cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/a9b5cbcfbbc59a7640725e977b9d47a1
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-05-03
DOI: 10.6073/pasta/b509b4fc81394660164c9904ea54e799
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1986-12-12
DOI: 10.6073/pasta/f34f46943e85969ef088d32c349d2949
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1986-10-09
DOI: 10.6073/pasta/f41cf413f341c6e597452b80ef282960
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-05-13
DOI: 10.4225/08/557fb4e47254e
title: Projected vegetation redistribution (MaxClass): Australia - 9sec gridded projection to 2050, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups using kernel regression with GDM (VAS_v5_r11) (CMIP5: CanESM2 RCP 8.5)

description: This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_MXC_85Can50 - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_MXP_85Can50 - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_NMC_85Can50 - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: CanESM2 RCP 8.5)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the CanESM2 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. 

The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models.  

The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94.  This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org
DOI: 10.4225/08/557fb4e49f67b
title: Potential vegetation distribution: Australia - 9second gridded 1990-centred baseline predictions of the pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11)

description: This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the baseline (1990-centred) predicted potential distribution of 77 Major Vegetation Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression predicted the potential distribution of the 77 Major Vegetation Sub-groups using 1990-centred (30 year average) baseline climates derived from ANUCLIM v6.1 (Xu and Hutchinson 2011). The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes. 

The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information). 

There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. 

This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org
DOI: 10.4225/08/557fb55763825
title: Projected vegetation redistribution (MaxClass): Australia - 9sec gridded projection to 2050, maximum probability class generalised pre-clearing patterns of Major Vegetation Sub-groups using kernel regression with GDM (VAS_v5_r11) (CMIP5: MIROC5 RCP 8.5)

description: This collection contains a 9-second gridded dataset (ESRI binary float format in GDA94) showing the generalised projected future (2050-centred) potential pre-clearing vegetation patterns of 77 Major Vegetation Sub-groups (MVS classes) derived from the maximum of their respective predicted probabilities for each grid cell (V_85MIR50_MXC - MaxClass). Two additional datasets show the maximum probability in each gird cell that was used to assign that class (V_85MIR50_MXP - MaxProb), and the number of classes with non-zero probabilities with potential to represent their type in each grid cell (V_85MIR50_NMC - NumClasses). The predicted probabilities for each class were derived based on their distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. These details are provided with the data package “Potential vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: MIROC5 RCP 8.5)”. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. This dataset projects the generalised potential pre-clearing vegetation patterns based on 2050-centred (30 year average) future climates derived from the MIROC5 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. 

The accuracy of projections is limited by the quality of the vegetation mapping used to train the models and the accuracy of environmental variables delimiting substrate boundaries and disturbance regimes. Uncertainty or errors in the underlying vegetation map and environmental data will be reproduced by the models. Furthermore, variables describing the relationship between extreme climatic events and ecological disturbance regimes, that have significant structural influences on vegetation, are not directly included in these models.  

The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94.  This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org
DOI: 10.4225/08/557fb6281353d
title: Lake Eyre Basin Invasive Plants Priority Threat Management

description: A summary of the native threatened fauna and flora in the Lake Eyre Basin, the invasive plants that threaten their persistence and an appraisal of the management strategies for mitigating the impacts of the invasive plants
DOI: 10.6073/pasta/04837be72f4b656162658c09c2fc3a34
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-06-14
DOI: 10.6073/pasta/126bb63597047a95c48753b7a59d4552
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-08-01
DOI: 10.6073/pasta/677b00882646a3f976faf81faeef91c4
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-05-13
DOI: 10.6073/pasta/8efa450d69ce5ad90d99a3b4d0aa3164
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-07-16
DOI: 10.6073/pasta/984371284c76d7e8eb200f1a86e3b2bf
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1987-01-13
DOI: 10.6073/pasta/9c1c1ea6d9b14315ec4f5871b84c7ade
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-04-11
DOI: 10.6073/pasta/a1a57f4f024c878540de7d55ea5f904a
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1987-03-02
DOI: 10.6073/pasta/a2f3a6f88620a45e276c01cdc9625400
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-06-20
DOI: 10.6073/pasta/d39aac2c01db8a48819fbcd55ed687f5
title: Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2001-10-08
DOI: 10.6073/pasta/d802f7713b12dc066f3db4810d1c0272
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-09-02
DOI: 10.6073/pasta/e26ae22f3d36509daaccd137a401dc86
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1987-01-29
DOI: 10.6073/pasta/e603761fa26e58a90efd1add5db85dac
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-09-18
DOI: 10.6073/pasta/fe77d13edb5a0eaa551d1695bcaed7af
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-04-27
DOI: 10.14457/cmu.the.2002.70
title: การพัฒนาฐานข้อมูลบรรณานุกรมระบบเกษตรของมหาวิทยาลัยเชียงใหม่ /
DOI: 10.14457/cmu.the.2004.119
title: การพัฒนาฐานข้อมูลการผลิตโคเนื้อระดับชุมชน ในตำบลป่าสัก อำเภอเมือง จังหวัดลำพูน /
DOI: 10.14457/cmu.the.2004.120
title: การพัฒนาฐานข้อมูลภูมิสารสนเทศ เพื่อการจัดลำดับความสำคัญของลุ่มน้ำ /
DOI: 10.14457/cmu.the.2006.87
title: การพัฒนาฐานข้อมูลภูมิสารสนเทศดิน เพื่อการใช้งานบนเว็บ /
DOI: 10.14457/cmu.the.2008.113
title: การพัฒนาฐานข้อมูลการเปลี่ยนแปลงคุณสมบัติทางฟิสิกส์ ของดินภายใต้การใช้ประโยชน์ที่ดินแบบต่างๆ ที่บ้านใหม่หนองบัว ตำบลหนองบัว อำเภอไชยปราการ จังหวัดเชียงใหม่ /
DOI: 10.14457/cmu.the.2009.127
title: การพัฒนาฐานข้อมูลการผลิตสุกร ในชุมชนตำบลป่าสัก อำเภอเมือง จังหวัดลำพูน /
DOI: 10.14457/cmu.the.2009.128
title: การพัฒนาฐานข้อมูลวัสดุดนตรีตะวันตก สำหรับห้องสมุดดิวอลด์ มหาวิทยาลัยพายัพ /
DOI: 10.14457/cmu.the.2010.212
title: การพัฒนาฐานข้อมูลวิเคราะห์คุณภาพงานวิจัยเพื่อส่งเสริมคุณภาพงานวิจัย ของคณาจารย์คณะแพทยศาสตร์ มหาวิทยาลัยเชียงใหม่ /
DOI: 10.4225/08/557fb7c0b1b29
title: Projected vegetation redistribution: Australia - 9second gridded projection to 2050, pre-clearing extents of 77 Major Vegetation Sub-groups using kernel regression with GDM-scaled environments for Vascular Plants (GDM: VAS_v5_r11; CMIP5: CanESM2 RCP 8.5)

description: This collection contains 9-second gridded datasets (ESRI binary float format in GDA94) showing the projected future (2050-centred) potential vegetation redistribution of 77 Major Vegetation Sub-groups (MVS classes) for continental Australia based on their pre-clearing distribution patterns and correlation with baseline ecological environments (c.1990 climates, substrate and landform). The pre-clearing vegetation patterns and classification derive from version 4.1 of “Australia - Estimated Pre1750 Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product)” developed by the Australian Government Department of the Environment and collaborating State agencies. A kernel regression was used with c.155,000 locations of training classes for the 77 MVS classes attributed with 17 GDM-scaled environmental predictors for Vascular Plants representing baseline ecological environments. The training class data input to the kernel regression is provided with this package. The GDM-scaled environmental predictors are available with the “VAS_v5_r11” data package. Using the 1990 baseline training MVS class data, and without constraining the prediction to pre-existing map boundaries, the kernel regression projected to 2050 the distribution of the 77 Major Vegetation Sub-groups using 2050-centred (30 year average) future climates derived from the CanESM2 global climate model for the emission scenario defined by a representative concentration pathway of 8.5. The kernel regression generates unconstrained probabilities varying in the range from 0 and up to 1 for each of the 77 MVS classes. 

The data are provided as 9-second (approximately 250m), ESRI binary float grid format in GDA94. Each class is denoted “UNCON###”, where the number refers to the code originally assigned to that MVS class by the supplier. A lookup table linking the MVS classes to the output codes and descriptive title is provided. Generalised representations of the vegetation classes derived from the individual class probabilities as the maximum probability in any grid cell are provided separately (see related information). 

There are three dataset packages in this series: 1) 1990 predictions of MVS classes; 2) 2050 CanESM2 RCP 8.5 predictions of MVS classes; 3) 2050 MIROC5 RCP 8.5 predictions of MVS classes. 
This dataset series and its use is described in the AdaptNRM Guide “Helping biodiversity adapt to climate change: a community-level modelling approach”, available online at: www.adaptnrm.org
DOI: 10.6073/pasta/05083b68435928437c5a4381202d7bc1
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1992-06-25
DOI: 10.6073/pasta/0f98954fc841f8665de753f18713c6f2
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-07-16
DOI: 10.6073/pasta/2040b0a88b22bbf59ddd3ec1c8f9e728
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1989-10-23
DOI: 10.6073/pasta/20cbbc06da62442471d6ecd478aa55b0
title: Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2001-11-09
DOI: 10.6073/pasta/4fb5e9b4aa0dfa66325e2a29c9894b69
title: Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2001-10-24
DOI: 10.6073/pasta/58789da46b8a7b6427cba7907b419a70
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1989-11-08
DOI: 10.6073/pasta/7556123af87f387de0cd556b83ca496f
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1988-08-29
DOI: 10.6073/pasta/9a45cb9315b954d8659b691954dbb1f1
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-11-05
DOI: 10.6073/pasta/9ab6ecd10d8e521e4fdaa9b1e9d3d941
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1988-10-04
DOI: 10.6073/pasta/a5b4b29edd49dcf18bb093936a8dcbd6
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1982-12-26
DOI: 10.6073/pasta/b0115d5288f67833d2e4cb33211e9170
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1982-11-17
DOI: 10.6073/pasta/fe0156271e1f31f43bdd9800a69a8e7d
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1989-10-07
DOI: 10.14457/cmu.res.2003.205
title: การพัฒนาฐานข้อมูลและโปรแกรมประยุกต์สำหรับระบบการบริหารวัตถุดิบและสินค้าคงคลังของร้านถ่ายเอกสาร ห้างหุ้นส่วนจำกัด ยูเนี่ยนก็อปปี้เซนเตอร์ /
DOI: 10.14457/cmu.the.2003.125
title: การพัฒนาตัวชี้วัดคุณภาพการปฏิบัติเพื่อป้องกันการแพร่กระจายเชื้อแบบมาตรฐาน /
DOI: 10.14457/cmu.the.2004.121
title: การพัฒนาตัวชี้วัดคุณภาพการปฏิบัติการทำให้ปราศจากเชื้อในหน่วยจ่ายกลาง /
DOI: 10.14457/cmu.the.2007.141
title: การพัฒนาดัชนีการจัดการมูลฝอยอันตรายจากครัวเรือน ในเขตเทศบาลนครเชียงใหม่ /
DOI: 10.14457/cmu.the.2008.114
title: การพัฒนาฐานข้อมูลสภาพชั้นดินเชียงใหม่ โดยระบบสารสนเทศทางภูมิศาสตร์ /
DOI: 10.14457/cmu.the.2008.115
title: การพัฒนาฐานข้อมูลออนไลน์ สำหรับวารสารอิเล็กทรอนิกส์ : กรณีศึกษาวารสารมนุษยศาสตร์สาร /
DOI: 10.14457/cmu.the.2010.213
title: การพัฒนาฐานข้อมูลเพื่อจัดทำสารสนเทศสนับสนุนการตัดสินใจของผู้บริหารด้านเทคนิค พื้นที่ภาคเหนือตอนบน บริษัท ทีทีแอนด์ที จำกัด [มหาชน] /
DOI: 10.5061/dryad.60p5d/10
title: FranceCG - code

description: Github repository storing R scripts used in this paper.
DOI: 10.6073/pasta/2bc656c820b752d12123ffb97187d3b5
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1989-09-28
DOI: 10.6073/pasta/5b681a7d47f18d9542aec245225dfdbd
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1992-08-12
DOI: 10.6073/pasta/6a87bdee18757971db153a914e4f24bf
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-07-06
DOI: 10.6073/pasta/6f6133b68c197b99ee6c4a96bf7d5fcd
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1984-05-06
DOI: 10.6073/pasta/709acd842934836f81d0fab727ef852e
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-09-02
DOI: 10.6073/pasta/7978ce0f99b52681f1b57bfcccb73e03
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1984-04-20
DOI: 10.6073/pasta/85c6c7be30230e82ca0099ceb78c21d8
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-07-22
DOI: 10.6073/pasta/95ad48bfde75fb4fe17c8959c02db557
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-08-23
DOI: 10.6073/pasta/a91488def12c2a929c6065ea443802ed
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1987-04-03
DOI: 10.6073/pasta/acdfc09c1be79e43ce70788af34c28e8
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-10-20
DOI: 10.6073/pasta/b662fbd8fcf1881253254f7893977ac7
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-09-18
DOI: 10.6073/pasta/cbc136140364932ccabb2264cc2b9b0e
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-08-01
DOI: 10.6073/pasta/e3513513431dea56b6cd4e628026a721
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1984-03-23
DOI: 10.6073/pasta/02e80a3f31ec60fbd1051bd6cdb082b1
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1984-12-16
DOI: 10.6073/pasta/22d8c2e9b111e22ec52b745b344e4459
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-12-07
DOI: 10.6073/pasta/27a106a230c0987d855ccb05075eb530
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1985-01-17
DOI: 10.6073/pasta/2b6b051f1a0a90b92ab28ba55b3f82fa
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1984-08-26
DOI: 10.6073/pasta/2f358be9324103e41a7f1f6222807f76
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-09-08
DOI: 10.6073/pasta/3ecb98e63f74366c1a3895032cfb5324
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-09-24
DOI: 10.6073/pasta/50b7cc5411dcec97521741d4c63459ef
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1984-09-11
DOI: 10.6073/pasta/602199a964e3a569d534a7907ca40843
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1986-10-10
DOI: 10.6073/pasta/8288d526f0480742902dccdb0941882c
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-12-23
DOI: 10.6073/pasta/9c7725a6de6fd9b9afd7fb644de7350e
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1992-05-31
DOI: 10.6073/pasta/a00d47be15b45c62e0dfe72b59be9bd0
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1987-06-06
DOI: 10.6073/pasta/a28edbb90b078ea6fd27400120449b47
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1989-12-01
DOI: 10.6073/pasta/c39af3cfea5733b3141da81c376bf8aa
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1985-01-01
DOI: 10.6073/pasta/ea56dcba1fdf5ff34a0db4e07d36625a
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1998-11-21
DOI: 10.6073/pasta/f5c9b9d34500d8b1700056ad6aeb5fac
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1989-11-15
DOI: 10.6073/pasta/f8e671c0a272d0479427cdeae1be7244
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1989-10-14
DOI: 10.6073/pasta/fcad225deaaa81404f08a7c141f2144e
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1984-10-29
DOI: 10.14457/cmu.the.2003.126
title: การพัฒนาตัวชี้วัดคุณภาพการป้องกันและควบคุม การติดเชื้อที่ตำแหน่งผ่าตัด /
DOI: 10.14457/cmu.the.2003.127
title: การพัฒนาตัวชี้วัดคุณภาพการป้องกันและควบคุม ปอดอักเสบจากการใช้เครื่องช่วยหายใจ /
DOI: 10.14457/cmu.the.2003.128
title: การพัฒนาตัวชี้วัดคุณภาพการป้องกันและควบคุมการติดเชื้อ จากการใส่คาสายสวนหลอดเลือดดำส่วนกลาง /
DOI: 10.14457/cmu.the.2003.129
title: การพัฒนาตัวชี้วัดคุณภาพการป้องกันและควบคุมการติดเชื้อจากการฟอกเลือดด้วยเครื่องไตเทียม /
DOI: 10.14457/cmu.the.2003.130
title: การพัฒนาตัวชี้วัดคุณภาพการป้องกันและควบคุมการติดเชื้อในบุคลากรสุขภาพ /
DOI: 10.6073/pasta/05353d0e4747a8da65afe58d7e312a8c
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1987-07-08
DOI: 10.6073/pasta/0a2ddd55bc3961c690b4d649d64711b2
title: Landsat Enhanced Thematic Mapper image data for Luquillo LTER collected on 1984-04-08
DOI: 10.6073/pasta/0b4de17ffc7da7f1e32295d5a33a3ff8
title: Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1985-10-16
DOI: 10.6073/pasta/0f1f32b5d11d69def506f01a024d0c7a
title: Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1987-06-22