cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e116269v1-22025
title: Replication data for: Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach

description: Using a Bayesian likelihood approach, we estimate a dynamic stochastic general equilibrium model for the US economy using seven macroeconomic time series. The model incorporates many types of real and nominal frictions and seven types of structural shocks. We show that this model is able to compete with Bayesian Vector Autoregression models in out-of-sample prediction. We investigate the relative empirical importance of the various frictions. Finally, using the estimated model, we address a number of key issues in business cycle analysis: What are the sources of business cycle fluctuations? Can the model explain the cross correlation between output and inflation? What are the effects of productivity on hours worked? What are the sources of the "Great Moderation"? (JEL D58, E23, E31, E32)
DOI: 10.5281/zenodo.3630693
title: Russian News 2017

description: new dataset for Dialogue 2020 Taxonomy Enrichment Shared Task
DOI: 10.5281/zenodo.3630702
title: Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.0

description: This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.0 [<em>Rodell et al.,</em> 2004] LSM outputs, available at; http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi The GLDAS-v.2.0 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files; GLDAS.2.0_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009) Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. Other necessary links associated with this database: RAPID model: https://github.com/c-h-david/rapid RAPID model pre-processor (rrr): https://github.com/c-h-david/rrr GLDAS outputs: https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS References: David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1 Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381 Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171
description: {"references": ["David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913\u2013934, https://doi.org/10.1175/2011JHM1345.1", "Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381\u2013394, https://doi.org/10.1175/BAMS-85-3-381", "Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171"]}
DOI: 10.5281/zenodo.3630703
title: Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.0

description: This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.0 [<em>Rodell et al.,</em> 2004] LSM outputs, available at; http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi The GLDAS-v.2.0 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files; GLDAS.2.0_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009) Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. Other necessary links associated with this database: RAPID model: https://github.com/c-h-david/rapid RAPID model pre-processor (rrr): https://github.com/c-h-david/rrr GLDAS outputs: https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS References: David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1 Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381 Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171
description: {"references": ["David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913\u2013934, https://doi.org/10.1175/2011JHM1345.1", "Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381\u2013394, https://doi.org/10.1175/BAMS-85-3-381", "Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171"]}
DOI: 10.15132/10000115
title: Optical redox ratio and endogenous porphyrins in the detection of urinary bladder cancer: a patient biopsy analysis
DOI: 10.6084/m9.figshare.11760597.v4
title: Simulated significant wave height (3-hourly, 2015.01-2100.12) from FIO-ESM v2.0 ssp126 experiment r1i1p1f1

description: Simulated significant wave height (3-hourly, 2015.01-2100.12) from FIO-ESM v2.0 ssp126 experiment (one of future scenarios) r1i1p1f1. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.5281/zenodo.3630740
title: Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.1

description: This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.1 [<em>Rodell et al.,</em> 2004] LSM outputs, available at; http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi The GLDAS-v.2.1 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files; GLDAS.2.1_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009) Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. Other necessary links associated with this database: RAPID model: https://github.com/c-h-david/rapid RAPID model pre-processor (rrr): https://github.com/c-h-david/rrr GLDAS outputs: https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS References: David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1 Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381 Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171
description: {"references": ["David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913\u2013934, https://doi.org/10.1175/2011JHM1345.1", "Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381\u2013394, https://doi.org/10.1175/BAMS-85-3-381", "Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171"]}
DOI: 10.5281/zenodo.3630741
title: Input Runoff Data for RAPID Model Pre-Processor (RRR) from GLDAS-v.2.1

description: This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the GLDAS-v.2.1 [<em>Rodell et al.,</em> 2004] LSM outputs, available at; http://hydro1.gesdisc.eosdis.nasa.gov/daac-bin/OTF/HTTP_services.cgi The GLDAS-v.2.1 outputs (from NOAH Land Surface Models) are available in 1º, 0.25º with 3-hour temporal resolution. The database contains the following files; GLDAS.2.1_NOAH<em><strong>res</strong></em>_3H_<em><strong>yyyy</strong></em>.tar.gz (Note: <em><strong>res</strong></em> = 10 or 025; <em><strong>yyyy</strong></em> = 2000 to 2009) Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. Other necessary links associated with this database: RAPID model: https://github.com/c-h-david/rapid RAPID model pre-processor (rrr): https://github.com/c-h-david/rrr GLDAS outputs: https://disc.gsfc.nasa.gov/datasets?keywords=GLDAS References: David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1 Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381 Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171
description: {"references": ["David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913\u2013934, https://doi.org/10.1175/2011JHM1345.1", "Rodell, M., P. R. Houser, U. Jambor, J. Gottschalck, K. Mitchell, C.-J. Meng, et al. [2004], The global land data assimilation system, Bull. Am. Meteorol. Soc. 85, 381\u2013394, https://doi.org/10.1175/BAMS-85-3-381", "Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171"]}
DOI: 10.5281/zenodo.3630781
title: Input Runoff Data for RAPID Model Pre-Processor (RRR) from ECMWF ERA-Interim/Land

description: This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the ECMWF ERA-Interim/Land [<em>Balsamo et al.,</em> 2015] outputs, available from ECMWF Data Server. The ERA-Interim/Land outputs are available in daily temporal resolution. The database contains the following files; ECMWF_Interim_Land_<strong><em>yyyy</em></strong>.tar.gz (Note: <strong><em>yyyy</em></strong> = 2000 to 2009) Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. Other necessary links associated with this database: RAPID model: https://github.com/c-h-david/rapid RAPID model pre-processor (rrr): https://github.com/c-h-david/rrr ECMWF outputs: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land References: Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015 David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1 Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171
description: {"references": ["Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389\u2013407, https://doi.org/10.5194/hess-19-389-2015", "David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913\u2013934, https://doi.org/10.1175/2011JHM1345.1", "Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171"]}
DOI: 10.5281/zenodo.3630782
title: Input Runoff Data for RAPID Model Pre-Processor (RRR) from ECMWF ERA-Interim/Land

description: This database can be used as the input runoff files in the RAPID model [<em>David et al.,</em> 2011] pre-processor (RRR). The runoff files were acquired/derived from the ECMWF ERA-Interim/Land [<em>Balsamo et al.,</em> 2015] outputs, available from ECMWF Data Server. The ERA-Interim/Land outputs are available in daily temporal resolution. The database contains the following files; ECMWF_Interim_Land_<strong><em>yyyy</em></strong>.tar.gz (Note: <strong><em>yyyy</em></strong> = 2000 to 2009) Note: These runoff data were used by <em>Sikder et al.</em> [2019] to assess the performance of available global LSM runoffs in South and Southeast Asian river basins. Other necessary links associated with this database: RAPID model: https://github.com/c-h-david/rapid RAPID model pre-processor (rrr): https://github.com/c-h-david/rrr ECMWF outputs: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim-land References: Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389–407, https://doi.org/10.5194/hess-19-389-2015 David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913–934, https://doi.org/10.1175/2011JHM1345.1 Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171
description: {"references": ["Balsamo, G., Albergel, C., Beljaars, A., Boussetta, S., Brun, E., Cloke, H., et al. [2015], ERA-Interim/Land: a global land surface reanalysis data set, Hydrol. Earth Syst. Sci., 19, 389\u2013407, https://doi.org/10.5194/hess-19-389-2015", "David, C. H., D. R. Maidment, G. Y. Niu, Z. L. Yang, F. Habets, and V. Eijkhout [2011], River network routing on the NHDPlus dataset, J. Hydrometeorol., 12, 913\u2013934, https://doi.org/10.1175/2011JHM1345.1", "Sikder, M. S., C. H. David, G. H. Allen, X. Qiao, E. J. Nelson, and M. A. Matin [2019], Evaluation of Available Global Runoff Datasets Through a River Model in Support of Transboundary Water Management in South and Southeast Asia, Front. Environ. Sci., 7:171, https://doi.org/10.3389/fenvs.2019.00171"]}
DOI: 10.25345/c54x2t
title: MassIVE MSV000084853 - Salivary Proteome of Cowpea Aphid, Native to Riverside, California
title: The salivary proteome of the cowpea aphid native to Riverside, California.
DOI: 10.5281/zenodo.3630783
title: Genotyping Porcine circovirus 3 (PCV-3) nowadays: does it make sense?

description: Supplementary data 1. List of the accession numbers of the sequences used in the present study. Relevant metadata are also reported.<br> Supplementary figure 1. Maximum likelihood phylogenetic tree based on the ORF2 sequences. The tree nodes have been colour coded based on the respective bootstrap support.
DOI: 10.5281/zenodo.3630784
title: Genotyping Porcine circovirus 3 (PCV-3) nowadays: does it make sense?

description: Supplementary data 1. List of the accession numbers of the sequences used in the present study. Relevant metadata are also reported.<br> Supplementary figure 1. Maximum likelihood phylogenetic tree based on the ORF2 sequences. The tree nodes have been colour coded based on the respective bootstrap support.
DOI: 10.25345/c5rm3p
title: MassIVE MSV000084858 - Mapping of the multiple myeloma cell surface N-glycoproteome and targeted validation in patient samples uncovers immunotherapy target
title: Multiple myeloma (MM) is characterized by clonal expansion of malignant plasma cells in the bone marrow. While recent advances in treatment for MM have improved patient outcomes, the 5-year survival rate remains ~50%. A better understanding of the MM cell surface proteome could facilitate development of new therapies and assist in stratification and monitoring of patient outcomes. In this study, we used a mass spectrometry (MS)-based discovery approach termed cell surface capture (CSC) technology to map the cell surface N-glycoproteome of MM cell lines. We identified 696 MM cell surface N-glycoproteins by CSC and developed targeted detection assays for 73 proteins of interest. We then applied targeted MS analysis to primary MM patient samples, revealing 30 proteins with significantly higher abundance in patient MM cells than controls. Nine of these proteins were identified as potential immunotherapeutic targets, including five that are already under investigation for other cancers. These data will be a valuable resource in the development of biomarkers and therapeutics, and we anticipate that our targeted MS assays will have clinical benefit for the diagnosis, stratification, and treatment of MM patients.
DOI: 10.6073/pasta/2c5b036a91ebe259e715ff3efbc9c39b
title: APT01 Daily precipitation amounts measured at multiple sites across konza prairie

description: Data set contains daily records of precipitation on 10 raingauges at 10 sites on Konza Prairie. Two sites (020A and 002C; SE) have 7-day clocks (one revolution per week), 7 have 24-hour clocks (one revolution per day), and the Headquarters raingauge generates daily data and 15 minute data. The Headquarters raingauge generates data year round. The remaining rain gauges are operated from April 1 to October 31. Precipitation amounts are recorded in mm. As of 2011, the HQ 1 (7-day clock) and HQ 2 (24-hour clock) raingauges have been discontinued and replaced with an Ott Pluvio2 rainguage that began data generation March 2010.
DOI: 10.7910/dvn/uo5xsw/a5gzx8
title: Script_clientelism.R

description: :unav
DOI: 10.7910/dvn/uo5xsw/bm4xh3
title: Dicionário.xls

description: :unav
DOI: 10.7910/dvn/uo5xsw/lbyvhh
title: Methodological notes_20181206.doc

description: :unav
DOI: 10.7910/dvn/uo5xsw/tjk1ph
title: Clientelism_data.csv

description: :unav
DOI: 10.15139/s3/gm9msv
title: Affective Forecasting and Performance 2016 - Study 1

description: Data is from a daily diary study in which participants made predictions about their emotions during upcoming work tasks, and then reported on their experienced emotions and performance over the course of a week.
DOI: 10.5519/qd.hss04vu6
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4502554 records)
DOI: 10.6073/pasta/03807061e5f373654c6ab747d4af317c
title: McMurdo Dry Valleys LTER: Daily summaries of seasonal stream gage measurements from Harnish Creek at F11 in Taylor Valley, Antarctica from 1996 to present

description: As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for the McMurdo Dry Valley's Harnish Creek at F11, located in the Fryxell Basin of Taylor Valley. Measurements commenced during the 1996-97 season and are ongoing.
DOI: 10.5281/zenodo.3630796
title: weecology/forecasts 2020-01-29

description: Archived forecasts for future analysis
DOI: 10.5519/qd.o4q8dy7i
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (785707 records)
DOI: 10.7910/dvn/ls8qof/8li1m5
title: Dicionário de códigos_v4.docx

description: :unav
DOI: 10.7910/dvn/ls8qof/lstipt
title: Instruções_v4.docx

description: :unav
DOI: 10.7910/dvn/ls8qof/vxhtw0
title: Tabelas_v4.docx

description: :unav
DOI: 10.7910/dvn/ls8qof/zxolub
title: Banco de Dados_v4.xlsx

description: :unav
DOI: 10.5281/zenodo.3630793
title: weecology/portalPredictions 2020-01-29

description: Using various models to forecast abundances at Portal