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opencc-by-sa-4.0Aug 2017View details →

Scenario delay times regarding the expansion of the transmission system in Germany based on social acceptance

The data is closely related to Mester et al. 2017. Integrating Social Acceptance of Electricity Grid Expansion into Energy System Modeling: A Methodological Approach for Germany. In Wohlgemuth, V., Fuchs-Kittowski, F. and Wittmann, J. (eds.) Advances and New Trends in Environmental Informatics: Stability, Continuity, Innovation. Cham: Springer International Publishing, pp. 115 - 129. doi: 10.1007/978-3-319-44711-7_10. Each dataset contains the assumed delays in commissioning of German transmission grid projects given in years influenced by social acceptance based on three different scenarios - low, mid and high. In the attached file VerNetzen-Verzoegerungszeiten-Kreise.csv these delay times are given per district, which can be identified by their key, "Regionalschlüssel", and corresponding geo data (EPSG: 25832) of the administrative area provided by the Federal Agency for Cartography and Geodesy. © GeoBasis-DE / BKG 2014 (data was changed) Additionally other files contain scenario data for each transmission grid project. Geo data in these files is provided by the Bundesnetzagentur (data was changed). The dataset was created in the context of the interdisciplinary research project VerNetzen and is described in detail in the final project report: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., pp. 72-92, 102-105, 135-142. Deutsch: Die Daten stehen in engem Zusammenhang mit Mester et al. 2017. Integrating Social Acceptance of Electricity Grid Expansion into Energy System Modeling: A Methodological Approach for Germany. In Wohlgemuth, V., Fuchs-Kittowski, F. and Wittmann, J. (eds.) Advances and New Trends in Environmental Informatics: Stability, Continuity, Innovation. Cham: Springer International Publishing, pp. 115 - 129. doi: 10.1007/978-3-319-44711-7_10. Je Datensatz ist angegeben, welche akzeptanz-bedingten Verzögerungen in Jahren für die Inbetriebnahme von Übertragungsnetzausbauvorhaben in den drei Szenarien - low, mid und high - zu erwarten sind. In der Datei VerNetzen-Verzoegerungszeiten-Kreise.csv werden Verzögerungen je Landkreis aufgeführt. Diese können durch den Regionalschlüssel oder durch Geodaten (EPSG: 25832) des Bundesamtes für Kartographie und Geodäsie zugeordnet werden. © GeoBasis-DE / BKG 2014 (Daten geändert) Darüber hinaus enthalten die anderen Dateien Daten je Vorhaben. Die entsprechenden Geodaten in diesen Dateien wurden von der Bundesnetzagentur bereitgestellt (Daten geändert). Der Datensatz ist im Kontext des interdisziplinären Forschungsprojekts VerNetzen entstanden und ist ausführlich im Projektabschlussbericht beschrieben: VerNetzen Degel, M., Christ, M., Grünert, J., Becker, L., Wingenbach, C., Soethe, M., Bunke, W.-D., Mester, K., und Wiese, F. (2016). VerNetzen: Sozial-ökologische und technisch-ökonomische Modellierung von Entwicklungspfaden der Energiewende. IZT Berlin, Europa-Universität Flensburg, Deutsche Umwelthilfe e.V., S.72-92, S.102-105, S.135-142.

opencc-by-4.0Jun 2022View details →

Tiempo de publicación en revistas académicas latinoamericanas. © / Time delay in Latin American academic journals. An international comparative analysis

Cuadros comparativos sobre tiempos de aceptación y de publicación de revistas académicas latinoamericanas (Argentina, Brasil, Chile, Colombia y México) incluidas en Scielo. Comparative tables on acceptance and publication times of Latin American academic journals (Argentina, Brazil, Chile, Colombia and Mexico) included in Scielo.

opencc-by-4.0Jul 2024View details →

CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL

CESM2 Experiment names: <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> Details for files cesm2.[experiment name].SST.noise.nc <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207 For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374

Figure 5. Sensory score and period of storage for processed cheese-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

R2 was found to be 96.5 percent of the total variation as explained by sensory scores. Period of storage (days) for which the processed cheese has been in the shelf can be determined based on sensory score (Fig. 5).

Figure 4. Comparison of ASS and PSS for multilayer model R-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

TDNN models with single and multi layers were developed taking soluble nitrogen, pH, standard plate count, yeast & mould count, spore count as input parameters, and sensory score as output parameter for predicting the shelf life of processed cheese stored at 30o C. Mean Square Error, Root Mean Square Error, Coefficient of Determination and Nash - Sutcliffo Coefficient were used in order to compare the prediction ability of the developed TDNN models. Regression equations were developed for predicting the shelf life of processed cheese, which came out as 28.25 days. Since, predicted value is close to the experimentally determined shelf life of 30 days, hence from the study it can be concluded that TDNN artificial neural network models are quite efficient in predicting shelf life of processed cheese.

Figure 2. Training pattern of TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The Neural Network Toolbox under MATLAB software was used for developing the TDNN models. Training pattern of TDNN models is presented in Fig.2.

Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate count, yeast & mould count, and spore count were taken as input parameters, and sensory score as output parameter for developing TDNN single and multilayer models (Fig.1).

Figure 3. Comparison of ASS and PSS single layer model-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese

opencc-by-4.0Oct 2019View details →

Spatial distribution of random velocity inhomogeneities in the southern Aegean from inversion of S‐wave peak delay times - Dataset

This dataset contains supplementary files uploaded as part of the above journal article. 5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few records contain negative peak delay times due to change in waveform shape from filtering, these were excluded during further calculations. The other 4 sub-datasets contain files as well as the script to generate the results of Δlog tp , κ, εparam and P(ml ) as shown in figures 7, 8, 9 and 10 respectively of the main article. Sub-Dataset S1: File “ds01.csv” contains the list of peak delay times (tp ) in 2-4 Hz, 4-8 Hz, 8-16 Hz and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent origin time (in year-month-day’T’hour:minute:seconds.microseconds format), event latitude, event longitude, event depth, station code, station latitude, station longitude, tp in 2-4 Hz, tp in 4- 8 Hz, tp in 8-16 Hz and tp in 16-32 Hz in a sequential manner. Sub-Dataset S2: File “ds02.zip” contains four text files (nodes_24e.txt, nodes_48e.txt, and nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt) written in BASH. The text files contain Δlog tp values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands respectively. The columns in the text files represent node index, node latitude, node longitude, node depth and Δlog tp value in a sequential manner. The GMT script uses GSHHG coastline data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ . Once downloaded and extracted its path can be added to the variable “GDIR” at the beginning of the script. The GMT script file can be run to see the spatial distribution of Δlog t p using GMT-5 (Wessel et al., 2013) and above. Sub-Dataset S3: File “ds03.zip” contains four text files (kappa_f10.txt, kappa_f30.txt, kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The text files contain κ values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in the text files represent node latitude, node longitude and κ value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution of κ using GMT-5 (Wessel et al., 2013) and above. Sub-Dataset S4: File “ds04.zip” contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt, aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain εparam values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in the text files represent node latitude, node longitude and εparam value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution of εparam using GMT-5 (Wessel et al., 2013) and above. Sub-Dataset S5: File “ds05.zip” contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt, psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain psdf (P(ml )) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in the text files represent node latitude, node longitude and P(ml ) value of the node sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution of P(ml ) using GMT-5 (Wessel et al., 2013) and above.

opencc-by-4.0Aug 2022View details →

GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation

The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at  (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.

opencc-zeroApr 2022View details →

Memory array locations, delay times, and participant response

Deliberative decisions based on an accumulation of evidence over time depend on working-memory, and working memory has limitations, but how these limitations affect deliberative decision-making is not understood. We used human psychophysics to assess the impact of working-memory limitations on the fidelity of a continuous decision variable. Participants decided the average location of multiple visual targets. This computed, continuous decision variable degraded with time and capacity in a manner that depended critically on the strategy used to form the decision variable. This dependence reflected whether the decision variable was computed either: 1) immediately upon observing the evidence, and thus stored as a single value in memory; or 2) at the time of the report, and thus stored as multiple values in memory. These results provide important constraints on how the brain computes and maintains temporally dynamic decision variables. The following data is the responses and trial information collected from each subject in each condition: Simultaneous Perceived, Simultaneous Computed, Sequential Perceived, and Sequential Computed.

opencc-zeroDec 2022View details →

Low-temperature nights delay the timing of breeding in a wild songbird

Global climate change has posed widespread challenges to the ecological process critical to the fitness of many wild organisms, such as reproductive phenology. Many bird species have advanced their reproductive phenology in response to the increases in spring temperatures. However, the mechanism of how climate influences the timing of breeding is still often unclear in many species. We explored the relationship between the timing of breeding and spring temperatures based on 14 years of data on Hair-Crested Drongos (Dicrurus hottentottus) in the wild. By applying a 'sliding window' approach, we aimed to identify the time window and weather variable that best explains the timing of breeding at both population and individual levels. We found that the more nights with a minimum temperature below 17℃, around three weeks earlier than the peak reproduction, delayed the breeding time. Low night temperatures may force females to allocate more energy to thermoregulation and therefore physiologically constrain egg-laying. Although annual minimum and maximum temperatures have increased over the study period, the timing of breeding showed no trend as there was no change in the number of low-temperature nights in the relevant period across years. The repeatability of the laying date for individual females (R = 0.211) and across the years (R = 0.270) were low indicating that Drongos were flexible in adjusting their breeding phenology to environmental variation. These results suggest an effect of low night temperature on avian breeding phenology. This effect may apply commonly across bird species considering shared physiological constraints.

zenodo36/100

Time-to-Event analysis of factors influencing delay in discharge from a subacute Complex Discharge Unit during the first year of the pandemic (2020) in an Irish tertiary centre hospital

Figure S1: Forest plots 1 and 2 depicting Age and Gender strata associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited statistically significant results for individuals <65 years of age who had a delay in discharge due to complications from comorbidities; those in 65-75 years of age category, had prolonged LOS due to admission with frailty, falls and/or integrated rehabilitation needs; and 75-85 years of age category showed an association of at least 4 out of the 5 common delaying factors. Strata Gender exhibited a significant delay in discharge due to complications from comorbidities and patient-centred needs; in comparison to the female gender who also experienced a delay in discharge as a result of both factors alongside frailty, falls and/or integrated rehabilitation needs.[A. Complications/comorbidities prolonging discharge, B. Healthcare-associated infection, C. Frailty, falls and/or integrated rehabilitation needs, D. Patient-centred needs, E. Community services].  Figure S2: Forest plot 3 depicting Multimorbidity (MM) strata-associated Hazard ratio (Markers) estimates (95% Confidence Interval demonstrated by horizontal line) exhibited a significant delay in discharge due to complications from comorbidities, frailty, falls, and/or integrated rehabilitation and patient-centred needs in patients with ≤4 MM. In contrast patients with >4 MM experienced significant delays in discharge due to complications from comorbidities and patient-centred needs. [A. Complications/comorbidities prolonging discharge, B. Healthcare-associated infection, C. Frailty, falls and/or integrated rehabilitation needs, D. Patient-centred needs, E. Community Services].

opencc-by-4.0Jan 2023View details →

ClinicalTrials.gov36/100

Delayed Sleep Timing in Teens Study

ClinicalTrials.gov study NCT03806296. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

Early Versus Delayed Timing of Intervention in Patients With Acute Coronary Syndromes

ClinicalTrials.gov study NCT00552513. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov36/100

A Study of Effects of Delay in the First Bathing Time of Body Temperature and the Rate of Exclusive Breastfeeding

ClinicalTrials.gov study NCT05425394. IPD Sharing: NO. Countries: 1. Publications: 51.

closedIPD-NOFeb 2026View details →

publicJul 2025View details →

Position and time coordinates, performance, and latencies of delayed non-match to place trials performed by rats with silenced hippocampal inputs to RSC

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