Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

THE ROLE OF PROPERTY MANAGEMENT IN PROMOTING ENERGY-EFFICIENT SOLUTIONS FOR RENTALS

Real estate management plays a key role in promoting energy efficient solutions when renting out properties. The purpose of the study is to analyze the impact of management companies on the introduction of energy-efficient technologies to increase the competitiveness of facilities and reduce operating costs. The methodology is based on the analysis of data on the application of modern energy-efficient solutions, including lighting, heating and automation systems in buildings in the Czech Republic. The results showed that the use of such technologies helps to reduce utility costs by 20-40% and increases the attractiveness of facilities for tenants. In conclusion, property management aimed at energy efficiency ensures the achievement of sustainable development and economic benefits for owners and tenants. These measures increase the market value of the properties and extend the lease terms, which strengthens the position in the real estate rental market.

opencc-by-4.0Oct 2024View details →

CS#1H Monitoring Data of Subsurface Passage in Managed Aquifer Recharge: Microbial and Organic Composition Analysis

<div>This dataset contains flow cytometry and cultivation-based microbial data, along with measurements of natural organic matter (NOM) characterized by fluorescence, absorbance, and liquid chromatography-organic carbon detection (LC-OCD) from CS#1H. Data were collected over a one-year monitoring period at two sampling locations: the infiltration ditch and the abstraction well. </div>

opencc-by-4.0Nov 2024View details →

THE IMPACT OF PAEDIATRIC EMERGENCY MANAGEMENT SKILLS ON PATIENT OUTCOMES IN ZARIA

Paediatric emergencies represent a critical challenge in Nigeria, where approximately 2,300 children under the age of five die daily, predominantly from preventable and treatable conditions such as pneumonia, malaria, and diarrheal diseases. Mortality rates are disproportionately higher in rural areas, where primary health care (PHC) centers often serve as the only point of access to medical services. The effectiveness of these centers is largely dependent on the emergency management skills of their healthcare workers. Despite their importance, there has been limited research on how the skills of PHC workers in managing paediatric emergencies impact patient outcomes. This study aimed to assess the proficiency of healthcare workers in primary healthcare centers in Zaria in managing common paediatric emergencies. A cross-sectional descriptive study was conducted among 139 health workers selected through a multi-stage sampling technique in Sabon Gari and Zaria Local Government Areas. Data collection was carried out using a pre-tested semi-structured self-administered questionnaire. The data were analyzed using SPSS version 21, and associations were tested using the chi-square test, with results presented in tables and charts. The findings revealed a significant deficiency in the emergency management skills of healthcare workers. A majority of respondents demonstrated inadequate skills in handling paediatric emergencies, with 66 (48.5%) exhibiting very poor competency in the management of common paediatric emergencies, and 32 (23.5%) displaying poor competency. Only a small fraction, 4 (2.9%) of respondents, demonstrated excellent skills in emergency management. Similarly, 46.7% of the respondents had very poor skills in the treatment of common paediatric emergencies, while only 3.0% exhibited excellent treatment skills. A statistically significant relationship was observed between the level of academic qualification and emergency management skills (p < 0.05). The results underscore the critical need to enhance the skill levels of healthcare workers in PHCs through continuous professional development. This could be achieved through collaborative efforts between the Department of Paediatrics at Ahmadu Bello University Teaching Hospital and the Primary Health Care Departments of Zaria and Sabon Gari local governments, focusing on regular training and workshops. Improving the skills of healthcare workers in managing paediatric emergencies is essential for reducing child mortality and improving health outcomes in the region.

opencc-by-4.0Jun 2024View details →

WONDERBREAD: A Benchmark + Dataset for Business Process Management (BPM) Tasks

Paper: <a href="https://arxiv.org/abs/2406.13264">WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks</a> <h2>Background</h2> The WONDERBREAD dataset contains 2,928 human demonstrations of 598 web navigation workflows across 6 types of BPM tasks. These tasks measure the ability of a model to generate accurate documentation, assist in knowledge transfer, and improve the efficiency of workflows. Please see our website for more details: <a href="https://wonderbread.stanford.edu/">https://wonderbread.stanford.edu/</a> <h2>Quick Start</h2> To start, download debug_demos.zip (1 GB). It contains a subset of 24 demonstrations which can give you a sense of how the dataset is structured. To reproduce the paper, download gold_demos.zip (33 GB). It contains 724 demonstrations corresponding to the 162 "Gold" tasks which were used for all the evaluations in the original paper. To obtain the full dataset, download demos.zip (133 GB). This contains all 2,928 demonstrations and can be used for training, fine-tuning, and evaluating models. <h2>Dataset Structure</h2> The dataset contains several files, defined below. <ol> <li>Raw Data (useful for training/fine-tuning/evaluation) <ol> <li>debug_demos.zip (1 GB) -- a subset of only 24 demonstrations taken from the full dataset. Useful to get a sense of the dataset and for debugging.</li> <li>gold_demos.zip (22 GB) -- a subset of only 724 demonstrations corresopnding to the 162 "Gold" tasks. This is the dataset that was used for all evaluations in the original WONDERBREAD paper.</li> <li>demos.zip (133 GB) -- all 2,928 demonstrations across 598 tasks. Useful for training your own models.</li> </ol> </li> <li>Modality-Specific Subsets of Raw Data (useful for specific types of training/fine-tuning/evaluation) <ol> <li>All Demos <ol> <li>demos_sop_only.zip (4 MB)-- only the SOP <code>.txt</code> files for all 2,928 demonstrations</li> <li>demos_sop_and_trace_only.zip (770 MB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for all 2,928 demonstrations</li> <li>demos_sop_and_trace_and_screenshots_only.zip (22 GB)-- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for all 2,928 demonstrations</li> </ol> </li> <li>"Gold" Demos <ol> <li>gold_demos_sop_only.zip (1 MB)-- only the SOP <code>.txt</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li>gold_demos_sop_and_trace_only.zip (190 MB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files for the 724 demonstrations in the "Gold" tasks.</li> <li>gold_demos_sop_and_trace_and_screenshots_only.zip (6 GB) -- only the SOP <code>.txt</code> files and action trace <code>.json</code> files and screenshot images for the 724 demonstrations in the "Gold" tasks</li> </ol> </li> </ol> </li> <li>Evaluation (useful for evaluation) <ol> <li>qa_dataset.csv -- contains all 120 questions and ground truth answers used in the "Knowlege Transfer" evaluation. </li> <li>df_rankings.csv -- contains the rankings of all "Gold" tasks used in the "SOP Ranking" evaluation. </li> </ol> </li> <li>Metadata (can be safely ignored) <ol> <li>Process Mining Task Demonstrations.xlsx -- maps human annotators to specific demonstrations; also contains "Gold" task rankings used in the "SOP Ranking" evaluation.</li> <li>metadata.json -- maps Google Drive URLs to Google Drive Folder IDs to demonstration names</li> <li>df_valid.csv -- tracks assets associated with each demonstration</li> </ol> </li> </ol>

opencc-by-4.0Jun 2021View details →

Data set: Forest management to increase carbon sequestration in boreal Pinus sylvestris forests

Data supporting the results and analyses published in Plant and Soil, "Forest management to increase carbon sequestration in boreal Pinus sylvestris forests". Data from a long-term fertilization (N and N+P) and thinning experiment in Pinus sylvestris stands across Sweden (56–67°N). Carbon stocks in soil and trees, tree growth, soil respiration and soil available nitrogen (ammonium, nitrate) are included. Data (data file + meta data file) include: jorgensen_etal_plantsoil_treesoil_data.csv (site data, carbon stocks: trees and their separate parts and soil, soil available nitrogen) jorgensen_etal_plantsoil_treesoil_data_METADATA.csv jorgensen_etal_plantsoil_resp_data.csv (site data, soil respiration, temperature, moisture) jorgensen_etal_plantsoil_resp_data_METADATA.csv R-script: Jorgensen_etal_plantsoil.R

opencc-by-4.0Jul 2021View details →

Simulation Results Data for 'Modelling new insecticide-treated bed nets for malaria-vector control: How to strategically manage resistance?'

GENERAL INFORMATION 1. Title of Dataset: Simulation Results Data for 'Modelling new insecticide-treated bed-nets for malaria-vector control: How to strategically manage resistance?' 2. Author Information     A. Investigator Contact Information         Name: Philip G. Madgwick         Institution: Syngenta          Address: Jealott’s Hill International Research Centre, Bracknell, RG42 6EY, UK         Email: philip.madgwick@syngenta.com     B. Investigator Contact Information         Name: Ricardo Kanitz         Institution: Syngenta          Address: Syngenta Crop Protection, Rosentalstrasse 67, CH-4058 Basel, Switzerland         Email: ricardo.kanitz@syngenta.com 3. Date of data collection (single date, range, approximate date): 2021-01-13 to 2021-02-01  4. Geographic location of data collection: UK  5. Information about funding sources that supported the collection of the data:  This work was conducted during a postdoctoral research position for PGM funded by the Innovative Vector Control Consortium (IVCC). SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: NA 2. Links to publications that cite or use the data: [UPDATE] 3. Links to other publicly accessible locations of the data: NA 4. Links/relationships to ancillary data sets: NA 5. Was data derived from another source? No 6. Recommended citation for this dataset: [UPDATE] DATA & FILE OVERVIEW 1. File List:  PSData_random6.csv - 10^6 random samples of each of the 17 parameters in the model, where rows are samples and columns are parameters (with column names corresponding to the parameters identified in the rows of Table 1 of the manuscript; see also DATA-SPECIFIC INFORMATION) Data_random6_maxpsMixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_maxpsMixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_maxpsMixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k=1; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mixture_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Mixture_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mixture_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used in a mixture (at rate k; see manuscript) as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mosaic_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Mosaic_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Mosaic_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A and B are used at 50% frequency each as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Rotation_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_revmm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_Rotation_Fixed_revmn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_Rotation_Fixed_revnn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used first in a rotation that switch between insecticides every 36 generations as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_SoloA_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloA_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloA_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide A is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   Data_random6_SoloB_Fixed_mm.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has mitochondrial inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloB_Fixed_mn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has mitochondrial inheritance   Data_random6_SoloB_Fixed_nn.csv - simulation dataset of 10^6 runs for each set of randomly sampled parameters; insecticide B is used solo as a fixed strategy for the entire run; the resistance allele at locus 1 has nuclear inheritance and the resistance allele at locus 2 has nuclear inheritance   2. Relationship between files, if important:  Files are named in accordance with the variables that describe each simulation setup, as described above. Each simulation dataset has 10^6 runs that correspond to the 10^6 random samples of each of the 17 parameters in the model in 'PSData_random6.csv'. 3. Additional related data collected that was not included in the current data package: NA 4. Are there multiple versions of the dataset? No METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Data were collected using Simulator.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  2. Methods for processing the data: Data were processed using Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  3. Instrument- or software-specific information needed to interpret the data: Analysis was conducted in R version 4.0.3 (2020-10-10), using R packages identified in Figures.R, which is in the vignettes of the 'detsims' R package that accompanies the manuscript.  4. Standards and calibration information, if appropriate: NA 5. Environmental/experimental conditions: NA 6. Describe any quality-assurance procedures performed on the data: NA 7. People involved with sample collection, processing, analysis and/or submission: NA  DATA-SPECIFIC INFORMATION FOR: PSData_random6.csv 1. Number of variables:  17 variables with column names that have the following parameter meanings (see Table 1 in the manuscript):  Population Size    = N = starting population size (and carrying capacity in logistic model); random sample range on log-scale: 10^2 - 10^9 Intrinsic Birth Rate = b = % population growth rate (in logistic model); random sample following a standard log-normal distribution with mean=0 and sd=1 Intrinsic Death Rate = d = % breeding mosquitoes that die into next generation; random sample range: 0 - 1 Female Exposure    = x_[female-symbol] = % female mosquitoes that receive a dose; random sample range: 0 - 1 Male Exposure x_[male-symbol] = % male mosquitoes that receive a dose; random sample range: 0 - 1 Initial Frequency A = f_0,A = starting frequency of allele A; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size Effectiveness 1    = m_1 = % dosed mosquitoes that die from insecticide 1; random sample range: 0 - 1 Resistance Restoration A = r_A = % return to baseline fitness with resistance allele A; random sample range: 0 - 1 Dominance of Resistance Restoration A = h^r_A = % resistance restoration in heterozygote with allele A; random sample range: 0 - 1 Resistance Cost A = c_A = % non-dosed mosquitoes that die from carrying allele A; random sample range on log-scale: 10^-3 - 10^-0.5 Dominance of Resistance Cost A = h^c_A = % resistance cost in heterozygote with allele A; random sample range: 0 - 1 Initial Frequency B = f_0,B = starting frequency of allele B; random sample range on log-scale: 10^-9 - 10^-2, limited to be within the range 1/N - N/100 where N is Population Size Effectiveness 2    = m_2 = % dosed mosquitoes that die from insecticide 2; random sample range: 0 - 1 Resistance Restoration B = r_B = % return to baseline fitness with resistance allele B; random sample range: 0 - 1 Dominance of Resistance Restoration B = h^r_B = % resistance restoration in heterozygote with allele B; random sample range: 0 - 1 Resistance Cost B = c_B = % non-dosed mosquitoes that die from carrying allele B; random sample range on log-scale: 10^-3 - 10^-0.5 Dominance of Resistance Cost B = h^c_B = % resistance cost in heterozygote with allele B; random sample range: 0 - 1 2. Number of cases/rows:  10^6, corresponding to the number of random samples  3. Variable List: NA  4. Missing data codes: NA 5. Specialized formats or other abbreviations used: NA DATA-SPECIFIC INFORMATION FOR: all other dataset files (e.g. Data_random6_maxpsMixture_Fixed_mm.csv)  1. Number of variables:  10 variables with column names that have the following meanings: A_t_50% = the recorded number of generations that it takes for resistance allele A to reach >50% frequency; 0 means that resistance allele A never reaches >50% frequency   A_f_250 = the frequency of resistance allele A at the 250th generation  A_f_bar = the mean frequency of resistance allele A over the first 250 generations   B_t_50% = the recorded number of generations that it takes for resistance allele B to reach >50% frequency; 0 means that resistance allele B never reaches >50% frequency   B_f_250 = the frequency of resistance allele B at the 250th generation  B_f_bar = the mean frequency of resistance allele B over the first 250 generations  nf_80% = the recorded number of generations that it takes for the female population size to recover to >80% of its original size in the 0th generation; 0 means that the female population size never reaches >80% recovery; 1 means that the female population size never drops below 80% of its original size in the 1st generation nf_250 = the female population size at the 250th generation nf_bar = the mean female population size over the first 250 generations  nf_ext = the recorded number of generations that it takes for the female population size to drop below 1 (i.e. population extinction); 0 means that female population size never reaches <1 2. Number of cases/rows:  10^6, corresponding to the number of random samples  3. Variable List: NA 4. Missing data codes: all missing data is recorded as 0  5. Specialized formats or other abbreviations used: NA

opencc-by-4.0Jul 2021View details →

Stochastic Mixed-Integer Programming for a Spare Parts Inventory Management Problem

The German Armed Forces provide an operation contingent to support the  North Atlantic Treaty Organization (NATO) Response Force (NRF). For short deployments (e.g., one month), the NRF troops can bring with them a tightly constrained "warehouse" of spare parts. To ensure optimal use this warehouse, we developed the computer program "The OPtimization of a Spare Parts INventory" (TOPSPIN) to find an optimal mix of spare parts to repair a set of systems. Each system is composed of several parts, and it can only be used again in the mission if all broken parts are replaced. Due to the stochastic nature of the problem, we generate scenarios that simulate the failure of the parts. The backbone of TOPSPIN is a mixed-integer linear program that determines an optimal, scenario-robust mix of spare parts. Using input data provided by the German Logistikzentrum (RealData.xlsx), we analyze how many scenarios need to be generated in order to determine reliable solutions. A further data set (SimData.xlsx) was generated with random data, having a similar structure as the real data set. Using these two data sets, we analyze the composition of the warehouse over a variety of different weight restrictions, and we examine the number of repairable systems for different values of this bound.

opencc-by-4.0Jul 2021View details →

Questionnaire Data Management in Microservices

Questionnaire used in the work "Data Management in Microservices: State of the Practice, Challenges, and Research Directions"

dryad40/100

BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations

The demand for agent-based models to explore the effects of environmental change on pollinator population dynamics is growing. However, models need a simple yet flexible interface to enable adoption by a wide range of stakeholders. We introduce BEE-STEWARD: a research and decision-support software tool, enabling researchers, policy-makers, land management advisors, and practitioners to predict and compare the effects of bee-friendly management interventions on bumblebee populations over several years. BEE-STEWARD integrates the BEESCOUT and Bumble-BEEHAVE agent-based models of bumblebee behaviour, colony growth and landscape exploration into a user-friendly interface, with reconstructed code, and expanded functionality. Bespoke automatic reports can be created to illustrate how different land management interventions can affect the densities of bumblebees and their colonies over time. BEE-STEWARD could be an important virtual test-bed for scientists exploring the impacts of different stressors on bumblebees and used by those with little or no modelling experience, enabling a shared methodology between research, policy, and practice.

opencc-zeroJul 2021View details →

FIG. 4 in Habitat preferences of Papilio alexanor Esper, [1800]: implications for habitat management in the Italian Maritime Alps

FIG. 4.— Pre-imaginal development of Papilio alexanor Esper, [1800] under laboratory conditions; box plots illustrate increases in larval length; vertical lines: median larval length; box: 25th-75th percentiles; whiskers: minimum and maximum observed values; outliers; dark grey band width represents the standard deviations of mean development intervals (days). Abbreviations: e, egg; I-IV, larval instars; p, pupa.

FIG. 2 in Habitat preferences of Papilio alexanor Esper, [1800]: implications for habitat management in the Italian Maritime Alps

FIG. 2.— Stable range of P. alexanor Esper, [1800] in Italy (Balletto et al. 2007), observations from S Italy are interpreted as being based on vagrant specimens from the Balkans, the arrow indicates the Valdieri study area.

FIG. 6 in Habitat preferences of Papilio alexanor Esper, [1800]: implications for habitat management in the Italian Maritime Alps

FIG. 6.— Comparison of mean values of larval survival rates calculated for the 21 (occupied) plots ranked by increasing total number of P. saxifraga (L.) Loret & Barrandon plants.

FIG. 5 in Habitat preferences of Papilio alexanor Esper, [1800]: implications for habitat management in the Italian Maritime Alps

FIG. 5.— Pre-imaginal development of P. alexanor Esper, [1800] in the field. Mean (± SE) number of eggs and larvae of P. alexanor collected during the four sampling events (2010).

FIG. 12 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México

FIG. 12. — Model of the life histories of the animals interred in Burial 6 divided into four stages; acquisition, management, preparation and sacrifice and/or deposition. Arrows and letters in grey indicate the zooarchaeological and archaeological indicators of these processes.

FIG. 11 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México

FIG. 11. — Photograph of basket excavated as a block from Burial 6. Arrow indicates the concentration of serpentine remains.

FIG. 10 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México

FIG. 10. — Canid and felid skull preparation methods: A, Element 2194 female wolf 1-2 years old; B, Element 1960 jaguar infant.

FIG. 9. — Element 1818 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México

FIG. 9. — Element 1818: A, in situ in Burial 6; B, deformed right femoral shaft; C, head injury along the nuchal crest; D, right innonimate with deformed acetabulum.

FIG. 8. — Element 2246 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México

FIG. 8. — Element 2246: A, pathology on the distal humeri, B, breakage along the occipital region of the cranium; C, perforation on the left coracoids bone.

FIG. 7 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México

FIG. 7. — Comparisons of the element distribution (grey=absent) and surface modifications (black arrow) present on the modern and archaeological sample: A, modern comparative sample; B, Element 2193; C, overlap of the two templates, areas in dark grey indicate where the element in both the comparative and archaeological sample were absent.

FIG. 1 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México

FIG. 1. — Iconographic representations of: A, close up of a canid depicted in mural painting "Coyote with Sacrifical Knife" probably from Techinantitla (taken from Millon 1988b: Figure V.12, tracing by S. Sugiyama); B, eagle from Atetelco apartment compound (photo taken by N. Sugiyama).