cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.b8gf4z
title: Occurrence Download

description: A dataset containing 694 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cirsium rhaphilepis (Hemsl.) Petr."
 ]
}

 The dataset includes 694 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022877-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15602926
title: YASHIL IQTISODIYOT VA BARQAROR RIVOJLANISH
DOI: 10.5281/zenodo.15602927
title: YASHIL IQTISODIYOT VA BARQAROR RIVOJLANISH
DOI: 10.15468/dl.pagqz5
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ctenopharyngodon idella (Valenciennes, 1844)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.0251 -31.3959,30.5701 -30.7329,30.5701 -28.5786,25.5395 -28.5786,25.5395 -32.9007,28.2048 -32.9007,28.5753 -32.6569,29.4902 -31.7517,30.0251 -31.3959))"
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022878-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29574354
title: Long-Term Effect Estimation When Combining Clinical Trial and Observational Follow-Up Datasets

description: Combining experimental and observational follow-up datasets has received much attention lately. In a survival setting, recent work has used Medicare claims to extend the follow-up period for participants in a prostate cancer clinical trial. This allows the estimation of the long-term effect that cannot be estimated by the trial data alone. In this article, we study the estimation of long-term effect when participants in a clinical trial are linked to an observational follow-up dataset. Such linkages are often incomplete and we formulate incomplete linkages as a missing data problem. We use the popular Cox model to define the long-term effect and we propose two approaches to deal with the missing data problem. The first approach, termed non-linked-as-censored (NLAC), is a simple approach that works when Cox model is correctly specified and linkage satisfies a conditionally independent assumption. To gain robustness against model mis-specification, we propose an inverse probability of linkage weighted approach, along with the augmented inverse probability of weighted method, based on a novel conditional linking at random (CLAR) assumption. We further extend our approach to incorporate time-dependent covariates. Simulation results confirm the validity of our method and we apply our methods to the SWOG study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29574354.v2
title: Long-Term Effect Estimation When Combining Clinical Trial and Observational Follow-Up Datasets

description: Combining experimental and observational follow-up datasets has received much attention lately. In a survival setting, recent work has used Medicare claims to extend the follow-up period for participants in a prostate cancer clinical trial. This allows the estimation of the long-term effect that cannot be estimated by the trial data alone. In this article, we study the estimation of long-term effect when participants in a clinical trial are linked to an observational follow-up dataset. Such linkages are often incomplete and we formulate incomplete linkages as a missing data problem. We use the popular Cox model to define the long-term effect and we propose two approaches to deal with the missing data problem. The first approach, termed non-linked-as-censored (NLAC), is a simple approach that works when Cox model is correctly specified and linkage satisfies a conditionally independent assumption. To gain robustness against model mis-specification, we propose an inverse probability of linkage weighted approach, along with the augmented inverse probability of weighted method, based on a novel conditional linking at random (CLAR) assumption. We further extend our approach to incorporate time-dependent covariates. Simulation results confirm the validity of our method and we apply our methods to the SWOG study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29574354.v1
title: Long-term effect estimation when combining clinical trial and observational follow-up datasets

description: Combining experimental and observational follow-up datasets has received much attention lately. In a survival setting, recent work has used Medicare claims to extend the follow-up period for participants in a prostate cancer clinical trial. This allows the estimation of the long-term effect that cannot be estimated by the trial data alone. In this paper, we study the estimation of long-term effect when participants in a clinical trial are linked to an observational follow-up dataset. Such linkages are often incomplete and we formulate incomplete linkages as a missing data problem. We use the popular Cox model to define the long-term effect and we propose two approaches to deal with the missing data problem. The first approach, termed non-linked-as-censored (NLAC), is a simple approach that works when Cox model is correctly specified and linkage satisfies a conditionally independent assumption. To gain robustness against model mis-specification, we propose an inverse probability of linkage weighted approach, along with the augmented inverse probability of weighted method, based on a novel conditional linking at random (CLAR) assumption. We further extend our approach to incorporate time-dependent covariates. Simulation results confirm the validity of our method and we apply our methods to the SWOG study.
DOI: 10.6084/m9.figshare.29534076
title: Design-Based Uncertainty for Quasi-Experiments

description: Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities of receiving treatment, thereby allowing for rich forms of selection. We provide conditions under which the estimands of popular quasi-experimental estimators correspond to interpretable finite-population causal parameters. We characterize the biases and distortions to inference that arise when these conditions are violated. These results can be used to conduct sensitivity analyses when there are concerns about selection into treatment. Taken together, our results establish a rigorous foundation for quasi-experimental analyses that more closely aligns with the way empirical researchers discuss the variation in the data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29534076.v2
title: Design-Based Uncertainty for Quasi-Experiments

description: Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities of receiving treatment, thereby allowing for rich forms of selection. We provide conditions under which the estimands of popular quasi-experimental estimators correspond to interpretable finite-population causal parameters. We characterize the biases and distortions to inference that arise when these conditions are violated. These results can be used to conduct sensitivity analyses when there are concerns about selection into treatment. Taken together, our results establish a rigorous foundation for quasi-experimental analyses that more closely aligns with the way empirical researchers discuss the variation in the data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29534076.v1
title: Design-Based Uncertainty for Quasi-Experiments*

description: Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This paper develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities of receiving treatment, thereby allowing for rich forms of selection. We provide conditions under which the estimands of popular quasi-experimental estimators correspond to interpretable finite-population causal parameters. We characterize the biases and distortions to inference that arise when these conditions are violated. These results can be used to conduct sensitivity analyses when there are concerns about selection into treatment. Taken together, our results establish a rigorous foundation for quasi-experimental analyses that more closely aligns with the way empirical researchers discuss the variation in the data.
DOI: 10.15468/dl.78bk6d
title: Occurrence Download

description: A dataset containing 478 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oculina patagonica",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-6.6 29.26705, -6.6 50.94, 40.81373 50.94, 40.81373 29.26705, -6.6 29.26705))",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Material sample)"
 ]
}

 The dataset includes 478 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001296-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5urd2e
title: Occurrence Download

description: A dataset containing 1701521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ruellia prostrata, Ruellia simplex, Ruellia tuberosa, Rumex abyssinicus, Rumex acetosella, Rumex alpinus, Rumex confertus, Rumex conglomeratus, Rumex crispus, Rumex hypogaeus, Rumex longifolius, Rumex obtusifolius, Rumex patientia, Rumex paulsenianus, Rumex pseudonatronatus, Rumex pulcher, Rumex sagittatus, Rumex spinosus, Rumex stenophyllus, Rumex thyrsiflorus)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 1701521 records from 3777 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001264-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/8v21g-58g25
title: Hyphomonas session4 34KX

description: high-order DNA, H-NS, nucleoid, asymmetric cell division
description: <strong>Tilt Series Date:</strong> 2008-11-01</p>
<strong>Data Taken By:</strong> Jian Shi</p>
<strong>Species / Specimen:</strong> Hyphomonas neptunium</p>
<strong>Strain:</strong> ATCC 15444</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 120.0 eV/Å²,  defocus: -12.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> rundir</p>
<strong>Processing Software Used:</strong> imod</p>
<strong>Collaborators and Roles:</strong> Cultured by Jian; data collected by Jian; processed by Jian</p>
<strong>Purification / Growth Conditions / Treatment:</strong> exponential phase in PYE media...</p>
<strong>Sample Preparation:</strong> C-flat grid, 10nm gold, 100% humidity...</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/jsa2008-11-01-6/rawdata/</p>08nov01a_b17x1_006.mrc,  Tilt Series (Pixel Size 0.632 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/jsa2008-11-01-6/rawdata/08nov01a_b17x1_006.mrc"
        > <i class="download icon"></i> Download </a></p>

        08nov01a_b17x1_006_1k.rec,  Reconstruction (Pixel Size 1.26 nm), 123.6 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/jsa2008-11-01-6/3dimage_5674/08nov01a_b17x1_006_1k.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.15468/dl.pqmgvu
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aplocheilichthys lacustris, Lacustricola lacustris, Aplocheilichthys macrurus, Lacustricola macrurus, Aplocheilichthys maculatus, Lacustricola maculatus, Aplocheilichthys mediolateralis, Lacustricola mediolateralis, Aplocheilichthys nigrolateralis, Lacustricola nigrolateralis, Aplocheilichthys omoculatus, Lacustricola omoculatus, Aplocheilichthys rudolfianus, Micropanchax rudolfianus, Aplocheilichthys spilauchen, Aplocheilichthys usanguensis, Lacustricola usanguensis, Aplocheilichthys vitschumbaensis, Lacustricola vitschumbaensis, Aplocheilus panchax, Apricaphanius saourensis, Archiaphyosemion guineense, Aristochromis christyi, Arius africanus, Arius festinus, Arius gigas, Arius latiscutatus, Arius madagascariensis, Arius uncinatus, Arnoglossus imperialis, Arnoldichthys spilopterus, Asprotilapia leptura, Xenotilapia leptura, Astatoreochromis alluaudi, Astatoreochromis straeleni, Astatoreochromis vanderhorsti, Astatotilapia bloyeti, Astatotilapia burtoni, Astatotilapia calliptera, Astatotilapia desfontainii, Astatotilapia paludinosa, Haplochromis paludinosus, Astatotilapia stappersii, Astatotilapia tweddlei, Atherina boyeri, Atherina caspia, Atherinomorus duodecimalis, Atopochilus chabanaudi, Atopochilus christyi, Atopochilus macrocephalus, Atopochilus mandevillei, Atopochilus pachychilus, Atopochilus savorgnani, Atopochilus vogti, Atopodontus adriaensi, Auchenoglanis biscutatus, Auchenoglanis occidentalis, Aulonocara aquilonium, Aulonocara auditor, Aulonocara baenschi, Aulonocara brevinidus, Aulonocara brevirostre, Aulonocara ethelwynnae, Aulonocara gertrudae, Aulonocara guentheri, Aulonocara hueseri, Aulonocara jacobfreibergi, Aulonocara kandeense, Aulonocara kandeensis, Aulonocara koningsi, Aulonocara korneliae, Aulonocara maylandi, Aulonocara nyassae, Aulonocara rostratum, Aulonocara saulosi, Aulonocara stonemani, Aulonocara stuartgranti, Aulonocara trematocephalum, Aulonocranus dewindti, Aurigequula fasciata, Auriglobus modestus, Austroglanis barnardi, Austroglanis gilli, Austroglanis sclateri, Awaous aeneofuscus, Awaous bustamantei, Awaous commersoni, Awaous lateristriga, Awaous macrorhynchus, Bagrus bajad, Bagrus caeruleus, Bagrus degeni, Bagrus docmak, Bagrus filamentosus, Bagrus lubosicus, Bagrus meridionalis, Bagrus orientalis, Bagrus ubangensis, Bagrus urostigma)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((58.3101 -20.2751,58.2432 -19.5708,57.8054 -19.3157,57.2737 -19.5421,56.8082 -20.4638,57.1232 -20.9316,57.7947 -20.9681,58.1423 -20.734,58.3101 -20.2751))"
 ]
}

 The dataset includes 8 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005863-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/haehk-xwe29
title: HUVECs infx pNLGFP

description: Raw data files of HUVECs infx pNLGFP
description: <strong>Tilt Series Date:</strong> 2013-09-12</p>
<strong>Data Taken By:</strong> Cora Woodward</p>
<strong>Species / Specimen:</strong> Unspecified</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1.0°, constant angular increment, dosage: 150.0 eV/Å²,  defocus: -6.0 μm,  magnification: 26000x. </p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> UCSFTomo</p>
<strong>Collaborators and Roles:</strong> pNLGFP obtained from Irvin Chen (UCLA)</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Whole cells were grown on Au finder grids and infected with pNLGFP- VSVG. Sample frozen 24h post infection</p>
<strong>Sample Preparation:</strong> manual blotting</p>
description: Files available via S3 at ttps://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/cwl2013-09-12-26/rawdata/</p>13sep12_HuvpNLgfp_24hPI_PI_0003.mrc,  Tilt Series (Pixel Size 0.42 nm), 3.3 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/cwl2013-09-12-26/rawdata/13sep12_HuvpNLgfp_24hPI_PI_0003.mrc"
        > <i class="download icon"></i> Download </a></p>

        13sep12_HuvpNLgfp_24hPI_PI_0003_full.rec,  Reconstruction (Pixel Size 1.68 nm), 430.6 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/cwl2013-09-12-26/3dimage_30957/13sep12_HuvpNLgfp_24hPI_PI_0003_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/nt0wq-qbk33
title: Pdb 110408 696

description: Prosthecobacter debontii, 08nov15a_b696_No1_037,
description: <strong>Tilt Series Date:</strong> 2008-11-15</p>
<strong>Data Taken By:</strong> Martin Pilhofer</p>
<strong>Species / Specimen:</strong> Prosthecobacter debontii</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 180.0 eV/Å²,  defocus: -10.0 μm,  magnification: 22500x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> rundir</p>
<strong>Processing Software Used:</strong> raptor</p>
<strong>Collaborators and Roles:</strong> cultured by Martin Pilhofer; data collected by Martin Pilhofer; processed by Martin Pilhofer</p>
<strong>Sample Preparation:</strong> quantifoil grid, 10nm gold, 100% humidity, blotting 1/-2/2/0/1/4/200</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mpa2008-11-15-33/rawdata/</p>08nov15a_b696_No1_037.mrc,  Tilt Series (Pixel Size 0.95 nm), 981.5 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mpa2008-11-15-33/rawdata/08nov15a_b696_No1_037.mrc"
        > <i class="download icon"></i> Download </a></p>

        08nov15a_b696_No1_037_full.rec,  Reconstruction (Pixel Size 1.9 nm), 1.3 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/mpa2008-11-15-33/3dimage_13186/08nov15a_b696_No1_037_full.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.15468/dl.4eq2vy
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Corydalis kiautschouensis"
 ]
}

 The dataset includes 31 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005864-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22002/azc1k-m1v66
title: Hyphomonas session3 41KX

description: high-order DNA, H-NS, nucleoid, asymmetric cell division
description: <strong>Tilt Series Date:</strong> 2008-08-31</p>
<strong>Data Taken By:</strong> Jian Shi</p>
<strong>Species / Specimen:</strong> Hyphomonas neptunium</p>
<strong>Strain:</strong> ATCC 15444</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -10.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> rundir</p>
<strong>Processing Software Used:</strong> imod</p>
<strong>Collaborators and Roles:</strong> Cultured by Jian; data collected by Jian; processed by Jian</p>
<strong>Purification / Growth Conditions / Treatment:</strong> exponential phase in PYE media...</p>
<strong>Sample Preparation:</strong> C-flat grid, 10nm gold, 100% humidity...</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/jsa2008-08-31-15/rawdata/</p>08aug31a_a1_057.mrc,  Tilt Series (Pixel Size 0.512 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/jsa2008-08-31-15/rawdata/08aug31a_a1_057.mrc"
        > <i class="download icon"></i> Download </a></p>

        08aug31a_a1_057_1k.rec,  Reconstruction (Pixel Size 1.02 nm), 255.8 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/jsa2008-08-31-15/3dimage_5450/08aug31a_a1_057_1k.rec"
        > <i class="download icon"></i> Download </a></p>
DOI: 10.22002/y6bz9-e2d26
title: Spore10

description: Sporulating cell.Keywords: septum, sporulation, peptidoglycan inside the septum, spoIVA
description: <strong>Tilt Series Date:</strong> 2008-06-13</p>
<strong>Data Taken By:</strong> Elitza Tocheva</p>
<strong>Species / Specimen:</strong> Acetonema longum</p>
<strong>Strain:</strong> None</p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-60.0°, 60.0°), step: 1°, constant angular increment, dosage: 200.0 eV/Å²,  defocus: -10.0 μm,  magnification: 0x. </p>
<strong>Acquisition Software:</strong> Leginon</p>
<strong>Upload Method:</strong> webload</p>
<strong>Processing Software Used:</strong> IMOD</p>
<strong>Collaborators and Roles:</strong> Eric Matson grew the cells</p>
<strong>Purification / Growth Conditions / Treatment:</strong> Cells were grown anaerobically on CO2+glu</p>
<strong>Sample Preparation:</strong> quantifoil grid, 10nm gold, 100% humidity</p>
description: Files available via S3 at https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2008-06-13-5/rawdata/</p>spore10.mrc,  Tilt Series (Pixel Size 0.961 nm), 1.0 GB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2008-06-13-5/rawdata/spore10.mrc"
        > <i class="download icon"></i> Download </a></p>

        spore10_full.rec,  Reconstruction (Pixel Size 1.922 nm), 906.0 MB  
        
<a role="button" class="ui compact mini button" href="https://sdsc.osn.xsede.org/ini210004tommorrell/tomography_archive/etl2008-06-13-5/3dimage_2421/spore10_full.rec"
        > <i class="download icon"></i> Download </a></p>