cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2k8x4x
title: CCDC 2362049: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Yaofeng Wang, Christian G. Feiler, Manfred S. Weiss, Alexey A. Popov, Fupin Liu|2024|Inorg.Chem.Front.|11|6343|doi:10.1039/D4QI01562B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2k8x5y
title: CCDC 2362050: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Yaofeng Wang, Christian G. Feiler, Manfred S. Weiss, Alexey A. Popov, Fupin Liu|2024|Inorg.Chem.Front.|11|6343|doi:10.1039/D4QI01562B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.60507/fk2/03zgyw/7ctmwp
title: weatherdata-70-25.csv
DOI: 10.5517/ccdc.csd.cc2k8x6z
title: CCDC 2362051: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Yaofeng Wang, Christian G. Feiler, Manfred S. Weiss, Alexey A. Popov, Fupin Liu|2024|Inorg.Chem.Front.|11|6343|doi:10.1039/D4QI01562B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2k8x70
title: CCDC 2362052: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Yaofeng Wang, Christian G. Feiler, Manfred S. Weiss, Alexey A. Popov, Fupin Liu|2024|Inorg.Chem.Front.|11|6343|doi:10.1039/D4QI01562B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2k8x81
title: CCDC 2362053: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Yaofeng Wang, Christian G. Feiler, Manfred S. Weiss, Alexey A. Popov, Fupin Liu|2024|Inorg.Chem.Front.|11|6343|doi:10.1039/D4QI01562B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2k8x92
title: CCDC 2362054: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Yaofeng Wang, Christian G. Feiler, Manfred S. Weiss, Alexey A. Popov, Fupin Liu|2024|Inorg.Chem.Front.|11|6343|doi:10.1039/D4QI01562B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.60507/fk2/03zgyw/ujfknl
title: weatherdata-83-9.csv
DOI: 10.5517/ccdc.csd.cc2k8xb3
title: CCDC 2362055: Experimental Crystal Structure Determination

description: Related Article: Yajuan Hao, Yaofeng Wang, Christian G. Feiler, Manfred S. Weiss, Alexey A. Popov, Fupin Liu|2024|Inorg.Chem.Front.|11|6343|doi:10.1039/D4QI01562B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.60507/fk2/03zgyw/gj62xp
title: weatherdata-73-6.csv
DOI: 10.60507/fk2/03zgyw/nnbr5q
title: weatherdata-76-3.csv
DOI: 10.60507/fk2/03zgyw/7i6xgb
title: weatherdata-61-13.csv
DOI: 10.60507/fk2/03zgyw/uesezc
title: weatherdata-61-16.csv
DOI: 10.5281/zenodo.7758534
title: Macroalgal deep genomics illuminate multiple paths to aquatic, photosynthetic multicellularity - ASSEMBLIES [INTERNATIONAL] - Data S1

description: Macroalgae are a polyphyletic group of multicellular aquatic organisms vital to global climate maintenance and have a wide variety of commercial applications. The lack of genomic datasets and poor physiological records preclude understanding their ecological roles and industrial potential. We de novo sequenced 121 macroalgal genomes from various climates spanning five major latitude parallels. The resultant genomic datasets reveal genetic bases for niche habitation facilitated by morphological complexity in diverse and extreme regions and illuminate the evolutionary mechanisms behind macroalgal diversification and specialization. Adhesome genes (e.g., cadherins, integrins, and lectins), extracellular matrix enzymes, and cytoskeletal organization regulating genes (e.g., spondins, Rho-type GTPases) predominantly distinguished macroalgal genomes from their microalgae correlates. Deep neural networks could accurately classify an alga as micro- or macro- from set of significance-ranked genomic features (n = 251, entropy R2 > 0.99, RASE = 0.001) as well as adhesome gene sets (n = 110, entropy R2 > 0.86). By deciphering the macroalgal adhesome, a clear picture of the genetic basis for the development and maintenance of complex algal tissues could be resolved. Sequences from giant viruses were rampant in the macroalgal genomes and coded for zinc-finger transcription factors, ankyrins, Rieske proteins, and other exotic codomains. Lineage-specific retentions of transcription factors, cadherins, integrins, polysaccharide-acting enzymes, and receptor kinases, many with predicted viral origins, outline the divergent mechanisms facilitating multicellularity in these three macroalgal lineages. This work sheds new light on the evolution of multicellularity in three phyla (Rhodophyceae, Chlorophyceae, and Ochrophyceae v. Phaeophyceae) through the lens of large-scale genomics and paves the way for the genomic exploration of macroalgal biology.
DOI: 10.60507/fk2/03zgyw/o9gtq9
title: weatherdata-86-19.csv
DOI: 10.5281/zenodo.7758533
title: Macroalgal deep genomics illuminate multiple paths to aquatic, photosynthetic multicellularity - ASSEMBLIES [INTERNATIONAL] - Data S1

description: Macroalgae are a polyphyletic group of multicellular aquatic organisms vital to global climate maintenance and have a wide variety of commercial applications. The lack of genomic datasets and poor physiological records preclude understanding their ecological roles and industrial potential. We de novo sequenced 121 macroalgal genomes from various climates spanning five major latitude parallels. The resultant genomic datasets reveal genetic bases for niche habitation facilitated by morphological complexity in diverse and extreme regions and illuminate the evolutionary mechanisms behind macroalgal diversification and specialization. Adhesome genes (e.g., cadherins, integrins, and lectins), extracellular matrix enzymes, and cytoskeletal organization regulating genes (e.g., spondins, Rho-type GTPases) predominantly distinguished macroalgal genomes from their microalgae correlates. Deep neural networks could accurately classify an alga as micro- or macro- from set of significance-ranked genomic features (n = 251, entropy R2 > 0.99, RASE = 0.001) as well as adhesome gene sets (n = 110, entropy R2 > 0.86). By deciphering the macroalgal adhesome, a clear picture of the genetic basis for the development and maintenance of complex algal tissues could be resolved. Sequences from giant viruses were rampant in the macroalgal genomes and coded for zinc-finger transcription factors, ankyrins, Rieske proteins, and other exotic codomains. Lineage-specific retentions of transcription factors, cadherins, integrins, polysaccharide-acting enzymes, and receptor kinases, many with predicted viral origins, outline the divergent mechanisms facilitating multicellularity in these three macroalgal lineages. This work sheds new light on the evolution of multicellularity in three phyla (Rhodophyceae, Chlorophyceae, and Ochrophyceae v. Phaeophyceae) through the lens of large-scale genomics and paves the way for the genomic exploration of macroalgal biology.
DOI: 10.60507/fk2/03zgyw/p9hfq0
title: weatherdata-76-16.csv
DOI: 10.60507/fk2/03zgyw/zkqxzg
title: weatherdata-67-13.csv
DOI: 10.60507/fk2/03zgyw/8i8fpg
title: weatherdata-67-19.csv
DOI: 10.60507/fk2/03zgyw/oxvplf
title: weatherdata-86-25.csv
DOI: 10.60507/fk2/03zgyw/bmtybx
title: weatherdata-86-13.csv
DOI: 10.60507/fk2/03zgyw/ckhjmk
title: weatherdata-83-25.csv
DOI: 10.60507/fk2/03zgyw/pvw8kg
title: weatherdata-64-22.csv
DOI: 10.60507/fk2/03zgyw/eafncr
title: weatherdata-70-19.csv
DOI: 10.60507/fk2/03zgyw/mdgkbu
title: weatherdata-67-25.csv
DOI: 10.60507/fk2/03zgyw/t92lmc
title: weatherdata-73-28.csv
DOI: 10.60507/fk2/03zgyw/ppmrip
title: weatherdata-64-16.csv
DOI: 10.60507/fk2/03zgyw/jum1rs
title: weatherdata-70-9.csv
DOI: 10.60507/fk2/03zgyw/qjmzp8
title: weatherdata-70-16.csv
DOI: 10.60507/fk2/03zgyw/bs1loe
title: weatherdata-73-16.csv
DOI: 10.60507/fk2/03zgyw/tsnbee
title: weatherdata-67-28.csv
DOI: 10.60507/fk2/03zgyw/gsp9pq
title: weatherdata-730.csv
DOI: 10.60507/fk2/03zgyw/f885fw
title: weatherdata-80-9.csv
DOI: 10.60507/fk2/4zq00s/n25wrt
title: metadata.xml
DOI: 10.60507/fk2/4zq00s/uhoxtw
title: Fragile_state_Index_2018_Rank__Coh_Ind_Group_Grievance.csv
DOI: 10.60507/fk2/4zq00s/cblokn
title: Fragile_state_Index_2018_Rank__Cohesion_Indicators__Group_Grievance_.pdf
DOI: 10.60507/fk2/4zq00s/gyenjb
title: Fragile_state_Index_2018_Rank__Cohesion_Indicators__Group_Grievance_.jpg
DOI: 10.60507/fk2/4zq00s/jw3ad9
title: proj_strive.png
DOI: 10.60507/fk2/tj9tfu/gsfhvh
title: metadata.xml
DOI: 10.60507/fk2/tj9tfu/zgonvh
title: Fragile_state_Index_2018_Rank_Political_Indicator___Public_Services_.pdf
DOI: 10.60507/fk2/tj9tfu/dhuqiu
title: proj_strive.png
DOI: 10.60507/fk2/tj9tfu/mxw0rx
title: Fragile_state_Index_2018_Rank_Political_Indicator___Public_Services_.jpg
DOI: 10.60507/fk2/tj9tfu/ougp2i
title: ReadMe_Variables.txt
DOI: 10.60507/fk2/tj9tfu/wia3v7
title: Fragile_state_Index_2018_Rank_Pol_Indicator_Public_Services.csv
DOI: 10.60507/fk2/nrj3dq/sezs2g
title: metadata.xml
DOI: 10.60507/fk2/nrj3dq/jy89mg
title: Fragile_state_Index_2018_Rank__Economic_Indicator___Economic_Decline_.pdf
DOI: 10.60507/fk2/nrj3dq/e7zwlw
title: Fragile_state_Index_2018_Rank_Econ_Ind_Econ_Decl.csv
DOI: 10.60507/fk2/nrj3dq/nkf1uv
title: Fragile_state_Index_2018_Rank__Economic_Indicator___Economic_Decline_.jpg
DOI: 10.60507/fk2/nrj3dq/9wurqc
title: proj_strive.png
DOI: 10.60507/fk2/nrj3dq/bssn9t
title: ReadMe_Variables.txt
DOI: 10.60507/fk2/nnt6r6/psjnqa
title: metadata.xml
DOI: 10.60507/fk2/nnt6r6/mlz3qv
title: Agronomic Radiation Use Efficiency (RUE) in Maize Stover Biomass in Ethiopia_300dpi.png
DOI: 10.60507/fk2/nnt6r6/cu3xis
title: BIOW_agr_RUE_maize-stoverBiomass_unfertilized_ethiopia_pol.zip
DOI: 10.60507/fk2/nnt6r6/qgi8sl
title: proj_BiomassWEB.png
DOI: 10.60507/fk2/nnt6r6/s6jjnw
title: Agronomic_Radiation_Use_Efficiency_RUE_in_Maize_Stover_Biomass_in_Ethiopia_new.png
DOI: 10.60507/fk2/7p8nns/psmzfj
title: metadata.xml
DOI: 10.60507/fk2/7p8nns/ovpsqc
title: Ranking_of_Economy___Getting_Credit_.pdf
DOI: 10.60507/fk2/7p8nns/vdbd5p
title: Ranking_of_Economy___Getting_Credit_.jpg
DOI: 10.60507/fk2/7p8nns/uv6tjm
title: Ranking_of_Economy_Getting_Credit.csv
DOI: 10.60507/fk2/7p8nns/7v7h7p
title: proj_strive.png
DOI: 10.60507/fk2/7p8nns/n1lnik
title: ReadMe_Variables.txt
DOI: 10.60507/fk2/4hpjdk/tahwmm
title: metadata.xml
DOI: 10.60507/fk2/4hpjdk/nesois
title: Tanzania_PhD_Database.xlsb
DOI: 10.60507/fk2/4hpjdk/zjpwvn
title: statistic-2.PNG
DOI: 10.60507/fk2/fc9oe8/i5badd
title: metadata.xml
DOI: 10.60507/fk2/fc9oe8/m3nuwl
title: Soil_data_Ghana_ERA_points.xls
DOI: 10.60507/fk2/fc9oe8/6kzxy0
title: proj_BiomassWEB.png
DOI: 10.60507/fk2/fc9oe8/li1ijo
title: type_datasets.png
DOI: 10.60507/fk2/fc9oe8/rzseji
title: ReadMe_Variables.txt
DOI: 10.60507/fk2/qswkf8/8eeabx
title: MargHotspots_BGDs_clear.jpg
DOI: 10.60507/fk2/qswkf8/cmtvx1
title: metadata.xml
DOI: 10.60507/fk2/qswkf8/vfkn6i
title: MargHotspots_BGDs_clear_s.png
DOI: 10.60507/fk2/qswkf8/dvsedt
title: MGP_Dimensions_Bangladesh_2005-10.zip
DOI: 10.60507/fk2/qswkf8/gzvk2i
title: Margip_BMGF_Edited.gif
DOI: 10.60507/fk2/wq1eay/xjset6
title: metadata.xml
DOI: 10.60507/fk2/wq1eay/merfvk
title: ReadMe-variables.txt
DOI: 10.60507/fk2/wq1eay/ztlmag
title: quantitativedata.png
DOI: 10.60507/fk2/wq1eay/tunhx0
title: ZEF_econ_agricultural-system_elasticities_brazil_2006.csv
DOI: 10.5281/zenodo.7751044
title: Macroalgal deep genomics illuminate multiple paths to aquatic, photosynthetic multicellularity - Supplementary Data - ANNOTATIONS - Data S2

description: Macroalgae are a polyphyletic group of multicellular aquatic organisms vital to global climate maintenance and have a wide variety of commercial applications. The lack of genomic datasets and poor physiological records preclude understanding their ecological roles and industrial potential. We de novo sequenced 121 macroalgal genomes from various climates spanning five major latitude parallels. The resultant genomic datasets illuminate the evolutionary mechanisms behind macroalgal diversification and specialization and reveal genetic bases for niche habitation facilitated by morphological complexity. Adhesome genes (e.g., cadherins, integrins, and lectins), extracellular matrix enzymes, and cytoskeletal organization regulating genes (e.g., spondins, Rho-type GTPases) predominantly distinguished macroalgal genomes from their microalgae correlates. Artificial neural networks could accurately classify an alga as micro- or macro- from set of significance-ranked genomic features (n = 251, entropy R2 > 0.99) as well as adhesome gene sets (n = 110, entropy R2 > 0.92). By deciphering the macroalgal adhesome, a clear picture of the genetic basis for the development and maintenance of complex algal tissues could be resolved in the three macroalgal phyla. Sequences from giant viruses were rampant in the macroalgal genomes and coded for zinc-finger transcription factors, ankyrins, Rieske proteins, and other exotic codomains. Lineage-specific retentions of transcription factors, cadherins, integrins, polysaccharide-acting enzymes, and receptor kinases, many with predicted viral origins, outline the divergent mechanisms facilitating multicellularity in these three macroalgal lineages. This work sheds new light on the evolution of multicellularity in three phyla (Rhodophyceae, Chlorophyceae, and Ochrophyceae v. Phaeophyceae) through the lens of large-scale genomics and paves the way for the genomic exploration of macroalgal biology.
DOI: 10.5281/zenodo.7751045
title: Macroalgal deep genomics illuminate multiple paths to aquatic, photosynthetic multicellularity - Supplementary Data - ANNOTATIONS - Data S2

description: Macroalgae are a polyphyletic group of multicellular aquatic organisms vital to global climate maintenance and have a wide variety of commercial applications. The lack of genomic datasets and poor physiological records preclude understanding their ecological roles and industrial potential. We de novo sequenced 121 macroalgal genomes from various climates spanning five major latitude parallels. The resultant genomic datasets illuminate the evolutionary mechanisms behind macroalgal diversification and specialization and reveal genetic bases for niche habitation facilitated by morphological complexity. Adhesome genes (e.g., cadherins, integrins, and lectins), extracellular matrix enzymes, and cytoskeletal organization regulating genes (e.g., spondins, Rho-type GTPases) predominantly distinguished macroalgal genomes from their microalgae correlates. Artificial neural networks could accurately classify an alga as micro- or macro- from set of significance-ranked genomic features (n = 251, entropy R2 > 0.99) as well as adhesome gene sets (n = 110, entropy R2 > 0.92). By deciphering the macroalgal adhesome, a clear picture of the genetic basis for the development and maintenance of complex algal tissues could be resolved in the three macroalgal phyla. Sequences from giant viruses were rampant in the macroalgal genomes and coded for zinc-finger transcription factors, ankyrins, Rieske proteins, and other exotic codomains. Lineage-specific retentions of transcription factors, cadherins, integrins, polysaccharide-acting enzymes, and receptor kinases, many with predicted viral origins, outline the divergent mechanisms facilitating multicellularity in these three macroalgal lineages. This work sheds new light on the evolution of multicellularity in three phyla (Rhodophyceae, Chlorophyceae, and Ochrophyceae v. Phaeophyceae) through the lens of large-scale genomics and paves the way for the genomic exploration of macroalgal biology.
DOI: 10.60507/fk2/wwjo7w/xgmosr
title: Map-new.PNG
DOI: 10.60507/fk2/wwjo7w/gzye13
title: bufu_126_2041_etss_upper.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/ztqewj
title: bufu_126_2041_etss_mean.tif
DOI: 10.60507/fk2/wwjo7w/0vfybr
title: bufu_126_2041_mtss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/lakctj
title: bufu_126_2041_mtss_lower.tif
DOI: 10.60507/fk2/wwjo7w/350kuk
title: bufu_126_2041_bto_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/blrfbt
title: bufu_126_2081_bto_upper.tif
DOI: 10.60507/fk2/wwjo7w/i2rxf5
title: bufu_126_2081_etss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/mrc5qj
title: bufu_126_2081_etss_lower.tif
DOI: 10.60507/fk2/wwjo7w/3fxfxk
title: bufu_126_2081_mtss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/seng2b
title: bufu_126_2081_mtss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/b2phbj
title: bufu_126_2041_etss_upper.tif
DOI: 10.60507/fk2/wwjo7w/cmjs4a
title: bufu_126_2081_tp_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/5ozj3o
title: bufu_126_2081_bto_mean.tif
DOI: 10.60507/fk2/wwjo7w/yabakc
title: bufu_245_2041_etss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/1geasx
title: bufu_245_2041_mtss_lower.tif
DOI: 10.60507/fk2/wwjo7w/hckgsn
title: bufu_245_2041_mtss_upper.tif
DOI: 10.60507/fk2/wwjo7w/hqd76i
title: bufu_245_2041_tp_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/wyjq6o
title: bufu_245_2081_bto_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/6taeni
title: bufu_245_2081_bto_upper.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/aulkus
title: bufu_245_2081_bto_upper.tif
DOI: 10.60507/fk2/wwjo7w/xpvvsx
title: bufu_245_2081_etss_lower.tif
DOI: 10.60507/fk2/wwjo7w/og8uz5
title: bufu_245_2081_etss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/8cy92t
title: bufu_245_2081_mtss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/7igblf
title: bufu_245_2081_mtss_upper.tif
DOI: 10.60507/fk2/wwjo7w/rxcsim
title: bufu_245_2081_mtss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/j08xxe
title: bufu_245_2081_tp_lower.tif
DOI: 10.60507/fk2/wwjo7w/z4nglv
title: bufu_245_2081_tp_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/cd6gmz
title: bufu_370_2041_bto_upper.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/gbq4pd
title: bufu_370_2041_etss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/y8okjy
title: bufu_245_2041_bto_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/30nssu
title: bufu_370_2041_etss_mean.tif
DOI: 10.60507/fk2/wwjo7w/ejlzpy
title: bufu_370_2041_mtss_mean.tif
DOI: 10.60507/fk2/wwjo7w/3palzi
title: bufu_370_2041_mtss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/6kz4c3
title: bufu_370_2041_mtss_upper.tif
DOI: 10.60507/fk2/wwjo7w/zq12qq
title: bufu_126_2041_tp_upper.tif
DOI: 10.60507/fk2/wwjo7w/vs5qnq
title: bufu_370_2081_bto_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/kmipk2
title: bufu_370_2081_bto_upper.tif
DOI: 10.60507/fk2/wwjo7w/qs8gjg
title: bufu_370_2081_etss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/zvhjut
title: bufu_370_2081_bto_upper.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/wk7ivq
title: bufu_370_2081_mtss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/srbbxm
title: bufu_370_2081_tp_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/ubuklk
title: bufu_370_2081_tp_mean.tif.aux.xml
DOI: 10.15468/dl.pezuv4
title: Occurrence Download

description: A dataset containing 161 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Glaucidium brasilianum cactorum van Rossem, 1937"
 ]
}

 The dataset includes 161 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022006-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60507/fk2/wwjo7w/qam4rp
title: bufu_585_2041_bto_lower.tif
DOI: 10.60507/fk2/wwjo7w/xd6qvt
title: bufu_245_2081_tp_upper.tif
DOI: 10.60507/fk2/wwjo7w/go4cwv
title: bufu_585_2041_etss_lower.tif
DOI: 10.60507/fk2/wwjo7w/aax1um
title: bufu_585_2041_etss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/yfqb0l
title: bufu_126_2081_mtss_lower.tif
DOI: 10.60507/fk2/wwjo7w/zvkcvv
title: bufu_585_2041_mtss_lower.tif
DOI: 10.60507/fk2/wwjo7w/bxvig1
title: bufu_585_2041_mtss_upper.tif
DOI: 10.60507/fk2/wwjo7w/2nzmut
title: bufu_585_2041_tp_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/qpa3mn
title: bufu_585_2041_tp_upper.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/xmokwe
title: bufu_585_2041_mtss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/johhl8
title: bufu_245_2041_etss_lower.tif
DOI: 10.60507/fk2/wwjo7w/rbxnaf
title: bufu_585_2081_bto_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/xhbqqk
title: bufu_585_2081_bto_lower.tif
DOI: 10.60507/fk2/wwjo7w/pm4wxs
title: bufu_585_2081_etss_mean.tif
DOI: 10.60507/fk2/wwjo7w/atwsl8
title: bufu_370_2081_mtss_upper.tif
DOI: 10.60507/fk2/wwjo7w/1ke3tk
title: bufu_585_2081_mtss_mean.tif
DOI: 10.60507/fk2/wwjo7w/dx5nfu
title: bufu_245_2081_etss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/ydxvy4
title: bufu_585_2081_mtss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/ftpgyb
title: bufu_585_2081_tp_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/lk0wox
title: bufu_370_2041_mtss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/ms1pps
title: bufu_585_2081_tp_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/ksczli
title: bufu_585_2081_tp_upper.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/41ilgh
title: bufu_current_etss_lower.tif
DOI: 10.5281/zenodo.13952368
title: The relevant data and trained model of LGS-PPIS
DOI: 10.5281/zenodo.13952369
title: The relevant data and trained model of LGS-PPIS
DOI: 10.60507/fk2/wwjo7w/6x2hmb
title: bufu_current_etss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/rq6nc5
title: bufu_current_etss_mean.tif
DOI: 10.60507/fk2/wwjo7w/9lwg0b
title: bufu_126_2041_bto_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/nljix3
title: bufu_current_mtss_upper.tif
DOI: 10.60507/fk2/wwjo7w/8qci3a
title: bufu_current_tp_mean.tif
DOI: 10.60507/fk2/wwjo7w/acu5j6
title: bufu_current_tp_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/2djmaa
title: bufu_current_tp_upper.tif
DOI: 10.60507/fk2/wwjo7w/9jbvkz
title: bufu_126_2041_mtss_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/3x6rry
title: chpa_126_2041_etss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/girdha
title: bufu_370_2041_tp_upper.tif
DOI: 10.60507/fk2/wwjo7w/u7r891
title: bufu_245_2041_tp_lower.tif
DOI: 10.60507/fk2/wwjo7w/fz8kpd
title: bufu_585_2081_bto_mean.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/lpfypg
title: chpa_126_2041_etss_mean.tif
DOI: 10.60507/fk2/wwjo7w/leqzaz
title: chpa_126_2041_mtss_lower.tif.aux.xml
DOI: 10.60507/fk2/wwjo7w/ahfsa2
title: chpa_126_2041_mtss_upper.tif.aux.xml