cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1219385.v2
title: Integration of Information and Communication Technologies (ICTs) in Teacher Education Programs in Pakistan

description: The research was funded and supervised by USAID teacher Education project for Pakistan. This research was aimed to investigate ‘what accounts more for ICTs implementation and integration’ in teacher training programs in the Khyber Pakthunkhwa and Punjab provinces in Pakistan. It was the first time that the Government of Pakistan realized the importance of ICTs and reflected its integration in teacher education programs as policy matter in the National Education Policy 2009. The course “ICTs in Education” was implemented for ADE/B. Ed (E) classes in 2010-11 for the first time. In this situation, it was vital to explore how successfully this course was implemented and ICTs were integrated in the pioneer institutions, so as to have baseline information for further improvement in the implementation process.
DOI: 10.6084/m9.figshare.1219431.v3
title: GPS data (.gpx) for litter survey 9/07/14: Snowdon Real 3 Peaks Challenge Litter Pick

description: Locations of litter found during a litter-pick during the 3 Peaks Challenge in Snowdonia, on 4th September 2014. These data have been provided by the Snowdonia Society to illustrate the amount and locations of littering along walking routes within the National Park.
DOI: 10.6084/m9.figshare.1219431.v5
title: GPS data (.gpx) for litter survey 9/07/14: Snowdon Litter Pick Workday

description: Locations of litter found during a litter-pick during the Snowdon Litter Pick Workday in Snowdonia, on 9th July 2014. These data have been provided by the Snowdonia Society to illustrate the amount and locations of littering along walking routes within the National Park.
DOI: 10.6084/m9.figshare.1219431.v4
title: GPS data (.gpx) for litter survey 9/07/14: Snowdon Litter Pick

description: Locations of litter found during a litter-pick during the 3 Peaks Challenge in Snowdonia, on 4th September 2014. These data have been provided by the Snowdonia Society to illustrate the amount and locations of littering along walking routes within the National Park.
DOI: 10.6084/m9.figshare.1219373
title: 2014 Env Microbiol, README on Supplement

description: README file containing details on supplementary information.
DOI: 10.6084/m9.figshare.1219373.v1
title: 2014 Env Microbiol, README on Supplement

description: README file containing details on supplementary information.
DOI: 10.6084/m9.figshare.1219371
title: Seasonal patterns of mixed species groups in large East African mammals

description: Dataset for Seasonal patterns of mixed species groups in large East African mammals by Christian Kiffner, John Kioko, Cecilia Leweri, Stefan Krause
DOI: 10.6084/m9.figshare.1219369
title: 2014 Env Microbiol, Supplementary Tables (S1-S9)

description: # Table_S1.pdf<br>Composition of an artificial ‘local’ dataset of broad ecological range (BER). A total of 53,999 16S sequences from 18 studies were selected to generate the dataset; additional information, and detailed references are provided in the table. # Table_S2.xlsx<br>Total OTU counts per method when clustering a global dataset of 887,870 16S sequences. OTU counts are given per method for different thresholds. For UPARSE, the respective number of clustered sequences that mapped to the UCHIME-filtered dataset is also provided (as UPARSE implements differential on-the-fly chimera filtering, removing different sets of sequences at different clustering thresholds). # Table_S3.xlsx<br>Table S3. Estimates of α- and β-diversity when clustering the HSM dataset to 97% sequence similarity according to different methods. The table provides raw data of diversity estimates across 21 skin samples for every method. Moreover, trends between methods (Pearson correlation of diversity estimates), absolute shifts (as binary log-ratio) and shift significance (as p-values in one-sided Mann-Whitney-U tests) are also provided for every index. Finally, an overview of the HSM dataset (sequence counts and internal sequence similarities per habitat), as well as pairwise partition similarities between methods at 97% clustering are also provided. # Table_S4.xlsx<br>Adjusted Mutual Information (AMI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S1. # Table_S5.xlsx<br>Normalized Mutual Information (NMI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S2. # Table_S6.xlsx<br>Adjusted Rand Index (ARI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S3. # Table_S7.xlsx<br>Adjusted Mutual Information (AMI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure 4 in the main text. Missing values for AL at thresholds &lt;92% and UCLUST / UPARSE &gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1). # Table_S8.xlsx<br>Normalized Mutual Information (NMI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure S4. Missing values for AL at thresholds &lt;92% and UCLUST / UPARSE &gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1). # Table_S9.xlsx<br>Adjusted Rand Index (ARI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure S5. Missing values for AL at thresholds &lt;92% and UCLUST / UPARSE &gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1).
DOI: 10.6084/m9.figshare.1219371.v1
title: Seasonal patterns of mixed species groups in large East African mammals

description: Dataset for Seasonal patterns of mixed species groups in large East African mammals by Christian Kiffner, John Kioko, Cecilia Leweri, Stefan Krause
DOI: 10.6084/m9.figshare.1219364
title: Effects of Human Trash on Invertebrate's Abundance, Diversity, and Density in Woodlot

description: For this field experiment, all data were collected in York University's Keele campus' Danby woodlot on Tuesday October 14, 2014 at around 2:30PM to 4:00PM in the afternoon. The weather forecast for that time of the day was cloudy (cloud cover of 100%) with a high of 22 degrees Celsius and a high chance of rain; wind speed at 10km/h South and a humidity of 72% (according to http://www.timeanddate.com/weather/canada/toronto/historic). During the duration of the data collection, very light precipitation was observed in the woodlot. This field experiment was led and supervised by Taylor Noble with the data collection for the woodlot conducted by Jason and Leron. We utilized a 1m x 1m quadrat to take our 30 randomly selected different samples. 15 quadrat samples were taken in the areas that were affected by human trash and 15 quadrat samples in the areas that were not affected. The basis for this was that we considered an area to be affected with human trash if it had a trash coverage of more than 20%. Only one person was assigned to do this estimation, which was Leron, to give us a better consistency in the estimation. We did not consider broken tree branches and dead leaves as trash, only human induced items were accounted for (objects that are not usually found in that woodlot environment). On the other hand, if the area studied had less than 20% human trash coverage, that sample was considered to be not affected. We recorded the number of invertebrates, the number of different types or invertebrates that we observed in each quadrat sample and their names. For this experiment, anything inside the quadrat as well as on top of the quadrat equipment were accounted for. We used gloves to gently remove the leaves and trash in the sample area. We also inspected for any invertebrates that were stuck on these leaves and trash and included them in our count. For invertebrates with high abundance, like the ants mostly, we divided the quadrat into 4 sections and we just counted one of the sections and estimated the abundance by multiplying by 4. We did not include any dead invertebrates in our data however. We only counted all the invertebrates that were visible on the surface after the removal of the trash and leaves and or broken branches. We did not dig into the soil to study what invertebrates were inside. The composition of the human trash was also recorded. We tried to generalize most of the trash into a single item that they belonged to unless of course there was only one of that item then we specifically indicated what it was. For example, candy wrappers included Halls wrapper, lickerish wrapper, Mars wrapper, Oh Henry wrapper, etc. So anything that was considered a candy/chocolate treat was listed as a candy wrapper.
DOI: 10.6084/m9.figshare.1219369.v1
title: 2014 Env Microbiol, Supplementary Tables (S1-S9)

description: # Table_S1.pdf<br>Composition of an artificial ‘local’ dataset of broad ecological range (BER). A total of 53,999 16S sequences from 18 studies were selected to generate the dataset; additional information, and detailed references are provided in the table. # Table_S2.xlsx<br>Total OTU counts per method when clustering a global dataset of 887,870 16S sequences. OTU counts are given per method for different thresholds. For UPARSE, the respective number of clustered sequences that mapped to the UCHIME-filtered dataset is also provided (as UPARSE implements differential on-the-fly chimera filtering, removing different sets of sequences at different clustering thresholds). # Table_S3.xlsx<br>Table S3. Estimates of α- and β-diversity when clustering the HSM dataset to 97% sequence similarity according to different methods. The table provides raw data of diversity estimates across 21 skin samples for every method. Moreover, trends between methods (Pearson correlation of diversity estimates), absolute shifts (as binary log-ratio) and shift significance (as p-values in one-sided Mann-Whitney-U tests) are also provided for every index. Finally, an overview of the HSM dataset (sequence counts and internal sequence similarities per habitat), as well as pairwise partition similarities between methods at 97% clustering are also provided. # Table_S4.xlsx<br>Adjusted Mutual Information (AMI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S1. # Table_S5.xlsx<br>Normalized Mutual Information (NMI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S2. # Table_S6.xlsx<br>Adjusted Rand Index (ARI) between methods across thresholds when clustering the HSM dataset. Values as shown in Figure S3. # Table_S7.xlsx<br>Adjusted Mutual Information (AMI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure 4 in the main text. Missing values for AL at thresholds &lt;92% and UCLUST / UPARSE &gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1). # Table_S8.xlsx<br>Normalized Mutual Information (NMI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure S4. Missing values for AL at thresholds &lt;92% and UCLUST / UPARSE &gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1). # Table_S9.xlsx<br>Adjusted Rand Index (ARI) between methods across thresholds when clustering the global dataset of 887,870 16S sequences. Values as shown in Figure S5. Missing values for AL at thresholds &lt;92% and UCLUST / UPARSE &gt;99% as clustering to these thresholds was prohibited by memory requirements (see Text S1).
DOI: 10.6084/m9.figshare.1219364.v1
title: Effects of Human Trash on Invertebrate's Abundance, Diversity, and Density in Woodlot

description: For this field experiment, all data were collected in York University's Keele campus' Danby woodlot on Tuesday October 14, 2014 at around 2:30PM to 4:00PM in the afternoon. The weather forecast for that time of the day was cloudy (cloud cover of 100%) with a high of 22 degrees Celsius and a high chance of rain; wind speed at 10km/h South and a humidity of 72% (according to http://www.timeanddate.com/weather/canada/toronto/historic). During the duration of the data collection, very light precipitation was observed in the woodlot. This field experiment was led and supervised by Taylor Noble with the data collection for the woodlot conducted by Jason and Leron. We utilized a 1m x 1m quadrat to take our 30 randomly selected different samples. 15 quadrat samples were taken in the areas that were affected by human trash and 15 quadrat samples in the areas that were not affected. The basis for this was that we considered an area to be affected with human trash if it had a trash coverage of more than 20%. Only one person was assigned to do this estimation, which was Leron, to give us a better consistency in the estimation. We did not consider broken tree branches and dead leaves as trash, only human induced items were accounted for (objects that are not usually found in that woodlot environment). On the other hand, if the area studied had less than 20% human trash coverage, that sample was considered to be not affected. We recorded the number of invertebrates, the number of different types or invertebrates that we observed in each quadrat sample and their names. For this experiment, anything inside the quadrat as well as on top of the quadrat equipment were accounted for. We used gloves to gently remove the leaves and trash in the sample area. We also inspected for any invertebrates that were stuck on these leaves and trash and included them in our count. For invertebrates with high abundance, like the ants mostly, we divided the quadrat into 4 sections and we just counted one of the sections and estimated the abundance by multiplying by 4. We did not include any dead invertebrates in our data however. We only counted all the invertebrates that were visible on the surface after the removal of the trash and leaves and or broken branches. We did not dig into the soil to study what invertebrates were inside. The composition of the human trash was also recorded. We tried to generalize most of the trash into a single item that they belonged to unless of course there was only one of that item then we specifically indicated what it was. For example, candy wrappers included Halls wrapper, lickerish wrapper, Mars wrapper, Oh Henry wrapper, etc. So anything that was considered a candy/chocolate treat was listed as a candy wrapper.
DOI: 10.6084/m9.figshare.1219361
title: A randomized orthogonal array-based procedure for the estimation of first- and second-order Sobol' indices

description: In variance-based sensitivity analysis, the method of Sobol' [Sensitivity analysis for nonlinear mathematical models. Math Model Comput Exp. 1993;1:407–414] allows one to compute Sobol' indices (SI) using the Monte Carlo integration. One of the main drawbacks of this approach is that estimating SI requires a number of simulations which is dependent on the dimension of the model of interest. For example, estimating all the first- or second-order SI of a <i>d</i>-dimensional function basically requires or independent input vectors, respectively. Some interesting combinatorial results have been introduced to weaken this defect, in particular by Saltelli [Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun. 2002;145:280–297] and more recently by Owen [Variance components and generalized Sobol' indices. SIAM/ASA J Uncertain Quantification. 2013;1:19–41], but the quantities they estimate still depend linearly on the dimension <i>d</i>. In this paper, we introduce a new approach to estimate all the first- and second-order SI by using only two input vectors. We establish theoretical properties of such a method for the estimation of first-order SI and discuss the generalization to higher order indices. In particular, we prove on numerical examples that this procedure is tractable and competitive for the estimation of all first- and second-order SI. As an illustration, we propose to apply this new approach to a marine ecosystem model of the Ligurian sea (northwestern Mediterranean) in order to study the relative importance of its several parameters. The calibration process of this kind of chemical simulators is well known to be quite intricate, and a rigorous and robust – that is, valid without strong regularity assumptions – sensitivity analysis, as the method of Sobol' provides, could be of great help. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1219348
title: ADP data - Guadiana Estuary

description: The datasets are extensively described in: Garel E, Ferreira Ó, 2015. Multi-year high-frequency physical and environmental observations at the Guadiana Estuary. Earth System Science Data, 8(2), 567-586, doi: 10.5194/essdd-8-567-2015.
DOI: 10.6084/m9.figshare.1219349
title: River discharge to the Guadiana Estuary

description: River discharge data (m3/s) from March 2008 to April 2014 at 3 hydrographic stations near the head of the Guadiana Estuary. The data source is the Portuguese Water Institute (INAG; presently APA, Portuguese Environment Agency) website (http://snirh.pt). The dataset is extensively described in: Garel E, Ferreira Ó, 2015. Multi-year high-frequency physical and environmental observations at the Guadiana Estuary. Earth System Science Data, 8(2), 567-586, doi: 10.5194/essdd-8-567-2015.
DOI: 10.6084/m9.figshare.1219361.v2
title: A randomized orthogonal array-based procedure for the estimation of first- and second-order Sobol' indices

description: In variance-based sensitivity analysis, the method of Sobol' [Sensitivity analysis for nonlinear mathematical models. Math Model Comput Exp. 1993;1:407–414] allows one to compute Sobol' indices (SI) using the Monte Carlo integration. One of the main drawbacks of this approach is that estimating SI requires a number of simulations which is dependent on the dimension of the model of interest. For example, estimating all the first- or second-order SI of a <i>d</i>-dimensional function basically requires or independent input vectors, respectively. Some interesting combinatorial results have been introduced to weaken this defect, in particular by Saltelli [Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun. 2002;145:280–297] and more recently by Owen [Variance components and generalized Sobol' indices. SIAM/ASA J Uncertain Quantification. 2013;1:19–41], but the quantities they estimate still depend linearly on the dimension <i>d</i>. In this paper, we introduce a new approach to estimate all the first- and second-order SI by using only two input vectors. We establish theoretical properties of such a method for the estimation of first-order SI and discuss the generalization to higher order indices. In particular, we prove on numerical examples that this procedure is tractable and competitive for the estimation of all first- and second-order SI. As an illustration, we propose to apply this new approach to a marine ecosystem model of the Ligurian sea (northwestern Mediterranean) in order to study the relative importance of its several parameters. The calibration process of this kind of chemical simulators is well known to be quite intricate, and a rigorous and robust – that is, valid without strong regularity assumptions – sensitivity analysis, as the method of Sobol' provides, could be of great help. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1219361.v1
title: A randomized orthogonal array-based procedure for the estimation of first- and second-order Sobol' indices

description: In variance-based sensitivity analysis, the method of Sobol' [Sensitivity analysis for nonlinear mathematical models. Math Model Comput Exp. 1993;1:407–414] allows one to compute Sobol' indices (SI) using the Monte Carlo integration. One of the main drawbacks of this approach is that estimating SI requires a number of simulations which is dependent on the dimension of the model of interest. For example, estimating all the first- or second-order SI of a <i>d</i>-dimensional function basically requires or independent input vectors, respectively. Some interesting combinatorial results have been introduced to weaken this defect, in particular by Saltelli [Making best use of model evaluations to compute sensitivity indices. Comput Phys Commun. 2002;145:280–297] and more recently by Owen [Variance components and generalized Sobol' indices. SIAM/ASA J Uncertain Quantification. 2013;1:19–41], but the quantities they estimate still depend linearly on the dimension <i>d</i>. In this paper, we introduce a new approach to estimate all the first- and second-order SI by using only two input vectors. We establish theoretical properties of such a method for the estimation of first-order SI and discuss the generalization to higher order indices. In particular, we prove on numerical examples that this procedure is tractable and competitive for the estimation of all first- and second-order SI. As an illustration, we propose to apply this new approach to a marine ecosystem model of the Ligurian sea (northwestern Mediterranean) in order to study the relative importance of its several parameters. The calibration process of this kind of chemical simulators is well known to be quite intricate, and a rigorous and robust – that is, valid without strong regularity assumptions – sensitivity analysis, as the method of Sobol' provides, could be of great help. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1219334
title: Probe multiparametric data - Guadiana Estuary

description: Temperature, turbidity, salinity, chlorophyll, dissolved oxygen and pH measured about 1m below the surface every hour. The datasets are extensively described in: Garel E, Ferreira Ó, 2015. Multi-year high-frequency physical and environmental observations at the Guadiana Estuary. Earth System Science Data, 8(2), 567-586, doi: 10.5194/essdd-8-567-2015.
DOI: 10.6084/m9.figshare.1219348.v2
title: ADP data

description: Pressure, velocity and temperature records from an Acoustic Doppler current Profiler, every 15 min. Cell size=0.8 m, 10 cells.
DOI: 10.6084/m9.figshare.1219349.v1
title: River discharge to the Guadiana Estuary

description: hourly river discharge data (m3/s) from March 2008 to April 2014. The discharge data were derived from 2 automatic hydrographic stations of the Portuguese Water Institute (INAG): Pulo de Lobo and Ponte Quintos, located at about 20 and 40 km from the head of the Guadiana Estuary respectively, and representing about 80 % of the river inputs to the estuary.
DOI: 10.6084/m9.figshare.1219349.v3
title: River discharge to the Guadiana Estuary

description: River discharge data (m3/s) from March 2008 to April 2014 at 3 hydrographic stations near the head of the Guadiana Estuary. The data source is the Portuguese Water Institute (INAG; presently APA, Portuguese Environment Agency) website (http://snirh.pt). The dataset is extensively described in: Garel E, Ferreira Ó, 2015. Multi-year high-frequency physical and environmental observations at the Guadiana Estuary. Earth System Science Data, 8(2), 567-586, doi: 10.5194/essdd-8-567-2015.
DOI: 10.6084/m9.figshare.1219348.v3
title: ADP data

description: Pressure, velocity and temperature records from an Acoustic Doppler current Profiler, every 15 min. Cell size=0.8 m, 10 cells.
DOI: 10.6084/m9.figshare.1219334.v2
title: Probe multiparametric data

description: Temperature, turbidity, salinity, chlorophyll, dissolved oxygen and pH measured about 1m below the surface every hour.