The Experts below are selected from a list of 174 Experts worldwide ranked by ideXlab platform
Nicholas J Cox - One of the best experts on this subject based on the ideXlab platform.
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myaxis stata module to reorder categorical Variable by specified sort criterion
Statistical Software Components, 2021Co-Authors: Nicholas J CoxAbstract:yaxis maps an existing "categorical" Variable, meaning usually a Numeric Variable with integer codes and value labels, or equivalently a string Variable, to a new Variable with integer values 1 up and with value labels, sorted according to a specified criterion. The element "axis" arises from a leading application of the command. You have a categorical Variable that would define an axis of a graph, or one dimension of a table (the rows, or the columns, say), but the existing order of categories is not ideal. Some graph and table commands offer sorting on the fly, but this command may help wherever other commands do not offer that.
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ROMANTOARABIC: Stata module for converting roman numerals to arabic numbers
2011Co-Authors: Nicholas J CoxAbstract:romantoarabic creates a Numeric Variable arabicvar from a string Variable romanvar containing roman numerals following rules specified in the help. As of January 2011, this package is considered superseded by the roman package by the author of both.
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CQUANTILE: Stata module to generate corresponding quantiles
2005Co-Authors: Nicholas J CoxAbstract:cquantile generates corresponding quantiles, namely, those quantiles that would be shown on a quantile-quantile plot, as in qqplot. Given either two Numeric Variables, or one Numeric Variable and one grouping Variable defining two groups, two new Variables are generated containing the quantiles.
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SLICEPLOT: Stata module for time series or other plot in slices
2005Co-Authors: Nicholas J CoxAbstract:sliceplot produces a time series or other plot divided into a series of slices and then combined. The horizontal Variable may be any Numeric Variable, but the program is motivated by the case of time series plots that would benefit from a short and wide scale.
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EXTREMES: Stata module to list extreme values of a Variable
Statistical Software Components, 2003Co-Authors: Nicholas J CoxAbstract:extremes lists extreme values of a Numeric Variable. If any other Variables are also specified, these are also listed for the same observations. By default, the extremes are the 5 lowest and the 5 highest values. Optionally values may be identified as extremes according to their distance from the nearer quartile.
Kristin Yeager - One of the best experts on this subject based on the ideXlab platform.
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LibGuides: SPSS Tutorials: Descriptive Stats for One Numeric Variable (Explore)
2013Co-Authors: Kristin YeagerAbstract:Written and illustrated tutorials for the statistical software SPSS. In SPSS, the Explore procedure produces univariate descriptive statistics, as well as confidence intervals for the mean, normality tests, and plots.
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LibGuides: SPSS Tutorials: Descriptive Stats for One Numeric Variable (Frequencies)
2013Co-Authors: Kristin YeagerAbstract:Written and illustrated tutorials for the statistical software SPSS. When applied to scale Variables, the Frequencies procedure in SPSS can compute quartiles, percentiles, and other summary statistics. It can also create histograms with an estimated normal distribution overlaid on the graph.
Amit Kumar Kohli - One of the best experts on this subject based on the ideXlab platform.
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channel estimation and long range prediction of fast fading channels for adaptive ofdm system
International Journal of Electronics, 2018Co-Authors: Divneet Singh Kapoor, Amit Kumar KohliAbstract:This correspondence presents the channel estimation and long-range prediction technique for adaptive-orthogonal-frequency-division-multiplexing (AOFDM) system. The efficient channel loading is accomplished by feeding the accurately predicted channel-state-information (CSI) back to transmitter. The frequency-selective wireless fading channel is modelled as a tapped-delay-line-filter governed by a first-order autoregressive (AR1) process; and an adaptive channel estimator based on the generalised-Variable-step-size least-mean-square (GVSS-LMS) algorithm tracks AR1 correlation coefficient. To compensate for the signal fading due to channel state variations, a modified-Kalman-filter (MKF)-based channel estimator is utilised. In addition, channel tracking is also performed for predicting future CSI at receiver, based on the Numeric-Variable-forgetting-factor recursive-least-squares (NVFF-RLS) algorithm. Subsequently, adaptive bit allocation for AOFDM system is employed by using predicted CSI at transmi...
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Adaptive Filtering Techniques using Cyclic Prefix in OFDM Systems for Multipath Fading Channel Prediction
Circuits Systems and Signal Processing, 2016Co-Authors: Amit Kumar Kohli, Divneet Singh KapoorAbstract:This paper presents adaptive channel prediction techniques for wireless orthogonal frequency division multiplexing (OFDM) systems using cyclic prefix (CP). The CP not only combats intersymbol interference, but also precludes requirement of additional training symbols. The proposed adaptive algorithms exploit the channel state information contained in CP of received OFDM symbol, under the time-invariant and time-variant wireless multipath Rayleigh fading channels. For channel prediction, the convergence and tracking characteristics of conventional recursive least squares (RLS) algorithm, Numeric Variable forgetting factor RLS (NVFF-RLS) algorithm, Kalman filtering (KF) algorithm and reduced Kalman least mean squares (RK-LMS) algorithm are compared. The simulation results are presented to demonstrate that KF algorithm is the best available technique as compared to RK-LMS, RLS and NVFF-RLS algorithms by providing low mean square channel prediction error. But RK-LMS and NVFF-RLS algorithms exhibit lower computational complexity than KF algorithm. Under typical conditions, the tracking performance of RK-LMS is comparable to RLS algorithm. However, RK-LMS algorithm fails to perform well in convergence mode. For time-variant multipath fading channel prediction, the presented NVFF-RLS algorithm supersedes RLS algorithm in the channel tracking mode under moderately high fade rate conditions. However, under appropriate parameter setting in $$2\times 1$$ 2 × 1 space–time block-coded OFDM system, NVFF-RLS algorithm bestows enhanced channel tracking performance than RLS algorithm under static as well as dynamic environment, which leads to significant reduction in symbol error rate.
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Numeric Variable Forgetting Factor RLS Algorithm for Second-Order Volterra Filtering
Circuits Systems and Signal Processing, 2012Co-Authors: Amit Kumar Kohli, Amrita RaiAbstract:Nonlinear adaptive filtering techniques for system identification (based on the Volterra model) are widely used for the identification of nonlinearities in many applications. In this correspondence, the improved tracking capability of a Numeric Variable forgetting factor recursive least squares (NVFF-RLS) algorithm is presented for first-order and second-order time-varying Volterra systems under a nonstationary environment. The nonlinear system tracking problem is converted into a state estimation problem of the time-variant system. The time-varying Volterra kernels are governed by the first-order Gauss–Markov stochastic difference equation, upon which the state-space representation of this system is built. In comparison to the conventional fixed forgetting factor recursive least squares algorithm, the NVFF-RLS algorithm provides better channel estimation as well as channel tracking performance in terms of the minimum mean square error (MMSE) for first-order and second-order Volterra systems. The NVFF-RLS algorithm is adapted to the time-varying signals by using the updating prediction error criterion, which accounts for the nonstationarity of the signal. The demonstrated simulation results manifest that the proposed method has good adaptability in the time-varying environment, and it also reduces the computational complexity.
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Space-time block-coded systems using Numeric Variable forgetting factor least squares channel estimator
International Journal of the Physical Sciences, 2011Co-Authors: Amit Grover, Amit Kumar KohliAbstract:In this correspondence, the bit-error-rate (BER) performance evaluation of the space-time block-coded (STBC) communication systems using the Numeric-Variable-forgetting-factor (NVFF) least-squares (LS) channel estimator is presented. The polynomial channel paradigm is incorporated in LS algorithm in conjunction with NVFF to improve the channel tracking performance under the nonstationary wireless environment. The implementation of NVFF precludes the usage of LMS based VFF updating, and it also reduces the computational complexity. The simulation results are presented to demonstrate that the BER performance of STBC communication system using the linear least squares algorithm based channel estimator with NVFF outperforms the higher-order polynomial model-based and conventional VFF-based approaches, when M-ary QAM signal constellations are used for the wireless transmission. Key words: Space-time block-codes, least squares algorithm, Rayleigh fading, Markov process, Variable forgetting factor.
Ewers Robert - One of the best experts on this subject based on the ideXlab platform.
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Increased importance of terrestrial vertebrate seed dispersal in tropical logged forests
2020Co-Authors: Qie Lan, Telford Elizabeth, Nilus Reuben, Ewers RobertAbstract:Description: A large seed dispersal experiement combining seed tracking and camera trapping at ten forest sites along a wide gradient of historical logging disturbance with AGB ranging between 4.7 and 614.0 Mg ha-1, all part of the established SAFE mammal survey network. Each experiment was run for a consecutive five days using experimental seeds with different hardness (fleshy vs hard) and size (large vs small). Each seed was tracked with a spool. Project: This dataset was collected as part of the following SAFE research project: Resilience of Tropical Forest Ecosystem Processes to the Interactive Effects of El Nino and Forest Disturbance Funding: These data were collected as part of research funded by: Natural Environment Research Council (Directed grant, NE/P00363X/1, https://gtr.ukri.org/projects?ref=NE%2FP00363X%2F1) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: template_seed_experiment_LanQieJan9.xlsx template_seed_experiment_LanQieJan9.xlsx This file contains dataset metadata and 2 data tables: Seed fate (described in worksheet Seed fate) Description: seed fate and removal distance of 12000 experimental seeds of different treatments at all sites Number of fields: 14 Number of data rows: 12000 Fields: seed.id: Seed ID (Field type: ID) Grid: Experimental site ID, with the same Grid identifier used in the core SAFE project small mammal trapping work -- see SAFE dataset 256 "CORE SAFE PROJECT SMALL MAMMAL TRAPPING DATA" (Field type: ID) Point: experimental points, selected from the camera trap points in SAFE gazetteer (Field type: Location) Day: The day of the seed outcome. Each experimental point was set up on Day 1 and checked on Day 2-6. This Numeric Variable is used for temporal analysis. For treating Day as a random effect, an additional "date" label can be created by pasting Grid and Day. (Field type: Numeric) type: Experiment seed type. OP = oil palm fruit, PK1 = single pumpkin seed, PK10 = pumpkin seed cluster of 10, PK20 = pumpkin seed cluster of 20, PT = pistachio nut. For analysis, these were classified as fleshy (OP) vs hard (other seeds), and large (> 10 g; OP, PK10, PK20) vs small (< 10 g; PT, PK1). (Field type: Categorical) trmt: Experiment treatment. Control = seeds accessible by all animals, Cage = exclosure cage treatment with 10x10cm entrances where large vertebrates were excluded (Field type: Categorical) Distance: Seed removal distance (Field type: Numeric) Bearing: Compass bearing of the removed seed from experimental point (Field type: Numeric) Location: Location of removed seed. Free text can be grouped into categories for analysis. (Field type: Comments) fate: Seed fate. Untouched = intact and not moved. Uneaten = removed but uneaten (dispersed). Eaten = eaten or partially eaten. Unknown = seed dragged into burrows, nests or up trees with seed fate unknown, presumed eaten in analsysis to be conservative about seed dispersal (Field type: Categorical) Day.3: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.4: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.5: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.6: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Camera trap records (described in worksheet Camera trap records) Description: For each visit to seed experiment by animals recorded by camera traps, we recorded the functional group (large vertebrate or small vertebrate) and seed activity (eating or moving) Number of fields: 12 Number of data rows: 2594 Fields: Grid: Experimental site ID, with the same Grid identifier used in the core SAFE project small mammal trapping work -- see SAFE dataset 256 "CORE SAFE PROJECT SMALL MAMMAL TRAPPING DATA" (Field type: ID) Point: experimental points, selected from the camera trap points in SAFE gazetteer (Field type: Location) Day: The day of the camera trap record. Each experimental point was set up on Day 1 and checked on Day 2-6. This Numeric Variable is used for temporal analysis. For treating Day as a random effect, an additional "date" label can be created by pasting Grid and Day. (Field type: Numeric) trmt: Experiment treatment. Control = seeds accessible by all animals, Cage = exclosure cage treatment with 10x10cm entrances where large vertebrates were excluded (Field type: Categorical) func.group: functional group of the seed visitor (Field type: Categorical) aminal.comment: animal species if possible to identify on photos, with some level of uncertainty (Field type: Comments) visit.number: visit number of the apparent repeated visits by the same animal, with some level of uncertainty (Field type: Numeric) PIT.tag: the PIT tag number of tagged animals entering cage, detected by the antenna and recorded by the data logger (Field type: ID) estimated.body.size: body size estimate from photos, in mm, with some level of uncertainty (Field type: Comments) activity: observed interaction with seeds, with details in the next column. For analysis, "investigate" was not considered an active interaction. (Field type: Categorical) activity.comment: detailed comments on the activity (Field type: Comments) seed.type: the seed type(s) interacted with, if possible to determine, with some level of uncertainty (Field type: Categorical) Date range: 2017-03-01 to 2017-10-31 Latitudinal extent: 4.6881 to 4.7519 Longitudinal extent: 116.9633 to 117.593
Ewers R - One of the best experts on this subject based on the ideXlab platform.
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Increased importance of terrestrial vertebrate seed dispersal in tropical logged forests
2019Co-Authors: Telford E, Qie L, Nilus R, Ewers RAbstract:A large seed dispersal experiement combining seed tracking and camera trapping at ten forest sites along a wide gradient of historical logging disturbance with AGB ranging between 4.7 and 614.0 Mg ha-1, all part of the established SAFE mammal survey network. Each experiment was run for a consecutive five days using experimental seeds with different hardness (fleshy vs hard) and size (large vs small). Each seed was tracked with a spool. Project: This dataset was collected as part of the following SAFE research project: Resilience of Tropical Forest Ecosystem Processes to the Interactive Effects of El Nino and Forest Disturbance Funding: These data were collected as part of research funded by: Natural Environment Research Council (Directed grant, NE/P00363X/1, https://gtr.ukri.org/projects?ref=NE%2FP00363X%2F1) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: template_seed_experiment_LanQieJan9.xlsx template_seed_experiment_LanQieJan9.xlsx This file contains dataset metadata and 2 data tables: Seed fate (described in worksheet Seed fate) Description: seed fate and removal distance of 12000 experimental seeds of different treatments at all sites Number of fields: 14 Number of data rows: 12000 Fields: seed.id: Seed ID (Field type: ID) Grid: Experimental site ID, with the same Grid identifier used in the core SAFE project small mammal trapping work -- see SAFE dataset 256 "CORE SAFE PROJECT SMALL MAMMAL TRAPPING DATA" (Field type: ID) Point: experimental points, selected from the camera trap points in SAFE gazetteer (Field type: Location) Day: The day of the seed outcome. Each experimental point was set up on Day 1 and checked on Day 2-6. This Numeric Variable is used for temporal analysis. For treating Day as a random effect, an additional "date" label can be created by pasting Grid and Day. (Field type: Numeric) type: Experiment seed type. OP = oil palm fruit, PK1 = single pumpkin seed, PK10 = pumpkin seed cluster of 10, PK20 = pumpkin seed cluster of 20, PT = pistachio nut. For analysis, these were classified as fleshy (OP) vs hard (other seeds), and large (> 10 g; OP, PK10, PK20) vs small (< 10 g; PT, PK1). (Field type: Categorical) trmt: Experiment treatment. Control = seeds accessible by all animals, Cage = exclosure cage treatment with 10x10cm entrances where large vertebrates were excluded (Field type: Categorical) Distance: Seed removal distance (Field type: Numeric) Bearing: Compass bearing of the removed seed from experimental point (Field type: Numeric) Location: Location of removed seed. Free text can be grouped into categories for analysis. (Field type: Comments) fate: Seed fate. Untouched = intact and not moved. Uneaten = removed but uneaten (dispersed). Eaten = eaten or partially eaten. Unknown = seed dragged into burrows, nests or up trees with seed fate unknown, presumed eaten in analsysis to be conservative about seed dispersal (Field type: Categorical) Day.3: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.4: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.5: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.6: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Camera trap records (described in worksheet Camera trap records) Description: For each visit to seed experiment by animals recorded by camera traps, we recorded the functional group (large vertebrate or small vertebrate) and seed activity (eating or moving) Number of fields: 12 Number of data rows: 2594 Fields: Grid: Experimental site ID, with the same Grid identifier used in the core SAFE project small mammal trapping work -- see SAFE dataset 256 "CORE SAFE PROJECT SMALL MAMMAL TRAPPING DATA" (Field type: ID) Point: experimental points, selected from the camera trap points in SAFE gazetteer (Field type: Location) Day: The day of the camera trap record. Each experimental point was set up on Day 1 and checked on Day 2-6. This Numeric Variable is used for temporal analysis. For treating Day as a random effect, an additional "date" label can be created by pasting Grid and Day. (Field type: Numeric) trmt: Experiment treatment. Control = seeds accessible by all animals, Cage = exclosure cage treatment with 10x10cm entrances where large vertebrates were excluded (Field type: Categorical) func.group: functional group of the seed visitor (Field type: Categorical) aminal.comment: animal species if possible to identify on photos, with some level of uncertainty (Field type: Comments) visit.number: visit number of the apparent repeated visits by the same animal, with some level of uncertainty (Field type: Numeric) PIT.tag: the PIT tag number of tagged animals entering cage, detected by the antenna and recorded by the data logger (Field type: ID) estimated.body.size: body size estimate from photos, in mm, with some level of uncertainty (Field type: Comments) activity: observed interaction with seeds, with details in the next column. For analysis, "investigate" was not considered an active interaction. (Field type: Categorical) activity.comment: detailed comments on the activity (Field type: Comments) seed.type: the seed type(s) interacted with, if possible to determine, with some level of uncertainty (Field type: Categorical) Date range: 2017-03-01 to 2017-10-31 Latitudinal extent: 4.6881 to 4.7519 Longitudinal extent: 116.9633 to 117.5934A large seed dispersal experiement combining seed tracking and camera trapping at ten forest sites along a wide gradient of historical logging disturbance with AGB ranging between 4.7 and 614.0 Mg ha-1, all part of the established SAFE mammal survey network. Each experiment was run for a consecutive five days using experimental seeds with different hardness (fleshy vs hard) and size (large vs small). Each seed was tracked with a spool. Project: This dataset was collected as part of the following SAFE research project: Resilience of Tropical Forest Ecosystem Processes to the Interactive Effects of El Nino and Forest Disturbance Funding: These data were collected as part of research funded by: Natural Environment Research Council (Directed grant, NE/P00363X/1, https://gtr.ukri.org/projects?ref=NE%2FP00363X%2F1) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: template_seed_experiment_LanQieJan9.xlsx template_seed_experiment_LanQieJan9.xlsx This file contains dataset metadata and 2 data tables: Seed fate (described in worksheet Seed fate) Description: seed fate and removal distance of 12000 experimental seeds of different treatments at all sites Number of fields: 14 Number of data rows: 12000 Fields: seed.id: Seed ID (Field type: ID) Grid: Experimental site ID, with the same Grid identifier used in the core SAFE project small mammal trapping work -- see SAFE dataset 256 "CORE SAFE PROJECT SMALL MAMMAL TRAPPING DATA" (Field type: ID) Point: experimental points, selected from the camera trap points in SAFE gazetteer (Field type: Location) Day: The day of the seed outcome. Each experimental point was set up on Day 1 and checked on Day 2-6. This Numeric Variable is used for temporal analysis. For treating Day as a random effect, an additional "date" label can be created by pasting Grid and Day. (Field type: Numeric) type: Experiment seed type. OP = oil palm fruit, PK1 = single pumpkin seed, PK10 = pumpkin seed cluster of 10, PK20 = pumpkin seed cluster of 20, PT = pistachio nut. For analysis, these were classified as fleshy (OP) vs hard (other seeds), and large (> 10 g; OP, PK10, PK20) vs small (< 10 g; PT, PK1). (Field type: Categorical) trmt: Experiment treatment. Control = seeds accessible by all animals, Cage = exclosure cage treatment with 10x10cm entrances where large vertebrates were excluded (Field type: Categorical) Distance: Seed removal distance (Field type: Numeric) Bearing: Compass bearing of the removed seed from experimental point (Field type: Numeric) Location: Location of removed seed. Free text can be grouped into categories for analysis. (Field type: Comments) fate: Seed fate. Untouched = intact and not moved. Uneaten = removed but uneaten (dispersed). Eaten = eaten or partially eaten. Unknown = seed dragged into burrows, nests or up trees with seed fate unknown, presumed eaten in analsysis to be conservative about seed dispersal (Field type: Categorical) Day.3: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.4: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.5: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Day.6: Fate of dispersed seeds on subsequent days, Day.3 - Day.6, where applicable, e.g. a seed dispersed on Day 4 would only be monitored on Day 5 and Day 6. Also, not all dispersed seeds could be practically monitored. (Field type: Categorical) Camera trap records (described in worksheet Camera trap records) Description: For each visit to seed experiment by animals recorded by camera traps, we recorded the functional group (large vertebrate or small vertebrate) and seed activity (eating or moving) Number of fields: 12 Number of data rows: 2594 Fields: Grid: Experimental site ID, with the same Grid identifier used in the core SAFE project small mammal trapping work -- see SAFE dataset 256 "CORE SAFE PROJECT SMALL MAMMAL TRAPPING DATA" (Field type: ID) Point: experimental points, selected from the camera trap points in SAFE gazetteer (Field type: Location) Day: The day of the camera trap record. Each experimental point was set up on Day 1 and checked on Day 2-6. This Numeric Variable is used for temporal analysis. For treating Day as a random effect, an additional "date" label can be created by pasting Grid and Day. (Field type: Numeric) trmt: Experiment treatment. Control = seeds accessible by all animals, Cage = exclosure cage treatment with 10x10cm entrances where large vertebrates were excluded (Field type: Categorical) func.group: functional group of the seed visitor (Field type: Categorical) aminal.comment: animal species if possible to identify on photos, with some level of uncertainty (Field type: Comments) visit.number: visit number of the apparent repeated visits by the same animal, with some level of uncertainty (Field type: Numeric) PIT.tag: the PIT tag number of tagged animals entering cage, detected by the antenna and recorded by the data logger (Field type: ID) estimated.body.size: body size estimate from photos, in mm, with some level of uncertainty (Field type: Comments) activity: observed interaction with seeds, with details in the next column. For analysis, "investigate" was not considered an active interaction. (Field type: Categorical) activity.comment: detailed comments on the activity (Field type: Comments) seed.type: the seed type(s) interacted with, if possible to determine, with some level of uncertainty (Field type: Categorical) Date range: 2017-03-01 to 2017-10-31 Latitudinal extent: 4.6881 to 4.7519 Longitudinal extent: 116.9633 to 117.593
