Find research datasets worth reusing

The Transformative Storytelling Technique for Developing Guided Digital Narratives in Mental Health Support. Pilot study

In a society where advances and innovations occur on a daily basis, the development of digital mental health tools continues at almost uncontrollable rates. Following trends in the utilization of storytelling across fields and reflecting on the lack of working models and frameworks for the application of storytelling for mental health support, the Transformative Storytelling Technique (TST) is designed and developed as a part of this work. This technique represents a new category for creating hybrid content to guide the experience of audiences, starting with the case of informal caregivers. In this pilot study, a TST caregiver story is assessed pre-post in an interventional study, investigating the potential role of mental health audio stories in supporting informal caregiver wellbeing. The anonymized raw dataset is available.

Continuous Development of AI: a Practitioners' Survey

The upload contains a raw dataset with the survey questions and a data analysis file as an appendix of the paper "Continuous Development of AI: a Practitioners Survey".

Data for 'A multi‐methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'

Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal' Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582 R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023 Principal Investigator: Graham W Prescott (graham.prescott.research@gmail.com) Authors: Biraj Adhikari (biraj.adhikari@unibe.ch, ORCID: 0000-0002-4260-8706) Davnah Urbach (davnah.payne@unibe.ch, ORCID: 0000-0001-9170-7834) Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879) Eklabya Sharma (eklabya.sharma11@gmail.com, ORCID:0000-0003-3089-8838) Thomas Breu (thomas.breu@unibe.ch, ORCID: 0000-0003-2348-504X) Jonas Geschke (jonas.geschke@unibe.ch, ORCID: 0000-0002-5654-9313) Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900) Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X) Date of data collection: August 2021 - June 2022 Location of data collection: Kathmandu Date of final release: Data Overview: There are two excel files. Each excel sheet is also uploaded as a separate .csv file. 1. 'Alldata.xls': This  excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data. Sheet overview: method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns: institution -> institutional affiliation of the participant inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government. exp_years -> experience (in years) of the participant in the conservation sector of Nepal sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible) conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score text_out -> (optional) a description of why the participant gave that outgoing interaction score inscore ->  the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible) conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score text_in -> (optional) a description of why the participant gave that incoming interaction score method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns: Year -> Year when the indicator was measured forest_cover -> Forest cover as a percent of total area (%) kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%) kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%) redlist -> Red List Index undernourishment -> Prevalence of undernourishment (%) food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%) death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability) suicide -> Suicide mortality rate (deaths per 100,000 population) uhc_index -> Universal health coverage (UHC) service coverage index f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats) safewater -> Proportion of population using safely managed drinking water services (%) electricity -> Proportion of population with access to electricity, by urban/rural (%) cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%) renewable -> Renewable energy share in the total final energy consumption (%) gdp_capita -> Annual growth rate of real GDP per capita (%) manu_value -> Manufacturing value added as a proportion of GDP (%) consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars) consumption_all -> Domestic material consumption (tonnes) consumption_cap -> Domestic material consumption per capita (tonnes) death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%) method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG. allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG. 2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.

3D images of ovule primordia development in Zea mays - Part II

This image resource describes the development of ovule primordia of Zea mays, in 3D and at cellular resolution. The images were segmented using ImarisCell (Bitplane, Switzerland) and annotated as described in the associated manuscript Ouedraogo et al. (2023) Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1174171 Zea mays (B73 genotype) floret meristem and ovule primordia were stained using a cell wall and nuclear dyes, cleared using Clearsee solution (Kurihara et al., 2015), imaged using multiphoton microscopy and processed for segmentation in ImarisCell (Bitplane) as decribed in Mendocilla Sato et al., 2017. The .ims files also contains Surfaces objects for 3D rendering of specific cells or organs, used for quantitative analysis as described in the associated manuscript. Each file corresponds to an individual ovule primordia or floret meristem, with file names corresponding to  ovule ID as described in Supplementary Dataset 1 of the associated manuscript Ouedraogo et al. (2023) Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1174171

3D images of ovule primordia development in Zea mays - Part I

This image resource describes the development of ovule primordia of Zea mays, in 3D and at cellular resolution. The images were segmented using ImarisCell (Bitplane, Switzerland) and annotated as described in the associated manuscript Ouedraogo et al. (2023) Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1174171 Zea mays (B73 genotype) floret meristem and ovule primordia were stained using a cell wall and nuclear dyes, cleared using Clearsee solution (Kurihara et al., 2015), imaged using multiphoton microscopy and processed for segmentation in ImarisCell (Bitplane) as decribed in Mendocilla Sato et al., 2017. Each file corresponds to an individual ovule primordia or floret meristem, with file names corresponding to  ovule ID as described in Supplementary Dataset 1 of the associated manuscript Ouedraogo et al. (2023) Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1174171

3D images of ovule primordia development in Zea mays - Part III

This image resource describes the development of ovule primordia of Zea mays, in 3D and at cellular resolution. The images were segmented using ImarisCell (Bitplane, Switzerland) and annotated as described in the associated manuscript Ouedraogo et al. (2023) Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1174171 Zea mays (B73 genotype) floret meristems and ovule primordia were stained using cell wall and nuclear dyes, cleared using Clearsee solution (Kurihara et al., 2015), imaged using multiphoton microscopy and processed for segmentation in ImarisCell (Bitplane) as decribed in Mendocilla Sato et al., 2017. The .ims files also contains Surfaces objects for 3D rendering of specific cells or organs, used for quantitative analysis as described in the associated manuscript. Each file corresponds to an individual ovule primordia or floret meristem, with file names corresponding to  ovule ID as described in Supplementary Dataset 1 of the associated manuscript Ouedraogo et al. (2023). Frontiers in Plant Science. DOI: 10.3389/fpls.2023.1174171

opencc-zeroDec 2022View details →

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Whole blood RNA-seq demonstrates an increased host immune response in individuals with cystic fibrosis who develop nontuberculous mycobacterial pulmonary disease

Background Individuals with cystic fibrosis have an elevated lifetime risk of colonization, infection, and disease caused by nontuberculous mycobacteria. A prior study involving non-cystic fibrosis individuals reported a gene expression signature associated with susceptibility to nontuberculous mycobacteria pulmonary disease (NTM-PD). In this study, we determined whether people living with cystic fibrosis who progress to NTM-PD have a gene expression pattern similar to the one seen in the non-cystic fibrosis population. Methods We evaluated whole blood transcriptomics using bulk RNA-seq in a cohort of cystic fibrosis patients with samples collected closest in timing to the first isolation of nontuberculous mycobacteria. The study population included patients who did (n = 12) and did not (n = 30) develop NTM-PD following the first mycobacterial growth. Progression to NTM-PD was defined by a consensus of two expert clinicians based on reviewing clinical, microbiological, and radiological information. Differential gene expression was determined by DESeq2. Results No differences in demographics or composition of white blood cell populations between groups were identified at baseline. Out of 213 genes associated with NTM-PD in the non-CF population, only two were significantly different in our cystic fibrosis NTM-PD cohort. Gene set enrichment analysis of the differential expression results showed that CF individuals who developed NTM-PD had higher expression levels of genes involved in the interferon (α and γ), tumor necrosis factor, and IL6-STAT3-JAK pathways. Conclusion In contrast to the non-cystic fibrosis population, the gene expression signature of patients with cystic fibrosis who develop NTM-PD is characterized by increased innate immune responses.

DCASE 2023 Challenge Task 2 Development Dataset

Description This dataset is the "development dataset" for the <a href="https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring"</a>. The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel 10-second audio that includes both a machine's operating sound and environmental noise. The following seven types of real/toy machines are used in this task: <ul> <li>ToyCar</li> <li>ToyTrain</li> <li>Fan</li> <li>Gearbox</li> <li>Bearing</li> <li>Slide rail</li> <li>Valve</li> </ul>   Overview of the task Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial-intelligence-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines.  This task is the follow-up from DCASE 2020 Task 2 to DCASE 2022 Task 2. The task this year is to develop an ASD system that meets the following four requirements.   1. Train a model using only normal sound (unsupervised learning scenario) Because anomalies rarely occur and are highly diverse in real-world factories, it can be difficult to collect exhaustive patterns of anomalous sounds. Therefore, the system must detect unknown types of anomalous sounds that are not provided in the training data. This is the same requirement as in the previous tasks. 2. Detect anomalies regardless of domain shifts (domain generalization task)  In real-world cases, the operational states of a machine or the environmental noise can change to cause domain shifts. Domain-generalization techniques can be useful for handling domain shifts that occur frequently or are hard-to-notice. In this task, the system is required to use domain-generalization techniques for handling these domain shifts. This requirement is the same as in DCASE 2022 Task 2. 3. Train a model for a completely new machine type For a completely new machine type, hyperparameters of the trained model cannot be tuned. Therefore, the system should have the ability to train models without additional hyperparameter tuning. 4. Train a model using only one machine from its machine type While sounds from multiple machines of the same machine type can be used to enhance detection performance, it is often the case that sound data from only one machine are available for a machine type. In such a case, the system should be able to train models using only one machine from a machine type.   The last two requirements are newly introduced in DCASE 2023 Task2 as the "first-shot problem".   Definition We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.". <ul> <li>"Machine type" indicates the type of machine, which in the development dataset is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.</li> <li>A section is defined as a subset of the dataset for calculating performance metrics.</li> <li>The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.</li> <li>Attributes are parameters that define states of machines or types of noise.</li> </ul>   Dataset This dataset consists of seven machine types. For each machine type, one section is provided, and the section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training, and (iii) 100 clips each of normal and anomalous sounds for the test. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.   File names and attribute csv files File names and attribute csv files provide reference labels for each clip. The given reference labels for each training/test clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format:     [filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]...   Recording procedure Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.   Directory structure - /dev_data       - /raw         - /fan             - /train (only normal clips)                   - /section_00_source_train_normal_0000_<attribute>.wav                   - ...                   - /section_00_source_train_normal_0989_<attribute>.wav                   - /section_00_target_train_normal_0000_<attribute>.wav                   - ...                   - /section_00_target_train_normal_0009_<attribute>.wav               - /test                  - /section_00_source_test_normal_0000_<attribute>.wav                     - ...                   - /section_00_source_test_normal_0049_<attribute>.wav                     - /section_00_source_test_anomaly_0000_<attribute>.wav                   - ...                   - /section_00_source_test_anomaly_0049_<attribute>.wav                   - /section_00_target_test_normal_0000_<attribute>.wav                 - ...                   - /section_00_target_test_normal_0049_<attribute>.wav                  - /section_00_target_test_anomaly_0000_<attribute>.wav                   - ...                   - /section_00_target_test_anomaly_0049_<attribute>.wav              - attributes_00.csv (attribute csv for section 00)     - /gearbox (The other machine types have the same directory structure as fan.)       - /bearing     - /slider (`slider` means "slide rail")     - /ToyCar       - /ToyTrain       - /valve     Baseline system The baseline system is available on the Github repository <a href="https://github.com/nttcslab/dase2023_task2_baseline_ae">dcase2023_task2_baseline_ae</a>.The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.   Condition of use This dataset was created jointly by Hitachi, Ltd. and NTT Corporation and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.   Citation If you use this dataset, please cite all the following papers. We will publish a paper on the description of the DCASE 2023 Task 2, so pleasure make sure to cite the paper, too. <ul> <li>Noboru Harada, Daisuke Niizumi, Yasunori Ohishi, Daiki Takeuchi, and Masahiro Yasuda. First-shot anomaly detection for machine condition monitoring: A domain generalization baseline. In arXiv e-prints: 2303.00455, 2023. [<a href="https://arxiv.org/pdf/2303.00455.pdf">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. In Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), 31-35. Nancy, France, November 2022, . [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul>   Contact If there is any problem, please contact us: <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> <li>Noboru Harada, <a href="mailto:noboru@ieee.org">noboru@ieee.org</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>

opencc-by-4.0Mar 2023View details →

Development Gateway shisha study quantitative data: a cross-sectional survey investigating factors associated with shisha use in Nigeria

This data is from a cross-sectional telephone survey among 1278 respondents (611 current shisha smokers and 667 shisha non-smokers) exploring the following questions: <ol> <li>What factors (demographic, social, cultural, behavioural, etc.) are associated with the use of shisha in Nigeria?</li> <li>What are the shisha use patterns and behaviours among those who use shisha in Nigeria?</li> </ol> The study was conducted across the six geo-political zones in Nigeria (North-East, North-West, North-Central, South-East, South-South and South-West) in the following states: Lagos, Oyo, FCT, Plateau, Gombe, Adamawa, Kaduna, Kano, Rivers, Edo, Anambra and Ebonyi. The data was collected between 28th July and 11th September 2022. Also included is the codebook and the study questionnaire.

opencc-by-4.0May 2023View details →

Evaluating Institutional Commitments to Open Scholarly Infrastructure: A Review of Open Access Collection Development Policies

Data prepared for the publication "Evaluating Institutional Commitments to Open Scholarly Infrastructure: A Review of Open Access Collection Development Policies." oa-cd-policies.csv Scope: This data represents collection development policies that contain substantial mention of open access. Data collection: The policies were sourced using an Advanced Google Search for "open access" AND "collection development policy" at ".edu" domains. Variables: <ul> <li>institution: Free text, name of the institution.</li> <li>carnegie_class: One of <a href="https://carnegieclassifications.acenet.edu/carnegie-classification/classification-methodology/basic-classification/">these options</a>; the Carnegie classification of the institution.</li> <li>institution_type: One of public or private; the funding source of the institution.</li> <li>policy_name: Free text; the title of the policy.</li> <li>supplemental_policy: Link to a supplemental open access policy if linked in the collection development policy.</li> <li>cd_policy_link: Link to the policy.</li> <li>infrastructure: TRUE or FALSE; whether the policy includes a commitment to open access scholarly infrastructure development, including open source platforms, locally hosted platforms, consortia, or an institutional repository.</li> <li>excerpt: Free text; text from the policy that mentions infrastructure.</li> </ul> principles-policies.csv Scope: This data represents those collection development policies from oa-cd-policies.csv that contain commitments in line with the <a href="https://openscholarlyinfrastructure.org/">Principles for Open Scholarly Infrastructure</a>. Variables: <ul> <li>institution: Free text, name of the institution.</li> <li>carnegie_class: One of <a href="https://carnegieclassifications.acenet.edu/carnegie-classification/classification-methodology/basic-classification/">these options</a>; the Carnegie classification of the institution.</li> <li>institution_type: One of public or private; the funding source of the institution.</li> <li>policy_name: Free text; the title of the policy.</li> <li>supplemental_policy: Link to a supplemental open access policy if linked in the collection development policy.</li> <li>cd_policy_link: Link to the policy.</li> <li>principle: One of the three main <a href="https://openscholarlyinfrastructure.org/">Principles</a>.</li> <li>sub_principle: One of the <a href="https://openscholarlyinfrastructure.org/">Sub-Principles</a>.</li> <li>excerpt: Free text; text from the policy that illustrates the sub_principle.</li> </ul>

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Data from: Extensive, transient, and long-lasting gene regulation in a song-controlling brain area during testosterone-induced song development in adult female canaries

Like other canary reproductive behaviors, song production occurs seasonally and can be triggered by gonadal hormones. Adult female canaries treated with testosterone sing first songs after four days and progressively develop towards typical canary song structure over several weeks, a behavior that females otherwise rarely or never show. We compared gene regulatory networks in the song-controlling brain area HVC after 1 hour (h), 3 h, 8 h, 3 days (d), 7d, and 14d testosterone treatment with placebo-treated control females, paralleling HVC and song development. Rapid onset (1 h or less) of extensive transcriptional changes (2,700 genes) preceded the onset of song production by four days. The highest level of differential gene expression occurred at 14 days when song structure was most elaborate, and song activity was highest. The transcriptomes changed massively several times during the two-week of song production. A total of 9,710 genes were differentially expressed, corresponding to about 60% of the known protein-coding genes of the canary genome. Most (99%) of the differentially expressed genes were regulated only at specific stages. The differentially expressed genes were associated with diverse biological functions, of which cellular level occurring early and nervous system level occurring primarily after prolonged testosterone treatment. Thus, the development of adult songs requires restructuring the entire HVC, including most HVC cell types, rather than altering only neuronal subpopulations or cellular components. Parallel regulation directly by androgen and estrogen receptors and by other hub genes such as the transcription factor SP8, which are under steroidogenic control, lead to massive transcriptomic and neural changes in the specific behavior-controlling brain areas and gradual seasonal occurrence of singing behavior.

opencc-zeroMay 2023View details →

opencc-by-4.0May 2023View details →

Supporting data for the publication "Emission ensemble approach to improve the development of multi-scale emission inventories"

This dataset includes the source IDL code as well as the three emission inventory aggregated emission datasets necessary to perform the analysis presented in the publication: "Emission ensemble approach to improve the development of multi-scale emission inventories (GMD)"

Figure 5 in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)

Figure 5 The age-stage specific consumption rate (Cxj) of Neoseiulus cucumeris when fed on saffron pollen for 10 and 20 generations (G10, G20) then switched toTetranychus urticae.

Figure 6 The age-specific predation rate k in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)

Figure 6 The age-specific predation rate k (x), and age-specific net predation rateqx() of Neoseiulus cucumeris when fed on saffron pollen for 10 and 20 generations (G10, G20) then switched toTetrany-

Figure 2 in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)

Figure 2 Age-specific survivorship (lx), age-specific fecundity (mx), and age-stage specific fecundityfxj() of Neoseiulus cucumeris fed on saffron pollen for 20 consecutive generations (G1-G20).

Figure 1 in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)

Figure 1 Age-stage-specific survival rate (sxj) of Neoseiulus cucumeris fed on saffron pollen for 20 consecutive generations (G1-G20).

Figure 4 in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)

Figure 4 Age-specific survivorship (lx), age-specific fecundity (mx), and age-stage specific fecundity (fxj) of Neoseiulus cucumeris when fed on saffron pollen for 10 and 20 generations (G10, G20) then switched toTetranychus urticae.

Figure 3 in Long-term effects of saffron pollen on development, reproduction and predation capacity of Neoseiulus cucumeris (Acari: Phytoseiidae)

Figure 3 Age-stage-specific survival rate (sxj) of Neoseiulus cucumeris when fed on saffron pollen for 10 and 20 generations (G10, G20) then switched toTetranychus urticae.