.figshare.4210269
title: Social Science Training Discussion 2

description: Social Science Librarian training discussion
DOI: 10.7910/dvn/v1rjpf/tf83va
title: cbb20150303_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/mjfj58
title: cbb20151022_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210269.v2
title: Social Science Training Discussion 2

description: Social Science Librarian training discussion
DOI: 10.7910/dvn/v1rjpf/c7rr73
title: cbb20150209_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ewtclz
title: cbb20151110_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210269.v1
title: Social Science Discussion 2

description: Social Science Librarian training discussion
DOI: 10.7910/dvn/v1rjpf/jdytji
title: cbb20151228_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/txbd1k
title: cbb20150428_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/s7jdyh
title: cbb20151019_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/3ttkeg
title: cbb20150429_03.txt.gz

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DOI: 10.7910/dvn/v1rjpf/eairbp
title: cbb20150521_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210227
title: BIO205 2016 Tut0109 Census 2 Data.xlsx

description: Data set collected at University of Toronto Mississauga (UTM) campus, following protocol posted at http://www.campusecology.network/protcol/
DOI: 10.7910/dvn/v1rjpf/gj675o
title: cbb20150807_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210227.v1
title: BIO205 2016 Tut0109 Census 2 Data.xlsx

description: Data set collected at University of Toronto Mississauga (UTM) campus, following protocol posted at http://www.campusecology.network/protcol/
DOI: 10.6084/m9.figshare.4210218
title: BIO205 2016 Tut0108 Census 2 Data.xlsx

description: Data set collected at University of Toronto Mississauga (UTM) campus, following protocol posted at http://www.campusecology.network/protcol/
DOI: 10.7910/dvn/v1rjpf/uuwzl5
title: cbb20150403_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210218.v1
title: BIO205 2016 Tut0108 Census 2 Data.xlsx

description: Data set collected at University of Toronto Mississauga (UTM) campus, following protocol posted at http://www.campusecology.network/protcol/
DOI: 10.6084/m9.figshare.4210215
title: BIO205 2016 Tut0108 Census 1 Data.xlsx

description: Data set collected at University of Toronto Mississauga (UTM) campus, following protocol posted at http://www.campusecology.network/protcol/
DOI: 10.6084/m9.figshare.4210215.v1
title: BIO205 2016 Tut0108 Census 1 Data.xlsx

description: Data set collected at University of Toronto Mississauga (UTM) campus, following protocol posted at http://www.campusecology.network/protcol/
DOI: 10.7910/dvn/v1rjpf/sdp0tj
title: cbb20150106_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tav4xm
title: cbb20151203_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/0umezb
title: cbb20150524_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/u45xue
title: cbb20150301_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/bkdcyp
title: cbb20150103_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210200
title: Tensor Decomposition With Generalized Lasso Penalties

description: We present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multiway data. This generalizes existing work on sparse tensor decomposition and penalized matrix decompositions, in a manner parallel to the generalized lasso for regression and smoothing problems. Our approach presents many nontrivial challenges at the intersection of modeling and computation, which are studied in detail. An efficient coordinate-wise optimization algorithm for PTD is presented, and its convergence properties are characterized. The method is applied both to simulated data and real data on flu hospitalizations in Texas and motion-capture data from video cameras. These results show that our penalized tensor decomposition can offer major improvements on existing methods for analyzing multiway data that exhibit smooth spatial or temporal features.
DOI: 10.7910/dvn/v1rjpf/a0kk6t
title: cbb20150604_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/t38twg
title: cbb20150316_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.4210200.v2
title: Tensor Decomposition With Generalized Lasso Penalties

description: We present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multiway data. This generalizes existing work on sparse tensor decomposition and penalized matrix decompositions, in a manner parallel to the generalized lasso for regression and smoothing problems. Our approach presents many nontrivial challenges at the intersection of modeling and computation, which are studied in detail. An efficient coordinate-wise optimization algorithm for PTD is presented, and its convergence properties are characterized. The method is applied both to simulated data and real data on flu hospitalizations in Texas and motion-capture data from video cameras. These results show that our penalized tensor decomposition can offer major improvements on existing methods for analyzing multiway data that exhibit smooth spatial or temporal features.
DOI: 10.6084/m9.figshare.4210200.v1
title: Tensor decomposition with generalized lasso penalties

description: We present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multi-way data. This generalizes existing work on sparse tensor decomposition and penalized matrix decompositions, in a manner parallel to the generalized lasso for regression and smoothing problems. Our approach presents many nontrivial challenges at the intersection of modeling and computation, which are studied in detail. An efficient coordinate-wise optimization algorithm for PTD is presented, and its convergence properties are characterized. The method is applied both to simulated data and real data on flu hospitalizations in Texas and motion-capture data from video cameras. These results show that our penalized tensor decomposition can offer major improvements on existing methods for analyzing multi-way data that exhibit smooth spatial or temporal features.
DOI: 10.7910/dvn/v1rjpf/lmkomk
title: cbb20150704_03.txt.gz

description: :unav