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1,049 results for “robustness”
Dataset for the manuscript "Robust ParaHydrogen-Induced Polarization at High Concentrations"
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A thermally/chemically robust and easily regenerable anilato-based ultramicroporous 3D MOF for CO2 uptake and separation
<p>Relevant data for the publication with DOI:</p> <table> <tbody> <tr> <td><a href="https://doi.org/10.1039/D1TA07436A"><span>10.1039/D1TA07436A</span></a></td> </tr> </tbody> </table>
Dataset for manuscript "Robust Enzyme Discovery and Engineering with Deep Learning using CataPro"
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Asian elephants are associated with a more robust mammalian community in tropical forests
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Robust detection of SARS-CoV-2 exposure in population using T-cell repertoire profiling
<p>The dataset contains processed T-cell receptor repertoire sequencing data from >1200 individuals of different sex and age. Note that only samples with good sequencing coverage are published (>10^5 reads per file). </p> <p>The main aim of our study is to find TCR sequence biomarkers and develop a bioinformatic pipeline that allows building an accurate and robust classifier that distinguishes COVID-19-convalescent donors from unexposed individuals. We performed immunosequencing of the rearranged TCR α and β regions for PBMCs. For the cohort described in this study (Cohort-I) we sequence both chains of the TCR heterodimer as both of these chains are required to properly predict antigen recognition26. We ran conventional T-cell repertoire data analysis and pre-processed data to remove low-coverage samples. </p> <p>Of samples in Cohort-I which passed read count threshold, 383/377 TCR α/β samples were from healthy donors (SARS-CoV-2 PCR test negative or obtained prior to pandemic) and 890/848 were from COVID-19-positive patients. The majority of samples were accompanied by information on HLA class I and II alleles. Samples were prepared and sequenced in nine batches.</p> <p>The metadata for both TCR alpha and beta repertoires contains the following information:</p> <div> <ul> <li>sequencing_date - date when seguencing was performed</li> <li>batch_name - one of the 9 unique batch identifiers</li> <li>sample_id, patient_id - information on sample identifier and donor identifier</li> <li>COVID_status, COVID_IgG, COVID_IgM, COVID_PCR - information on COVID-19 status</li> <li>HLA-A.1, HLA-A.2, HLA-B.1, HLA-B.2, HLA-C.1, HLA-C.2 - MHC class I alleles</li> <li>HLA-DPB1.1, HLA-DPB1.2, HLA-DQB1.1, HLA-DQB1.2, HLA-DRB1.1, HLA-DRB1.2 - MHC class II alleles</li> <li>file_name - name of the corresponding file in <em>fmba_clonotype_usage_tables.zip </em>archive</li> </ul> </div> <p>Each file in <em>fmba_clonotype_usage_tables.zip </em>archive stores the information on either TCR alpha or beta repertoire. Each line in a file corresponds to the unique clonotype and each clonotype is accompanied with the following information:</p> <ul> <li>count - number of reads where the clonotype was detected</li> <li>freq - count of reads with the clonotype divided by thw whole number of reads in a sample</li> <li>cdr3nt, cdr3aa - nucleotide and amino acid sequences of TCR's CDR3 sequence</li> <li>v, d, j - the V/D/J segment name which was used for the clonotype's rearrangement</li> <li>VEnd, DStart, DEnd, JStart - information on VDJ junction positions </li> </ul> <p>We proceed with selecting a set of CDR3 sequences that can serve as biomarkers and form a feature list for COVID-19 status classifier. We also validate the resulting set of clonotypes in several ways. Co-occurence of specific TCR α and β clonotypes can serve as an independent validation for biomarkers and their co-association with some specific pathogen. Additional information on donor HLAs is provided to filter the set of biomarkers based on HLA restriction: association with donor HLA serves as an additional evidence for TCR specificity to a specific set of antigens presented in a given donor and allows detecting the fingerprint of past and present infection. Furthermore, clonotypes with similar sequences can be aggregated into 'metaclonotype' biomarkers based on clonotype graph analysis.</p> <p>Finally, we train various COVID-19 status classifiers on selected batches from Cohort-I data using different algorithms and incorporating different feature sets. Verification of the robustness of our results was performed using independent batches of the Cohort-I and data from Cohort-II published previously.</p>
Data from: Towards robust evolutionary inference with integral projection models
Integral projection models (IPMs) are extremely flexible tools for ecological and evolutionary inference. IPMs track the distribution of phenotype in populations through time, using functions describing phenotype-dependent development, inheritance, survival and fecundity. For evolutionary inference, two important features of any model are the ability to (i) characterize relationships among traits (including values of the same traits across ages) within individuals, and (ii) characterize similarity between individuals and their descendants. In IPM analyses, the former depends on regressions of observed trait values at each age on values at the previous age (development functions), and the latter on regressions of offspring values at birth on parent values as adults (inheritance functions). We show analytically that development functions, characterized this way, will typically underestimate covariances of trait values across ages, due to compounding of regression to the mean across projection steps. Similarly, we show that inheritance, characterized this way, is inconsistent with a modern understanding of inheritance, and underestimates the degree to which relatives are phenotypically similar. Additionally, we show that the use of a constant biometric inheritance function, particularly with a constant intercept, is incompatible with evolution. Consequently, current implementations of IPMs will predict little or no phenotypic evolution, purely as artefacts of their construction. We present alternative approaches to constructing development and inheritance functions, based on a quantitative genetic approach, and show analytically and through an empirical example on a population of bighorn sheep how they can potentially recover patterns that are critical to evolutionary inference.
Data for the paper Robust expansion of extreme extratropical storms under global warming
<p>Data for the paper "Robust expansion of extreme extratropical storms under global warming"</p>
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis
<p>A detailed list of the selected scRNA-seq datasets used in the paper, also provided in Additional file <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1898-6#MOESM1">1</a>: Table S1-S2.</p>
EasyDIA a comprehensive and robust pipeline for DIA proteomics research and its application in multi-organ mouse proteomics atlas generation
<p>Supplementary data of the article " EasyDIA: a comprehensive and robust pipeline for DIA proteomics research, and its application in multi-organ mouse proteomics atlas generation". Mass spectrometry-based proteomics measurement has emerged as a routine tool for detecting protein constitution and expression level change in most organisms. But till now, long-term stability, reproducibility, throughput, identification depth, multi-center consistency, robustness cannot be satisfied simultaneously by traditional proteomics method. We introduced EasyDIA pipeline with widely used commercial products, state-of-art direct DIA-PASEF method and EasyDIA software platform for data quality evaluation and bioinformatics analysis. With EasyDIA pipeline, even entry level researchers can conduct a complete proteomics experiment in less than one day. We further generated a multi-organ mouse proteomics atlas dataset in less than four days. Up to 8000 protein groups can be quantified in a single organism with only 60 minutes LC gradient. This pipeline provides a promising solution for both small-scale proteomics experiment and large-cohort clinical research.</p>
MicroED Characterization of a Robust Cationic σ-Alkane Complex Stabilized by the [B(3,5-(SF5)2C6H3)4]– Anion, via On-Grid Solid/Gas Single-Crystal to Single-Crystal Reactivity.
<p>3DED/MicroED datasets collected using a Thermo Fisher Scientific Glacios microscope equipped with a Ceta-D Camera associated with the publication "MicroED Characterization of a Robust Cationic σ-Alkane Complex Stabilized by the [B(3,5- (SF5)2C6H3)4]– Anion, via On-Grid Solid/Gas Single-Crystal to Single-Crystal Reactivity." published in <a href="https://doi.org/10.1039/D2DT00335J">Dalton Transactions</a>. </p> <p>Data collection details: </p> <p>200kV, microprobe, gun lens 4, spot size 11, 30 µm C2 aperture. Illuminated area was ~4 µm and a 40 µm SA aperture was used. Under these conditions flux is ~0.01 e-/Å^2/s. Camera length calibrated from Al powder was 958.5 mm. Data acquired using EPU-D with following settings: 2x binning, 0.5 °/s, 2s exposure, rolling shutter, noise reduction mode enabled. </p> <p>MRC format images can be processed with DIALS using the FormatMRC dxtbx format (distributed with DIALS from version 3.5 onwards), use goniometer.axes=1,0,0. Some processing notes for the σ-alkane Complex ([1-NBA][S-BArF4] in the paper) can be found <a href="https://github.com/huwjenkins/ed_scripts/wiki/Processing-%5BRh(NBA)(dcpe)%5D%5BB(ArSF5)4%5D-data-from-zenodo.org-record-5760938">here</a>.</p>
"Robust Folding of Elastic Origami" Data Supplement
<p>Data sets for "Robust Folding of Elastic Origami", meant to be used with Mathematica notebooks provided at <a href="https://github.com/meleetrimble/robust-folding-paper-support">https://github.com/meleetrimble/robust-folding-paper-support</a>.</p>
Data for the paper Robust expansion of extreme extratropical storms under global warming (CMIP6)
<p>Data for the paper "Robust expansion of extreme extratropical storms under global warming"</p>
On following pages: 272. SulawesiYellow Bat (Scotophilus celebensis); 273. Nut-colored Yellow Bat (Scotophilus nux); 274. Malagasy Yellow Bat (Scotophilus tandrefana); 275. Marovaza Yellow Bat (Scotophilus marovaza); 276. White-bellied Yellow Bat (Scotophilus leucogaster); 277. East African Yellow Bat (Scotophilus altilis); 278. Robust Yellow Bat (Scotophilus robustus); 279. LesserYellow Bat (Scotophilus borbonicus); 280. Eastern Greenish Yellow Bat (Scotophilus viridis); 281. Ejeta's Yellow Bat (Scotophilus ejetal); 282. Schreber's Yellow Bat (Scotophilus nigrita); 283. Robbins's Yellow Bat (Scotophilus nucella); 284. Andrew Rebori's Yellow Bat (Scotophilus andrewreboril); 285. Western Greenish Yellow Bat (Scotophilus nigritellus); 286. Livingstone's Yellow Bat (Scotophilus livingstonii); 287. Trujillo's Yellow Bat (Scotophilus trujillo); 288. African Yellow Bat (Scotophilus dinganii); 289. Eritrean Yellow Bat (Scotophilus colias); 290. North American Evening Bat (Nycticeius humeralis); 291. Cuban Evening Bat (Nycticeius cubanus); 292. Temminck''s Mysterious Bat (Nycticeius aenobarbus). in Vespertilionidae
On following pages: 272. SulawesiYellow Bat (Scotophilus celebensis); 273. Nut-colored Yellow Bat (Scotophilus nux); 274. Malagasy Yellow Bat (Scotophilus tandrefana); 275. Marovaza Yellow Bat (Scotophilus marovaza); 276. White-bellied Yellow Bat (Scotophilus leucogaster); 277. East African Yellow Bat (Scotophilus altilis); 278. Robust Yellow Bat (Scotophilus robustus); 279. LesserYellow Bat (Scotophilus borbonicus); 280. Eastern Greenish Yellow Bat (Scotophilus viridis); 281. Ejeta's Yellow Bat (Scotophilus ejetal); 282. Schreber's Yellow Bat (Scotophilus nigrita); 283. Robbins's Yellow Bat (Scotophilus nucella); 284. Andrew Rebori's Yellow Bat (Scotophilus andrewreboril); 285. Western Greenish Yellow Bat (Scotophilus nigritellus); 286. Livingstone's Yellow Bat (Scotophilus livingstonii); 287. Trujillo's Yellow Bat (Scotophilus trujillo); 288. African Yellow Bat (Scotophilus dinganii); 289. Eritrean Yellow Bat (Scotophilus colias); 290. North American Evening Bat (Nycticeius humeralis); 291. Cuban Evening Bat (Nycticeius cubanus); 292. Temminck''s Mysterious Bat (Nycticeius aenobarbus).
On following pages: 48. Paraguayan Tuco-tuco (Ctenomys paraguayensis); 49. Pilar Tuco-tuco (Ctenomys pilarensis (Ctenomys coludo); 53. Famatina Tuco-tuco (Ctenomys famosus); 54. Foch's Tuco-tuco (Ctenomys fochi); 55. Jujuy tuconax); 58. Monte Tuco-tuco (Ctenomys viperinus); 59. Santa Fe Tuco-tuco (Ctenomys " yolandae"); 60. Azara's johannis); 63. Osvaldo Reig's Tuco-tuco (Ctenomys osvaldoreigi); 64. Brown Tuco-tuco (Ctenomys pontifex); 65. Rosendo Guaymallen Tuco-tuco (Ctenomys validus); 68. Emilio's Tuco-tuco (Ctenomys emilianus); 69. Colonial Tuco-tuco (Ctenomys); 50. Maule Tuco-tuco (Ctenomys maulinus); 51. Bonetto's Tuco-tuco (Ctenomys bonettol); 52. Puntilla Tuco-tuco Tuco-tuco (Ctenomysjuris); 56. Catamarca Tuco-tuco (Ctenomys knight); 57. Robust Tuco-tuco (Ctenomys Tuco-tuco (Ctenomys azarae); 61. Cordoba Tuco-tuco (Ctenomys berg); 62. San Juan Tuco-tuco (Ctenomys Pascual's Tuco-tuco (Ctenomys rosendopascuali); 66. Sierra Tontal Tuco-tuco (Ctenomys tulduco); 67. sociabilis). in Ctenomyidae
On following pages: 48. Paraguayan Tuco-tuco (Ctenomys paraguayensis); 49. Pilar Tuco-tuco (Ctenomys pilarensis (Ctenomys coludo); 53. Famatina Tuco-tuco (Ctenomys famosus); 54. Foch's Tuco-tuco (Ctenomys fochi); 55. Jujuy tuconax); 58. Monte Tuco-tuco (Ctenomys viperinus); 59. Santa Fe Tuco-tuco (Ctenomys " yolandae"); 60. Azara's johannis); 63. Osvaldo Reig's Tuco-tuco (Ctenomys osvaldoreigi); 64. Brown Tuco-tuco (Ctenomys pontifex); 65. Rosendo Guaymallen Tuco-tuco (Ctenomys validus); 68. Emilio's Tuco-tuco (Ctenomys emilianus); 69. Colonial Tuco-tuco (Ctenomys); 50. Maule Tuco-tuco (Ctenomys maulinus); 51. Bonetto's Tuco-tuco (Ctenomys bonettol); 52. Puntilla Tuco-tuco Tuco-tuco (Ctenomysjuris); 56. Catamarca Tuco-tuco (Ctenomys knight); 57. Robust Tuco-tuco (Ctenomys Tuco-tuco (Ctenomys azarae); 61. Cordoba Tuco-tuco (Ctenomys berg); 62. San Juan Tuco-tuco (Ctenomys Pascual's Tuco-tuco (Ctenomys rosendopascuali); 66. Sierra Tontal Tuco-tuco (Ctenomys tulduco); 67. sociabilis).
Distribution. SW Brazil, known only from two sites, the type locality in Rondonia and Juruena (Mato Grosso State)Descriptive notes Head-body ¢.230 mm, tail ¢.80 mm. No specific data are available for body weight. Rondon's Tuco-tuco is medium-sized. Dorsal hairs are pale at bases and sepia at tips. Head and venterare slightly rufous, and tail is uniform brown. Skull is robust and depressed. Inter-maxillaries are also robust, with lateral protruding expansion; maxillaries are narrow; and mandible is strong and wide. Supraorbital process protrudes, and traverse occipital-temporal crest is straight. Bullae are inflated. in Ctenomyidae
Distribution. SW Brazil, known only from two sites, the type locality in Rondonia and Juruena (Mato Grosso State)Descriptive notes Head-body ¢.230 mm, tail ¢.80 mm. No specific data are available for body weight. Rondon's Tuco-tuco is medium-sized. Dorsal hairs are pale at bases and sepia at tips. Head and venterare slightly rufous, and tail is uniform brown. Skull is robust and depressed. Inter-maxillaries are also robust, with lateral protruding expansion; maxillaries are narrow; and mandible is strong and wide. Supraorbital process protrudes, and traverse occipital-temporal crest is straight. Bullae are inflated.
On following pages: 18. Omilteme Cottontail (Sylvilagus insonus); 19. Common Tapeti (Sylvilagus brasiliensis); 20 Cottontail (Sylvilagus dice); 23. Mexican Cottontail (Sylvilagus cunicularius); 24. Tres Marias Cottontail (Sylvilagus Robust Cottontail (Sylvilagus robustus); 28. Manzano Mountain Cottontail (Sylvilagus cognatus); 29. Hispid Hare (. Central American Tapeti (Sylvilagus gabbi); 21. Venezuelan Lowland Rabbit (Sylvilagus varynaensis), 22. Dice's graysoni); 25. Eastern Cottontail (Sylvilagus floridanus); 26. Appalachian Cottontail (Sylvilagus obscurus); 27. Caprolagus hispidus); 30. Bunyoro Rabbit (Poelagus marjorita); 31. European Rabbit (Oryctolagus cuniculus). in Leporidae
On following pages: 18. Omilteme Cottontail (Sylvilagus insonus); 19. Common Tapeti (Sylvilagus brasiliensis); 20 Cottontail (Sylvilagus dice); 23. Mexican Cottontail (Sylvilagus cunicularius); 24. Tres Marias Cottontail (Sylvilagus Robust Cottontail (Sylvilagus robustus); 28. Manzano Mountain Cottontail (Sylvilagus cognatus); 29. Hispid Hare (. Central American Tapeti (Sylvilagus gabbi); 21. Venezuelan Lowland Rabbit (Sylvilagus varynaensis), 22. Dice's graysoni); 25. Eastern Cottontail (Sylvilagus floridanus); 26. Appalachian Cottontail (Sylvilagus obscurus); 27. Caprolagus hispidus); 30. Bunyoro Rabbit (Poelagus marjorita); 31. European Rabbit (Oryctolagus cuniculus).
PINNup: Robust neural network wavefield solutions using frequency upscaling and neuron splitting
<p>Solving for the frequency-domain scattered wavefield via physics-informed neural network (PINN) has great potential in increasing the flexibility and reducing the computational cost of seismic modeling and inversion. We propose a novel implementation of PINN using frequency upscaling and neuron splitting, which allows the neural network model to grow in size as we increase the frequency while leveraging the information from the pre-trained model for lower-frequency wavefields, resulting in fast convergence to high-accuracy wavefield solutions. In this letter, we present the relevant dataset to the paper. </p>
FIGURE. Morphology of Marchantia species of Sri Lanka. (A) Yellowish green, robust thallus of M. acaulis with a distinct dark median band and dark purplish margin (B) Light green thallus of M. paleacea without a distinct median band (C) Thallus of M. papillata with a distinct black coloured median band (D) Pale green coloured thallus of M. pappeana without a distinct median band (E) Dark green thallus of M. polymorpha (F) Green coloured thallus of M. emarginata with a blackish median band. A—Ruklani & Rubasinghe 108-14SR; B—Ruklani & Rubasinghe 14-14SR; C—Ruklani & Rubasinghe 28-14SR; D—Ruklani & Rubasinghe 55-14SR; E—Ruklani & Rubasinghe 360-15SR; F—Ruklani & Rubasinghe 169-14SR. in Thalloid Liverworts (Marchantiopsida) of Sri Lanka
FIGURE. Morphology of Marchantia species of Sri Lanka. (A) Yellowish green, robust thallus of M. acaulis with a distinct dark median band and dark purplish margin (B) Light green thallus of M. paleacea without a distinct median band (C) Thallus of M. papillata with a distinct black coloured median band (D) Pale green coloured thallus of M. pappeana without a distinct median band (E) Dark green thallus of M. polymorpha (F) Green coloured thallus of M. emarginata with a blackish median band. A—Ruklani & Rubasinghe 108-14SR; B—Ruklani & Rubasinghe 14-14SR; C—Ruklani & Rubasinghe 28-14SR; D—Ruklani & Rubasinghe 55-14SR; E—Ruklani & Rubasinghe 360-15SR; F—Ruklani & Rubasinghe 169-14SR.
Self-assembling peptide nanofiber HIV vaccine elicits robust vaccine-induced antibody functions and modulates Fc glycosylation.
<p>To develop vaccines for certain key global pathogens such as HIV, it is crucial to elicit both neutralizing and non-neutralizing Fc-mediated effector antibody functions. Clinical evidence indicates that non-neutralizing antibody functions including antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) contribute to protection against several pathogens. In this study, we demonstrated that conjugation of HIV Envelop (Env) antigen gp120 to a self-assembling nanofiber material named Q11 induced antibodies with higher breadth and functionality when compared to soluble gp120. Immunization with Q11-conjugated gp120 vaccine (gp120-Q11) demonstrated higher tier 1 neutralization, ADCP and ADCC as compared to soluble gp120. Moreover, Q11 conjugation altered the Fc N-glycosylation profile of antigen-specific antibodies, leading to a phenotype associated with increased ADCC in animals immunized with gp120-Q11. Thus, this nanomaterial vaccine strategy can enhance non-neutralizing antibody functions possibly through modulation of IgG Fc N-glycosylation.</p>
On following pages: 489. Coues's Marsh Rice Rat (Oryzomys couesi); 490. White-bellied Marsh Rice Rat (Oryzomys albiventer), 491. Nicaraguan Marsh Rice Rat (Oryzomys dimidiatus); 492. Gorgas's Marsh Rice Rat (Oryzomys gorgasi, 493. Santiago Galapagos Mouse (Nesoryzomys swarthi); 494. Small Fernandina Galapagos Mouse (Nesoryzomys fernandinae); 495. Large Fernandina Galapagos Mouse (Nesoryzomys narboroughi); 496. Galapagos Rice Rat (Aegialomys galapagoensis); 497 Yellowish Rice Rat (Aegialomys xanthaeolus); 498. Baron's Rice Rat (Aegialomys baroni); 499. Ica Rice Rat (Aegialomys ica); 500. Alfaro's Water Rat (Sigmodontomys alfari); 501. Harris's Rice Water Rat (Tanyuromys aphrastus); 502. Black-and-Yellow Rice Rat (Melanomys chrysomelas); 503. Cinnamon-rufous Rice Rat (Melanomys idoneus); 504. Colombian Rice Rat (Melanomys columbianus); 505. Dusky Rice Rat (Melanomys caliginosus); 506. Robust Dark Rice Rat (Melanomys robustulus); 507. Zuniga's Dark Rice Rat (Melanomys zunigae); 508. Intermediate Lesser Grass Mouse (Microakodontomys transitorius); 509. Lagoa Santa Pink-lipped Mouse (Bibimys labiosus); 510. Chacoan Pink-lipped Mouse (Bibimys chacoensis); 511. Torres's Pink-lipped Mouse (Bibimys torresi), 512. Brazilian Swamp Rat (Scapteromys meridionalis); 513. Argentinean Swamp Rat (Scapteromys aquaticus); 514. Uruguay Swamp Rat (Scapteromys tumidus); 515. Cerrado Giant Rat (Gyldenstolpia planaltensis); 516. Fossorial Giant Rat (Gyldenstolpia fronto); 517. Woolly Giant Rat (Kunsia tomentosus); 518. Andean Rat (Lenoxus apicalis); 519. Atlantic Forest Burrowing Mouse (Blarinomys breviceps); 520. Gray-bellied Brucie (Brucepattersonius griserufescens); 521. Short-tailed Brucie (Brucepattersonius soricinus); 522. Ihering's Brucie (Brucepattersonius iheringi). in Cricetidae
On following pages: 489. Coues's Marsh Rice Rat (Oryzomys couesi); 490. White-bellied Marsh Rice Rat (Oryzomys albiventer), 491. Nicaraguan Marsh Rice Rat (Oryzomys dimidiatus); 492. Gorgas's Marsh Rice Rat (Oryzomys gorgasi, 493. Santiago Galapagos Mouse (Nesoryzomys swarthi); 494. Small Fernandina Galapagos Mouse (Nesoryzomys fernandinae); 495. Large Fernandina Galapagos Mouse (Nesoryzomys narboroughi); 496. Galapagos Rice Rat (Aegialomys galapagoensis); 497 Yellowish Rice Rat (Aegialomys xanthaeolus); 498. Baron's Rice Rat (Aegialomys baroni); 499. Ica Rice Rat (Aegialomys ica); 500. Alfaro's Water Rat (Sigmodontomys alfari); 501. Harris's Rice Water Rat (Tanyuromys aphrastus); 502. Black-and-Yellow Rice Rat (Melanomys chrysomelas); 503. Cinnamon-rufous Rice Rat (Melanomys idoneus); 504. Colombian Rice Rat (Melanomys columbianus); 505. Dusky Rice Rat (Melanomys caliginosus); 506. Robust Dark Rice Rat (Melanomys robustulus); 507. Zuniga's Dark Rice Rat (Melanomys zunigae); 508. Intermediate Lesser Grass Mouse (Microakodontomys transitorius); 509. Lagoa Santa Pink-lipped Mouse (Bibimys labiosus); 510. Chacoan Pink-lipped Mouse (Bibimys chacoensis); 511. Torres's Pink-lipped Mouse (Bibimys torresi), 512. Brazilian Swamp Rat (Scapteromys meridionalis); 513. Argentinean Swamp Rat (Scapteromys aquaticus); 514. Uruguay Swamp Rat (Scapteromys tumidus); 515. Cerrado Giant Rat (Gyldenstolpia planaltensis); 516. Fossorial Giant Rat (Gyldenstolpia fronto); 517. Woolly Giant Rat (Kunsia tomentosus); 518. Andean Rat (Lenoxus apicalis); 519. Atlantic Forest Burrowing Mouse (Blarinomys breviceps); 520. Gray-bellied Brucie (Brucepattersonius griserufescens); 521. Short-tailed Brucie (Brucepattersonius soricinus); 522. Ihering's Brucie (Brucepattersonius iheringi).
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.