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Figure 12 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 12. Summary of floral colouration and floral phenology characteristics for flowering tree and shrub species that we observed in the Skukuza Ranger District, Kruger National Park, Republic of South Africa. Numbers indicate percentages of the 27 tree and shrub species included in this study.
Figure 10 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 10. Adults of species of the carpenter bee genus Xylocopa (Hymenoptera: Apidae) observed in the Skukuza Ranger District, Kruger National Park, Republic of South Africa. (a) X. caffra female; (b) X. caffra male; (c) X. inconstans female; (d) X. inconstans male; (e) X. flavorufa female; (f) X. lugubris female; (g) X. flavorufa male.
Figure 11 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 11. Characteristic growth forms of common tree and shrub species in the Skukuza Ranger District, Kruger National Park, Republic of South Africa. (a) Peltophorum africanum; (b) Terminalia prunioides; (c) Terminalia sericea; (d) Grewia flavescens; (e) Acacia exuvialis; (f) Acacia nigrescens; (g) Acacia tortilis; (h) Dichrostachys cinerea; (i) Ziziphus mucronata.
Figure 2 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 2. Schematic diagram showing our sampling framework as deployed along the road segments listed in the Appendix, centred along tourist roads, gravel roads and firebreak roads in the Skukuza Ranger District of the Kruger National Park. Floral visitors of flowering trees and shrubs located within 30 m of the road edge in each road segment were surveyed as part of our field effort.
Figure 9 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 9. Adult females of the carpenter bee Xylocopa caffra (Hymenoptera: Apidae) on or near flowers of Peltophorum africanum, photographed in the Skukuza Ranger District, Kruger National Park, Republic of South Africa.
Figure 1 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 1. Map of the Republic of South Africa, showing the location of the Kruger National Park and the Skukuza Ranger District.
Figure 8 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 8. Adult of the day-flying moth Arniocera auriguttata (Lepidoptera: Thyrididae) on flowers of the shrub Flueggea virosa, photographed along the Sabie River in the Skukuza Ranger District, Kruger National Park, Republic of South Africa.
Figure 5 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 5. Adults of species of Cleridae and Lycidae (Coleoptera) from the Skukuza Ranger District, Kruger National Park, Republic of South Africa. (a) Aphelochroa sanguinalis (Cleridae); (b) Lycus trabeatus male (Lycidae); (c) Aphelochroa sanguinea (Cleridae); (d) Lycus trabeatus female (Lycidae).
Figure 4 in Natural history of a South African insect pollinator assemblage (Insecta: Coleoptera, Diptera, Hymenoptera, Lepidoptera): diagnostic notes, food web analysis and conservation recommendations
Figure 4. Examples of high-resolution digital images showing pollen grains adhering to insect floral visitors. (a) Pedinorrhina trivittata on flowers of Acacia grandicornuta. (b) Leucocelis amethystina on flowers of Acacia grandicornuta. (c) pair of Leucocelis vitticollis on flowers of Peltophorum africanum. White arrows point to areas where pollen is adhering to the insect's integument.
Fig. 1 in Biology and Conservation of Cicindela ohlone Freitag and Kavanaugh (Coleoptera: Carabidae: Cicindelinae), the Endangered Ohlone Tiger Beetle. II. Population Ecology of Adults and Larvae and Recommended Monitoring Methods
Fig. 1. Marking scheme for individual identification of Ohlone tiger beetle adults used during the capturerecapture and frequency of capture studies. Some variation in the maculations exists. Numbers on the elytra represent the positions for marking each beetle with a unique identification number. Marks applied to single or multiple locations uniquely identify each marked beetle. For example, beetle #1 would have a mark at the #1 position, beetle #12 would have marks at the #2 and #10 locations, and beetle #147 would have marks at the #7, #40, and #100 locations.
Fig. 4 in Biology and Conservation of Cicindela ohlone Freitag and Kavanaugh (Coleoptera: Carabidae: Cicindelinae), the Endangered Ohlone Tiger Beetle. II. Population Ecology of Adults and Larvae and Recommended Monitoring Methods
Fig. 4. Population curves for adult Ohlone tiger beetle generations at Glenwood, Santa Cruz Co., CA. a) Triangular model in 2016, b) Multi-peak model in 2017.
Fig. 3 in Biology and Conservation of Cicindela ohlone Freitag and Kavanaugh (Coleoptera: Carabidae: Cicindelinae), the Endangered Ohlone Tiger Beetle. II. Population Ecology of Adults and Larvae and Recommended Monitoring Methods
Fig. 3. Frequency of observed dispersal distances (m) by Ohlone tiger beetle males (bars with vertical lines) and females (bars with horizontal lines).
Fig. 2 in Biology and Conservation of Cicindela ohlone Freitag and Kavanaugh (Coleoptera: Carabidae: Cicindelinae), the Endangered Ohlone Tiger Beetle. II. Population Ecology of Adults and Larvae and Recommended Monitoring Methods
Fig. 2. Recapture decay plot of observed Ohlone tiger beetle adult lifespans (i.e., residence) and fitted trend line
Fig. 6 in Biology and Conservation of Cicindela ohlone Freitag and Kavanaugh (Coleoptera: Carabidae: Cicindelinae), the Endangered Ohlone Tiger Beetle. II. Population Ecology of Adults and Larvae and Recommended Monitoring Methods
Fig. 6. Inverse correlation between annual (July 1 – June 30) rainfall amounts (cm) and estimated Ohlone tiger beetle (OTB) adult generation population sizes for Moore Creek, Santa Cruz Co., CA.
Fig. 5. Annual Ohlone tiger beetle adult generation population estimates from 2000 through 2017 in Biology and Conservation of Cicindela ohlone Freitag and Kavanaugh (Coleoptera: Carabidae: Cicindelinae), the Endangered Ohlone Tiger Beetle. II. Population Ecology of Adults and Larvae and Recommended Monitoring Methods
Fig. 5. Annual Ohlone tiger beetle adult generation population estimates from 2000 through 2017 at four study sites near Santa Cruz, CA. Horizontal line represents the average generation size of all annual estimates for each site.
A unified DTI prediction framework based on knowledge graph and recommendation system
<p>## A unified DTI prediction framework based on knowledge graph and recommendation system</p> <p> </p> <p># Code and data description</p> <p>## Scripts</p> <p>- `kge_nfm.py`: the complement of the KGE_NFM & NFM methods.</p> <p>- `kge_rf.py`: the complement of the KGE_RF & RF methods.</p> <p>- `deepdit.py`: the complement of the MPNN_CNN & DeepDTI methods.</p> <p>- the complement of DTINet and DTiGEMS is tested based on their source packages (more in Prerequisites)</p> <p><br> </p> <p>## `data/` directory</p> <p>#### `yamanishi_08/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg_data/`: supporting knowledge graph data</p> <p>- `dt_all_08.csv`: whole DTI dataset</p> <p>- `791drug_struc.csv`: drugbank id and smiles of drugs</p> <p>- `989proseq.csv`: kegg id and sequences of proteins</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `BioKG/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `comp_struc.csv`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `hetionet/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `map_drugs_df`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `luo's_dataset/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `mapping/`: related mappings and similarity matrix (https://github.com/luoyunan/DTINet)</p> <p>- `protein.txt`: list of protein names</p> <p>- `disease.txt`: list of disease names</p> <p>- `se.txt`: list of side effect names</p> <p>- `drug_dict_map`: a complete ID mapping between drug names and DrugBank ID</p> <p>- `protein_dict_map`: a complete ID mapping between protein names and UniProt ID</p> <p>- `Similarity_Matrix_Drugs.txt` : Drug similarity scores based on chemical structures of drugs</p> <p>- `Similarity_Matrix_Proteins.txt` : Protein similarity scores based on primary sequences of proteins</p> <p>- `feature/`: related features used in methods</p> <p>- `drug_smiles.csv`: drugbank id and smiles</p> <p>- `seq.txt`: list of protein sequences</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `eg_model/` directory</p> <p>We provided a pre-trained kge model for example.</p> <p>- `dismult_400_warm_1_10.pkl`</p> <p><br> </p> <p># Prerequisites</p> <p>#### Operating system: Linux</p> <p>#### Programing language: python</p> <p>#### KGE_NFM & NFM dependencies</p> <p>```</p> <p>- python 3.6</p> <p>- pandas '1.1.5'</p> <p>- numpy '1.18.4'</p> <p>- scikit-learn '0.24.1'</p> <p>- tensorflow '1.15.0'</p> <p>- ampligraph '1.3.2'</p> <p>- deepctr '0.8.4'</p> <p>```</p> <p>#### baseline dependencies</p> <p>- RF & KGE_RF (included in KGE_NFM&NFM dependencies)</p> <p>- MPNN_CNN & DeepDTI:</p> <p>- source: https://github.com/kexinhuang12345/DeepPurpose</p> <p>```</p> <p>- deeppurpose '0.0.9'</p> <p>- torch '1.6.0+cu101'</p> <p>```</p> <p>- DTINet:</p> <p>- source: https://github.com/luoyunan/DTINet</p> <p>- note: in this work, we run the DTINet in a python environment, which need Linux system and python2. Importantly, this method requires the [Inductive Matrix Completion](http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/) (IMC) library. More detailed information about the installation of this method could be found in the source code of the DTINet.</p> <p>- DTiGEMS:</p> <p>- source: https://github.com/MahaThafar/DTiGEMSplus</p> <p>- TriModel:</p> <p>- source: http://drugtargets.insight-centre.org/</p> <p><br> <br> </p> <p># Example (kge_nfm.py)</p> <p> </p> <p>#### A brief presentation of the results:</p> <p>- return average loss when training kge model</p> <p>```</p> <p>Average Loss: 0.475181: 2%|###3 | 1/50 [01:10<57:31, 70.44s/epoch]</p> <p>```</p> <p>- return performance(mrr) on training set of DTI for early stopping (kge_model in `eg_model/`)</p> <p>```</p> <p>In [35]: roc = roc_auc(test_label,test_score)</p> <p>...: pr = pr_auc(test_label,test_score)</p> <p>...: print(roc)</p> <p>...: print(pr)</p> <p>0.8731770833333332</p> <p>0.44079654835037246</p> <p>```</p> <p> </p> <p>- nfm training process (`patience=10`)</p> <p> </p> <p>```</p> <p>In [45]: roc_nfm,pr_nfm,pred_y = train_nfm(feature_columns,train_model_input,train_label,test_model_input,test_label,patience)</p> <p>Train on 44851 samples</p> <p>Epoch 1/2000</p> <p>44851/44851 - 2s - loss: 0.5332 - precision: 0.0976</p> <p>Epoch 2/2000</p> <p>44851/44851 - 1s - loss: 0.4143 - precision: 0.0000e+00</p> <p>Epoch 3/2000</p> <p>44851/44851 - 1s - loss: 0.3456 - precision: 0.0000e+00</p> <p>Epoch 4/2000</p> <p>44851/44851 - 1s - loss: 0.3443 - precision: 0.0000e+00</p> <p>Epoch 5/2000</p> <p>44851/44851 - 1s - loss: 0.3470 - precision: 0.0000e+00</p> <p>Epoch 6/2000</p> <p>44851/44851 - 1s - loss: 0.3382 - precision: 0.0000e+00</p> <p>......</p> <p>Epoch 279/2000</p> <p>44851/44851 - 1s - loss: 0.0758 - precision: 0.9248</p> <p>Epoch 280/2000</p> <p>44851/44851 - 1s - loss: 0.0753 - precision: 0.9327</p> <p>Epoch 281/2000</p> <p>44851/44851 - 1s - loss: 0.0796 - precision: 0.9155</p> <p>Epoch 282/2000</p> <p>44851/44851 - 1s - loss: 0.0764 - precision: 0.9276</p> <p>Epoch 283/2000</p> <p>44851/44851 - 1s - loss: 0.0739 - precision: 0.9127</p> <p>```</p> <p> </p> <p>- reutrn results as type of roc_auc & pr_auc</p> <p>```</p> <p>0.9812476679104477</p> <p>0.8803416284646345</p> <p>```</p>
Workshop recording: Towards Implementation of the UNESCO Recommendation on Open Science – How to integrate citizen science into (open) science policy
<p>Recording of the workshop: Towards Implementation of the UNESCO Recommendation on Open Science – How to integrate citizen science into (open) science policy. If you have any questions or comments, please contact the workshop organisers Uta Wehn (<a href="mailto:u.wehn@un-ihe.org">u.wehn@un-ihe.org</a>) and Libby Hepburn (<a href="mailto:libby@atlasoflife.org.au">libby@atlasoflife.org.au</a>).</p>
Bats of Bangladesh — A systematic review of the diversity and distribution with recommendations for future research
<p>Bangladesh is a South Asian country located at the crossroads of the Indochina and Indo-Himalayan subregions, making it a country of rich faunal diversity. Bangladesh's high population density paired with rapid habitat alteration leaving only 6% of its natural habitats threatens its faunal diversity. Over 1,455 bat species live on earth, providing immense ecological services to maintain biodiversity. The paucity of bat research in Bangladesh and the lack of comprehensive work has led us to set the goal of checking how many species are present in Bangladesh, and the possibility of bat species yet to have occurred. Here we compiled species occurrence data on the bats of Bangladesh and states in neighboring countries (India – states are West Bengal, Sikkim, Meghalaya, Assam, Tripura, Mizoram; Myanmar – states are Chin, Rakhine) from the museums (American Museum of Natural History, Smithsonian National Museum of Natural History, Natural History Museum at United Kingdom, Field Museum of Natural History, Hungarian Natural History Museum, and Royal Ontario Museum), Global Biodiversity Information Facility, and literature, and constructed distribution maps for each species. The maps depicted both the fine-scale and coarse-scale distribution of the species. We confirmed 31 species are occurring in Bangladesh – among them, 22 species are confirmed with the voucher specimen, 15 species are associated with the preserved tissues, and one is confirmed with the morphometric data and key characteristics. Based on the species occurrence in the states of India and Myanmar, along with the habitat preference, an additional 83 species are yet to have occurred in Bangladesh. Among them, 38 species are categorized as Highly Probable, 33 species are Probable, and 10 species are Possible. We recommend bat surveys are urgent in Bangladesh using all complementary capture techniques that will contribute to voucher specimen collections and confirm the presence of bats. In addition, echolocation calls of bats can help establish call libraries. </p>
WeatherPon: A Weather and Machine Learning-based Coupon Recommendation Mechanism in Digital Marketing
<p>These are the datasets that we used.</p>
video games recommendations dataset crafted by human experts
<p>video games recommendations dataset crafted by human experts</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.